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It is easy. On the TrendStats homepage, click the Create Chart tab and choose your analysis type.
Newly released data are now available in PowerStats and QuickStats. The 2011-12 Beginning Postsecondary Students Longitudinal Study (BPS:12/17) now includes data from the Postsecondary Education Transcript Studies (PETS). Learn more about the 2011-12 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).
Now available in PowerStats: the 1988 National Study of Postsecondary Faculty (NSOPF:88). Learn more about the National Study of Postsecondary Faculty (NSOPF).
Now available in PowerStats: the 1999 National Study of Postsecondary Faculty (NSOPF:99). Learn more about the National Study of Postsecondary Faculty (NSOPF).
Now available in PowerStats: the 1993 National Study of Postsecondary Faculty (NSOPF:93). Learn more about the National Study of Postsecondary Faculty (NSOPF).
Tables for One Year After a Bachelor’s Degree: A Profile of 2015-16 Graduates are now available in the DataLab Tables Library.
Tables for First-Time Subbaccalaureate Students: An Overview of Their Institutions, Programs, Completion, and Labor Market Outcomes After 3 Years are now available in the DataLab Tables Library.
Now available in PowerStats: the 1992-93 National Postsecondary Student Aid Study (NPSAS:93). Learn more about the Postsecondary Student Aid Study (NPSAS).
Now available in PowerStats: the 1986-87 National Postsecondary Student Aid Study (NPSAS:87). Learn more about the Postsecondary Student Aid Study (NPSAS).
Now available in PowerStats: the 1989-90 National Postsecondary Student Aid Study (NPSAS:90). Learn more about the Postsecondary Student Aid Study (NPSAS).
Tables for What Is the Price of College? Total, Net, and Out-of-Pocket Prices in 2015-16 are now available in the DataLab Tables Library.
Tables for High School Longitudinal Study of 2009 (HSLS:09): A First Look at the Postsecondary Transcripts and Student Financial Aid Records of Fall 2009 Ninth-Graders are now available in the DataLab Tables Library.
Now available in PowerStats and QuickStats: the 1999-2000 School Survey on Crime and Safety (SSOCS). Learn more about SSOCS.
Download the Creating a TrendStats Chart (PDF, 3.04 MB) tutorial to learn more about creating a custom chart in TrendStats.
Now available in PowerStats and QuickStats: 2016/2017 Baccalaureate and Beyond (B&B:16/17). Learn more about Baccalaureate and Beyond.
Now available in PowerStats and QuickStats: the 2003-04 School Survey on Crime and Safety (SSOCS). Learn more about SSOCS.
Tables for Trends in Graduate Student Financing: Selected Years, 2003–04 to 2015–16 are now available in the DataLab Tables Library.
Tables for Trends in Undergraduate Nonfederal Grant and Scholarship Aid by Demographic and Enrollment Characteristics: Selected Years, 2003–04 to 2015–16 are now available in the DataLab Tables Library.
Tables for Trends in Pell Grant Receipt and the Characteristics of Pell Grant Recipients: Selected Years, 2003–04 to 2015–16 are now available in the DataLab Tables Library.
Tables for Trends in Ratio of Pell Grant to Total Price of Attendance and Federal Loan Receipt are now available in the DataLab Tables Library.
Newly released data are now available in PowerStats and QuickStats. The 2011-12 Beginning Postsecondary Students Longitudinal Study (BPS:12/17) now includes data from the second follow-up in 2017. Learn more about the 2011-12 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).
Tables for Baccalaureate and Beyond (B&B:16/17): A First Look at the Employment and Educational Experiences of College Graduates, 1 Year Later are now available in the DataLab Tables Library.
Tables for Profile of Very Low- and Low-Income Undergraduates in 2015–16 are now available in the DataLab Tables Library.
Tables for Considerations for Using the School Courses for the Exchange of Data (SCED) Classification System in High School Transcript Studies: Applications for Converting Course Codes from the Classification of Secondary School Courses (CSSC) are now available in the DataLab Tables Library.
Tables for Changes in Undergraduate Program Completers’ Borrowing Rates and Loan Amounts by Age: 1995–96 Through 2015–16 are now available in the DataLab Tables Library.
Tables for Advanced Placement, International Baccalaureate, and Dual-Enrollment Courses: Availability, Participation, and Related Outcomes for 2009 Ninth-Graders: 2013 are now available in the DataLab Tables Library.
TrendStas ChartsVisit TrendStats to create custom bar charts, stacked bar charts, pie charts, and line graphs from your TrendStats analysis.
Tables for Going Back to College: Undergraduates Who Already Held a Postsecondary Credential are now available in the DataLab Tables Library.
Now available in PowerStats and QuickStats: the High School and Beyond Longitudinal Study and the National Education Longitudinal Study of 1988. Learn more about HS&B and NELS.
Tables for Student Financing of Undergraduate Education in 2015–16: Students’ Net Price, Expected Family Contribution, and Financial Need are now available in the DataLab Tables Library
Tables for Student Financing of Undergraduate Education in 2015–16: Income, Tuition, and Total Price are now available in the DataLab Tables Library
Tables for Student Financing of Undergraduate Education in 2015–16: Financial Aid by Type and Source are now available in the DataLab Tables Library.
Tables for Profile and Financial Aid Estimates of Graduate Students: 2015–16 are now available in the DataLab Tables Library.
Tables for Profile of Undergraduate Students: Attendance, Distance and Remedial Education, Degree Program and Field of Study, Demographics, Financial Aid, Financial Literacy, Employment, and Military Status: 2015–16 are now available in the DataLab Tables Library.
Tables for Persistence, Retention, and Attainment of 2011–12 First-Time Beginning Postsecondary Students as of Spring 2017 are now available in the DataLab Tables Library.
Tables for Military Service and Educational Attainment of High School Sophomores After 9/11: Experiences of 2002 High School Sophomores as of 2012 are now available in the DataLab Tables Library.
New variables released in Baccalaureate and Beyond: 2008/2012 PowerStats. See a list of variables or learn more.
B&B 2012 Students New variables now available in B&B 2012 PowerStats. The following variables have been added: Variable Name Variable Label B2DISTINSTE Distance between primary job in 2012 and bachelor's degree institution B2DISTINSTR Distance between residence in 2012 and bachelor's degree institution B2SMSTE Primary job in 2012 is in same state as bachelor's degree institution state B2SMSTER Primary job and residence in 2012 are in same state as bachelor's degree institution state B2SMSTR Residence in 2012 is in same state as bachelor's degree institution state B2STCDE State of primary job: 2012 B2STCDR State of residence: 2012
New variables now available in B&B 2012 PowerStats. The following variables have been added:
Now available in PowerStats, QuickStats, and TrendStats: the National Household Education Survey (NHES) for 2012 and 2016 Parent and Family Involvement in Education (PFI). Learn more about PFI.
Tables for What High Schoolers and Their Parents Know About Public 4-Year Tuition and Fees in Their State are now available in the DataLab Tables Library.
New IPEDS Tables are now available in the DataLab Tables Library.
Now available in PowerStats and QuickStats: the National Household Education Survey (NHES) for 2016 Adult Training and Education (ATES). Learn more about ATES.
Now available in PowerStats, QuickStats, and TrendStats: the National Household Education Survey (NHES) for 2012 and 2016 Early Childhood Program Participation (ECPP). Learn more about ECPP.
QuickStats ChartsVisit QuickStats to view the latest enhancements to charting options, including new chart types, color switching, and more.
Introducing the DataLab Tables LibraryThe College & Career Tables Library is now the DataLab Tables Library. Visit the DataLab Tables Library.
Tables for Trends in Free Application for Federal Student Aid (FAFSA) Submissions are now available in the DataLab Tables Library.
Tables for Four Years Later: 2007–08 College Graduates' Employment, Debt, and Enrollment in 2012 (2018435) are now available in the DataLab Tables Library.
Tables for Working Before, During, and After Beginning at a Public 2-Year Institution: Labor Market Experiences of Community College Students (2018428) are now available in the DataLab Tables Library.
Tables for First-Generation Students: College Access, Persistence, and Postbachelor’s Outcomes (2018401) are now available in the DataLab Tables Library.
Now available in PowerStats and QuickStats: the 2015-16 School Survey on Crime and Safety (SSOCS). Learn more about SSOCS.
Now available in PowerStats and QuickStats: the High School Longitudinal Study of 2009 (HSLS:09) Second Follow-Up. Learn more about HSLS.
Tables for High School Longitudinal Study of 2009 (HSLS:09) Second Follow Up: A First Look at Fall 2009 Ninth–Graders in 2016 (2018139) are now available in the DataLab Tables Library.
Now available in PowerStats and QuickStats: the 2015-16 National Teacher and Principal Survey (NTPS). Learn more about NTPS.
Now available in PowerStats and QuickStats: the 2015-16 National Postsecondary Student Aid Study, Undergraduate Students (NPSAS:2016 UG). Learn more about NPSAS.
Now available in PowerStats and QuickStats: the 2015-16 National Postsecondary Student Aid Study, Graduate Students (NPSAS:2016 GR). Learn more about NPSAS.
New variables released in NPSAS 1995-96, 1999-2000, 2003-04, 2007-08, and 2011-12 Undergraduate PowerStats. See a list of variables or learn more.
NPSAS Undergraduate Students New variables now available in NPSAS Undergraduates PowerStats. The following variables have been added: Variable Name Variable Label INGRTAMT2Institutional grants total (updated) INSTAMT2Institutional aid total (updated) INSTNEED2Institutional need-based grants (updated) STATNEED2State need-based grants (updated) STGTAMT2State grants total (updated) STATEAMT2State aid total (updated)
New variables now available in NPSAS Undergraduates PowerStats. The following variables have been added:
Tables for National Postsecondary Student Aid Study (NPSAS:16): Student Financial Aid Estimates for 2015–16 First Look (2018466) are now available in the DataLab Tables Library.
Tables for First-Generation Students: College Access, Persistence, and Postbachelor's Outcomes (2018421) are now available in the DataLab Tables Library.
Now available in PowerStats and QuickStats: the 1999-00, 2003-04, and 2007-08 Schools and Staffing District Survey (SASS). Learn more about SASS.
Now available in PowerStats and QuickStats: the 1999-00, 2003-04, and 2007-08 Schools and Staffing Library Media Center Survey (SASS). Learn more about SASS.
Tables for Characteristics and Outcomes of Undergraduates with Disabilities (2018432) are now available in the DataLab Tables Library.
Tables for Science, Technology, Engineering, and Mathematics (STEM) Majors: Where Are They 4 Years After Receiving a Bachelor's Degree? (2018423) are now available in the DataLab Tables Library.
Now available in PowerStats and QuickStats: the 1999-00, 2003-04, and 2007-08 Schools and Staffing School Survey (SASS). Analyze these datasets by public, private, or combined public and private schools. Learn more about SASS.
Tables for Beginning College Students Who Change Their Majors Within 3 Years of Enrollment (2018434) are now available in the DataLab Tables Library.
2015 Federal Student Aid Supplements added to BPS:1996/2001 and BPS:2004/2009 PowerStats. Additional variables related to borrowing, repayment, and student characteristics are now available.
Tables for Repayment of Student Loans as of 2015 Among 1995-96 and 2003-04 First-Time Beginning Students (2018410) are now available in the DataLab Tables Library.
Now available in PowerStats and QuickStats: the 1999-00, 2003-04, and 2007-08 Schools and Staffing Principal Survey (SASS). Analyze these datasets by public, private, or combined public and private school principals. Learn more about SASS.
Now available in PowerStats and QuickStats: the 1999-00, 2003-04, and 2007-08 Schools and Staffing Teacher Survey (SASS). Analyze these datasets by public, private, or combined public and private school teachers. Learn more about SASS.
Tables for Four Years After a Bachelor’s Degree: Employment, Enrollment, and Debt Among College Graduates (2017438) are now available in the DataLab Tables Library.
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Tables for The Debt Burden of Bachelor’s Degree Recipients (2017436) are now available in the DataLab Tables Library.
Tables for A Profile of the Enrollment Patterns and Demographic Characteristics of Undergraduates at For-Profit Institutions (2017416) are now available in the DataLab Tables Library.
Interested in trends related to crime and safety in US public schools? Identify trends over time in school crime with the newest addition to TrendStats, the School Survey on Crime and Safety.
Now available in PowerStats and QuickStats: the 2011-12 Private School Universe Survey (PSS). Learn more about PSS.
IPEDS Fall 2013 State/Compendium Tables are now available in the DataLab Tables Library.
IPEDS Spring 2013 State/Compendium Tables are now available in the DataLab Tables Library.
Tables for New American Undergraduates Enrollment Trends and Age at Arrival of Immigrant and Second-Generation Students (NCES 2017414) are now available in the DataLab Tables Library.
Tables for Use of Private Loans by Postsecondary Students: Selected Years 2003-04 Through 2011-12 (2017420) tables are now available in the DataLab Tables Library.
Tables for Employment and Enrollment Status of Baccalaureate Degree Recipients 1 Year After Graduation: 1994, 2001, and 2009 (2017407) are now available in the DataLab Tables Library.
Tables for First-time Postsecondary Students in 2011-12: Three-year Withdrawal, Stopout, and Transfer Rates (2016139) are now available in the DataLab Tables Library.
Tables for First-time Postsecondary Students in 2011-12: Three-year Persistence and Attainment at Any Institution (2016138) are now available in the DataLab Tables Library.
Tables for First-time Postsecondary Students in 2011-12: Three-year Retention and Attainment at First Institution (2016137) are now available in the DataLab Tables Library.
Tables for First-time Postsecondary Students in 2011-12: A Profile (2016136) are now available in the DataLab Tables Library.
Tables for Reaching the Limit: Undergraduates Who Borrow the Maximum Amount in Federal Direct Loans: 2011-12 (2016408) are now available in the DataLab Tables Library.
Tables for Bachelor's degree recipients 1 year after graduation: employment and enrollment in 1994, 2001, and 2009 (2016435) are now available in the DataLab Tables Library.
Tables for Changes in Pell Grant Participation and Median Income of Recipients (2016407) are now available in the DataLab Tables Library.
Tables for A Profile of Military Undergraduates: 2011-12 (2016415) are now available in the DataLab Tables Library.
Tables for Undergraduates Who Do Not Apply for Financial Aid (2016406) are now available in the DataLab Tables Library.
Additional 2011-12 National Postsecondary Student Aid Study (NPSAS:12) variables released. See list.
New Variables in NPSAS:2011-12 PowerStats The following NPSAS Undergraduate variables are now available in PowerStats. Name Label AIDAPP2Applied for any aid (including nonfederal only) AIDCST3Ratio of aid (excluding private loans and Direct PLUS loans to parents) to student budget
The following NPSAS Undergraduate variables are now available in PowerStats.
New variables added to High School Longitudinal Study of 2009 PowerStats. See list.
New Variables added to High School Longitudinal Study of 2009 PowerStats The following HSLS:09 variables are now available in PowerStats. Name Label P1DISABP1 Doctor/school has told parent 9th grader has a disability P1EAREYEP1 D03D Doctor/school has told parent 9th grader has hearing/vision problem P1JOINTP1 D03E Doctor/school has told parent 9th grader has bone/joint/muscle problem S3CLGSECTORU13 Postsecondary institutional sector
The following HSLS:09 variables are now available in PowerStats.
Newly released data are now available in PowerStats and QuickStats. The 2011-12 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:12/14) now includes over 250 new variables focusing on students’ borrowing while enrolled and their labor market outcomes 3 years after beginning postsecondary education. Learn more about the 2011-12 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:12/14).
Additional 2008-12 Baccalaureate and Beyond (B&B:08/12) variables released. See list.
New Variables in 2008-12 Baccalaureate and Beyond (B&B:08/12) PowerStats The following B&B:08/12 variables are now available in PowerStats. Name Label B2CMRJSTPrimary job: Employed in primary job in 2012 B2EMPTYPPrimary job: Employer type, 2012 BA_ENRMonths between bachelor's degree award date and first post-bachelor's enrollment BA_JOB1Months between bachelor's degree award date and first job JOB1GT3Held first job longer than 3 months
The following B&B:08/12 variables are now available in PowerStats.
Tables from K-12 Teaching Experience Among 2007-08 College Graduates: 2012 (2016641) are now available in the DataLab Tables Library.
Now available in PowerStats: the School Survey on Crime and Safety 2007-08 (SSOCS:2008) and School Survey on Crime and Safety 2005-06 (SSOCS:2006).
Now available in PowerStats and QuickStats: High School Longitudinal Study of 2009 (HSLS:2009).
Additional NPSAS Undergraduate variables added to TrendStats. See list.
NPSAS Undergraduate Variables Added to TrendStats The following NPSAS Undergraduate variables are now available in TrendStats. Name Description AIDCST3Total aid received excluding private loans and Direct PLUS loans to parents as a percentage of the total student budget. AIDSNEEDThe amount of financial aid exceeding federal need. AIDSRCAid package by source of aid received. ATTEND2Student's attendance status during the fall term. ATTENDMRThe student's main reason for enrolling at NPSAS. ATTNPTStudent's attendance intensity at all institutions in the given survey year. CALSYSThe NPSAS institution's academic calendar system. CLOCKDenotes if the NPSAS institution is on a clock or credit hour system. CNTLAFFIThe NPSAS institution's control or affiliation. CRNUMCRDThe number of credit cards a student has in his or her own name. CUMLNTP1Indicates the source of the student's loans during undergraduate education. DEGPRIndicates whether the student has earned degrees or certificates since high school. DEGPRAAIndicates whether the student has already earned an associate's degree since high school. DEGPRCRTIndicates whether the student has already earned an undergraduate certificate/diploma since high school. DEGPRMSIndicates whether the student has already earned a master's degree since high school. DEGPRPTBIndicates whether the student has already earned a post-baccalaureate certificate since high school. DEGPRPTMIndicates whether the student has already earned a post-master's certificate since high school. DEPEND4Student's dependency status including dependents and marital status. DEPNUMNumber of student's dependents (children and others). DEPOTHERIndicates whether the student had dependents other than children. DEPTYPEType of student's dependents. DEPYNGThe age of the student's youngest child. DSTUINCDependent student's own income in the year two years prior to the survey year, excluding the income of the parents. EFCAIDThe total amount of financial aid that is subject to federal EFC limitations if the student received need-based federal aid. EFFORT18Net price after all aid except work-study as a percentage of total income. EFFORT20Net price after grants and loans as a percentage of total income. EMPLWAIVTuition waivers for staff and families of staff at the institution. EMPLYAM3Indicates the amount of tuition aid received from the student's or the parents' employers. EMPLYAMTTotal amount of aid received from employers. ENLENNumber of months enrolled. ENR01Monthly enrollment status for July of the academic year. ENR02Monthly enrollment status for August of the academic year. ENR03Monthly enrollment status for September of the academic year. ENR04Monthly enrollment status for October of the academic year. ENR05Monthly enrollment status for November of the academic year. ENR06Monthly enrollment status for December of the academic year. ENR07Monthly enrollment status for January of the academic year. ENR08Monthly enrollment status for February of the academic year. ENR09Monthly enrollment status for March of the academic year. ENR10Monthly enrollment status for April of the academic year. ENR11Monthly enrollment status for May of the academic year. ENR12Monthly enrollment status for June of the academic year. ENRFALLStudent was enrolled during the July through December term. ENRSPRStudent was enrolled during the January through June term. ENRSTATStudent's enrollment pattern during the academic year. ESUBMX2Indicates whether undergraduates who took out subsidized Stafford loans took out their individual maximum subsidized amount, that is, the maximum they could borrow that was allowed for the program (based on their class level) and also taking into account their financial need after all aid except for any subsidized Stafford loans. ETOTMX2Indicates whether undergraduates who took out Stafford loans in took out their individual maximum total amount (sum of subsidized and unsubsidized), that is, the maximum they could borrow that was allowed for the program (based on their class level and dependency) and also taking into account their total price of attendance reduced by all financial aid except for any loans. EVER2PUBThe student has ever taken classes for credit offered through a community college. EVER4YRThe student has ever attended a 4-year college. FEDBENThis variable indicates whether any member of the student's household received any of the following federal benefits during the academic year: - Food Stamps Benefits - Free/Reduced Price School Lunch Benefits - Supplemental Security Income Benefits - TANF Benefits - WIC Benefits. FEDBENAThis variable indicates whether any member of the student's household received the following federal benefit during the academic year: Food Stamp Benefit. FEDBENBThis variable indicates whether any member of the student's household received the following federal benefit during the academic year: Free or Reduced Price School Lunch Benefits. FEDBENCThis variable indicates whether any member of the student's household received the following federal benefit during the academic year: Supplemental Security Income Benefits. FEDBENDThis variable indicates whether any member of the student's household received the following federal benefit during the academic year: TANF Benefits. FEDBENEThis variable indicates whether any member of the student's household received the following federal benefit during the academic year: WIC Benefits. FEDGRPCTRatio of total federal grants to total aid received during the academic year. FEDLNPAKFederal loan package by type of loan received during the academic year. FEDNEEDTotal amount of federal need-based aid received during the academic year. FEDPCTRatio of total federal aid to total aid received during the academic year. FLNPCT6Ratio of federal loans to federal aid (excluding PLUS loans and Veterans' benefit) received during the academic year. GRNTSRCGrant package by source of grant received during the academic year. GRTCSTTotal grants received as a percentage of the total student budget. GRTLOANRatio of total grants to total loans received during the academic year. GRTPCTTNTotal grants received as a percentage of tuition and fees at NPSAS. GRTRATIORatio of total grants to total grants and loans received during the academic year. GRTSNEEDGrant amount exceeding federal need. HCHONORSCount of high school subject areas (English, math, foreign languages, science, and social studies) in which the student has taken an advanced placement, accelerated or honors course, according to self-report on standardized test questionnaire. HCMATHHIHighest level of math completed or planned to take, according to self-report on standardized test questionnaire and the student interview. HCSCINUMNumber of science courses the student took or planned to take, according to self-report on standardized test questionnaire. HCTKBIOLStudent took or planned to take to take Biology, according to self-report on standardized test questionnaire. HCTKCHEMStudent took or planned to take to take Chemistry, according to self-report on standardized test questionnaire. HCTKPHYSStudent took or planned to take to take Physics, according to self-report on standardized test questionnaire. HCYSENGLYears of high school coursework completed or planned in English, according to self-report on standardized test questionnaire. HCYSLANGYears of high school coursework completed or planned in foreign languages, according to self-report on standardized test questionnaire. HCYSMATHYears of high school coursework completed or planned in math, according to self-report on standardized test questionnaire and the student interview. HCYSSCIEYears of high school coursework completed or planned in science, according to self-report on standardized test questionnaire. HCYSSOCIYears of high school coursework completed or planned in social studies, according to self-report on standardized test questionnaire. HIGHLVEXThe highest level of education that the student ever expects to complete. HLOFFERHighest level of postsecondary degree or award offered at the NPSAS sample institution attended during the academic year. HOMESTUDThe student owns a home or pays a mortgage on a home. HSCRDANYIndicates whether or not student earned any college credits in high school. College credits can be college credits earned at a college or Advanced Placement credits earned in high school. HSCRDAPIndicates whether or not student earned Advanced Placement credits in high school. HSCRDCOLIndicates whether or not student earned college credits at a college during high school. HSGPAHigh school grade point average on the standardized test date, according to self-report on test questionnaire. HSGRADYYThe calendar year the student graduated from high school. HSIZEStudent's family size during the academic year. INCPCT2The tuition charged at the NPSAS institution as a percentage of total income. INJURISIndicates whether the tuition charged at public NPSAS institutions was in or out of jurisdiction. INSTCATInstitutional category was derived using the level of offerings reported on the Institutional Characteristics (IC) component and the number and level of awards that were reported on the Completions (C) component. INSTGPCTRatio of total institutional grants to total aid received during the academic year. INSTPACKAid package with institutional aid received during the academic year. For years 1996 and 2000 the categories for "No institutional" and "No aid received" are combined to be comparable to "No Institutional Aid" in subsequent years. INSTPCTRatio of total institutional aid to total aid received during the academic year. INSWAIVTotal amount of institutional tuition waivers received during the academic year. JOBEARNThe student's total amount earned from work (excluding work-study, assistantship, and traineeship) during the academic year. JOBONOFFThe location of the student's job. JOBROLEThe student's primary role while enrolled at NPSAS and also working. JOBTYPE2Indicates the student has a work-study/assistantship job, a regular job, or both. LNREPAYThe student expects help with repaying their student loans, from an individual other than their spouse. LOANCSTTotal student loans (excluding Parent PLUS) received as a percentage of the total student budget. LOANCST2Total student loans (including Parent PLUS loans) received as a percentage of the total student budget. LOANPCTRatio of total loans (excluding Parent PLUS loans) to total aid received during the academic year. LOANPCT2Ratio of total loans (including Parent PLUS loans) to total aid received during the academic year. LOCALEThe degree of urbanization in which the NPSAS institution is located. LOCALESTThe degree of urbanization in which the students home is located. MAJCHGFQThe frequency with which the student formally changed his or her major. MAJORS23The student's undergraduate major or field of study during the academic year. MAJORS2YThe student's undergraduate major or field of study during the academic year. MAJORS4YThe student's undergraduate major or field of study during the academic year. MFTNumber of months enrolled full-time between July and June of the academic year. MHTNumber of months enrolled half-time between July and June of the academic year. MLTNumber of months enrolled less than half-time between July and June of the academic year. MNTRENTThe average monthly rent or mortgage the student paid during the academic year. MPTNumber of months enrolled part time between July and June of the academic year. NEEDAID1Indicates the amount of need-based aid received. NETCST10Tuition and fees minus federal grants for the academic year. NETCST12Tuition and fees minus state grants for the academic year. NETCST13Tuition and fees minus institutional grants from the NPSAS institution for the academic year. NETCST14Tuition and fees minus non-federal grants for the academic year. NETCST15Tuition and fees minus all state and institutional grants for the academic year. NETCST16Total net price after all federal and state grants for the academic year. NETCST17Total net price after all grants and loans for the academic year. NETCST18Total net price after all financial aid except work-study for the academic year. NETCST4Total net price after all grants and one-half of all loans for the academic year. NETCST41Net total price after all financial aid except private loans for the academic year. OTHFDGRTTotal amount of grants from various small federal programs. OTHRSCRTotal aid from outside sources received. OTHTYPE2Total amount of aid received during the academic year that was not classified by type as grants and loans to students, but included work-study. PARBORNStudent's parent(s) were born in the United States. PDADEDFather's highest level of education. PELLRAT1Ratio of Pell grants to total aid received during the academic year. PELLRAT2Ratio of Pell grants to total grants received during the academic year. PELLYRSNumber of years that a Pell grant was received. PERKCUM1Indicates the cumulative amount of Perkins loans ever borrowed for undergraduate education through July 1 of the survey year. PFAMNUMFamily size of dependent student during the academic year. PFEDTAXThe amount of federal income tax paid by the parents of dependent student in the year two years prior to the survey year. PINCOLNumber of parent's dependent children in college during the academic year. PLUSPCTRatio of Parent PLUS loans to total aid received during the academic year. PMARITALParent's marital status during the academic year. PMOMEDMother's highest level of education. PRIVAMTTotal private source grants and loans for the academic year. PRIVCSTPrivate loans received as a percentage of the total student budget. PRIVLRATRatio of private loans to total loans received during the academic year. PRIVPCTRatio of private loans to total aid received during the academic year. PTAXFILEWhether or not parents of dependent student filed federal income tax in the year two years prior to the survey year. REANOAPAThe student did not apply for financial aid because he/she did not want to take on debt. REANOAPBThe student did not apply for financial aid because the application forms were too much work or too time consuming. REANOAPCThe student did not apply for financial aid he/she did not have enough information about how to apply for financial aid. REANOAPDThe student did not apply for financial aid because he/she did not need financial aid. REANOAPEThe student did not apply for financial aid because he/she thought he/she would be ineligible. SECTOR1Sector of the NPSAS sample institution attended during the academic year. SFAMNUMIndicates the number of persons in the independent student's own family (including the student). SFEDTAXThe amount of federal income tax the independent student paid for the year two years prior to the survey. SIBINCOLIndicates whether the student has siblings who attended college or graduate school during the academic year. SINCOLNumber of persons in the independent student's household who attended college during the academic year. SJHOURSThe average number of hours the student worked per week during the academic year. SJMAJORThe student's work-study job was related to his/her major or field of study. SJONOFFThe location of the student's work-study job. SJSCHOOLThe student's work study job was for NPSAS or for another institution or organization. SNEED3The remaining need after all federal grants, calculated as the total student budget minus the Expected Family Contribution and minus federal grants. SNEED4The remaining need after all grants and federal need-based aid, calculated as the total student budget minus the Expected Family Contribution and minus aid subject to the federal EFC limitation. SNEED7The remaining need after federal and state grants, calculated as the total student budget minus the Expected Family Contribution and minus federal and state grants. SNEED8The remaining need after all federal, state, and other grants, calculated as the total student budget minus the Expected Family Contribution and minus federal state, and outside grants. SPINCOLThe student's spouse attended college or graduate school during the academic year. SPSINCSpouse's earned income in the year two years prior to the survey year, if married. STAFFRATRatio of Stafford loans to total loans received during the academic year. STAFFSTThis variable indicates the first year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received. STAFLSTThis variable indicates the last year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received. STAFYRSThe number of years that the student received Direct Subsidized Loans or Direct Unsubsidized Loans (also known as subsidized and unsubsidized Stafford Loans). STAPCTRatio of total state aid to total aid received during the academic year. STAXFILEWhether or not independent student filed federal income tax for the year two years prior to the survey year. STGRPCTRatio of total state grants to total aid received during the academic year. STNOND1Indicates the amount of state grants received in the academic year that were based neither on need nor merit. STSBCUM1Cumulative subsidized Stafford loan amounts borrowed for undergraduate education through July 1 of the survey year. STSUBMXIndicates whether undergraduates who took out subsidized Stafford loans took out the maximum subsidized amount that was allowed for the program, based on their class level. STTOTMXIndicates whether undergraduates who took out Stafford loans in took out the maximum total amount (sum of subsidized and unsubsidized) that was allowed for the program, based on their class level and dependency status. SUBCUM1Cumulative federal subsidized loan amounts borrowed for undergraduate education through July 1 of the survey year. T4LNAMT2Total amount of federal Title IV loans (including Parent PLUS loans) received during the academic year. TEACTDERACT composite score, derived from either a reported ACT score or the SAT I combined score converted to an estimated ACT composite score. TESATCP1The average percentile rank of reported SAT verbal and math scores, among all test takers. TESATCREThe sum of reported SAT verbal and math scores. TESATMDESAT I math score, derived as either the actual SAT I math score or the ACT math score converted to an estimated SAT I math score. TESATMP1Percentile rank of reported SAT math score (recentered), among all test takers. TESATMREThe reported SAT math score (recentered). TESATNP1Percentile rank of derived SAT math score, among all test takers (nationwide). TESATVDESAT I verbal score derived as either the actual SAT I verbal score or the ACT English and reading score converted to an estimated SAT I verbal score. TESATVP1Percentile rank of reported SAT verbal score (recentered), among all test takers. TESATVREThe reported SAT verbal score (recentered). TETOOKIndicates whether the student took the SAT I or ACT college entrance exam. TFEDAID2Total amount of federal aid (including Veterans' benefit & DOD) received during the academic year. TFEDAID6Total amount of federal aid excluding Parent PLUS loans and Veterans' benefit received during the academic year. TNFEDAIDTotal amount of non-federal financial aid received during the academic year. TOTAID2Total amount of federal Title IV, state, and institutional aid received during the academic year. TOTAID4Total amount of all financial aid received except Parent PLUS loans during the academic year. TOTAID6Total amount of all financial aid received except Parent PLUS loans and federal Veterans' benefit during the academic year. TOTAID7Total amount of all financial aid received during the academic year except for federal Veterans' benefit. TOTAID8Total amount of all financial aid received during the academic year except for private loans. TOTGRT4Total amount of state and institutional grants received in the academic year. TOTNOND3Indicates non-need based aid received from both institutional non-need aid and state non-need aid programs during the academic year. UNSBLOANTotal amount of all unsubsidized loans from any source received during the academic year. UNTAXBFCIndicates if the student received worker's compensation during the academic year. USBORNThe student was born in the United States. VETBENIndicates the total amount of federal veterans' benefit received in the academic year. VOCHELPTotal amount of vocational rehabilitation and job training grants received during the academic year. WORKPCTRatio of total work-study to total aid received during the academic year.
The following NPSAS Undergraduate variables are now available in TrendStats.
Additional NPSAS Graduate variables added to TrendStats. See list.
NPSAS Graduate Variables Added to TrendStats The following NPSAS Graduate variables are now available in TrendStats. Name Description AIDCSTTotal aid received as a percentage of the total student budget. AIDSNEEDThe amount of financial aid exceeding federal need. ATTEND2Student's attendance status during the fall term. ATTNPTStudent's attendance intensity at all institutions attended in the academic year. CALSYSThe NPSAS institution's academic calendar system. CLOCKDenotes if the NPSAS institution is on a clock or credit hour system. CNTLAFFIThe NPSAS institution's control or affiliation. CUMLNTP1Indicates the source of the student's loans during undergraduate education. DEGPRThe student has earned degrees or certificates since high school. DEGPRAAThe student has already earned an associate's degree since high school. DEGPRCRTThe student has already earned an undergraduate certificate/diploma since high school. DEGPRMSThe student has already earned a master's degree since high school. DEGPRPTBThe student has already earned a post-baccalaureate certificate since high school. DEGPRPTMThe student has already earned a post-master's certificate since high school. DEPCAREThe student has dependent children in daycare during the academic year. DEPCOSTThe student's monthly daycare costs for dependent children during the academic year. DEPEND5AStudent's dependency status including dependents and marital status during the academic year. DEPNUMNumber of student's dependents (children and others) during the academic year. DEPOTHERIndicates whether the student had dependents other than children. DEPTYPEIndicates whether the student has dependents who are their children, not their children, or both. DEPYNGThe age of the student's youngest child during the academic year. EFCAIDThe total amount of financial aid that is subject to federal EFC limitations if the student received need-based federal aid. EFFORT18Net price after all aid except work-study as a percentage of total income. EFFORT3Net price after grants as a percentage of total income in the year two years prior to the survey year. EFFORT9Net tuition after all grants as a percentage of total income in the year two years prior to the survey year. EMPLWAIVTuition waivers for staff and families of staff at the institution attended during the academic year. EMPLYAM3Indicates the amount of tuition aid received from the student's or the parents' employers in the academic year. ENLENNumber of months enrolled between July and June of the academic year. ENR01Monthly enrollment status for July of the academic year. ENR02Monthly enrollment status for August of the academic year. ENR03Monthly enrollment status for September of the academic year. ENR04Monthly enrollment status for October of the academic year. ENR05Monthly enrollment status for November of the academic year. ENR06Monthly enrollment status for December of the academic year. ENR07Monthly enrollment status for January of the academic year. ENR08Monthly enrollment status for February of the academic year. ENR09Monthly enrollment status for March of the academic year. ENR10Monthly enrollment status for April of the academic year. ENR11Monthly enrollment status for May of the academic year. ENR12Monthly enrollment status for June of the academic year. ENRFALLStudent was enrolled during the July through December term. ENRLSIZEFall enrollment. ENRSPRStudent was enrolled during the January through June term. ENRSTATStudent's enrollment pattern during the academic year. FEDBENThis variable indicates whether any member of the student's household received any of the following federal benefits during the academic year: food Stamps Benefits, free/Reduced Price School Lunch Benefits, supplemental Security Income Benefits, TANF Benefits, or WIC Benefits FEDBENAThis variable indicates whether any member of the student's household received the following federal benefit during the academic year: Food Stamp Benefit. FEDBENBThis variable indicates whether any member of the student's household received the following federal benefit during the academic year: Free or Reduced Price School Lunch Benefits. FEDBENCThis variable indicates whether any member of the student's household received the following federal benefit during the academic year: Supplemental Security Income Benefits. FEDBENDThis variable indicates whether any member of the student's household received the following federal benefit during the academic year: TANF Benefits. FEDBENEThis variable indicates whether any member of the student's household received the following federal benefit during the academic year: WIC Benefits. FEDCUM1Cumulative federal loan amounts borrowed for undergraduate education through July 1 of the survey year. FEDOWE1Indicates total amount owed on all federal loans for undergraduate education as of late in the survey year. FGRTLNTotal amount of federal student loans and federal grants received during the academic year. FLNPCT6Ratio of federal loans to federal aid (excluding veterans benefits) received during the academic year. GRNTSRCGrant package by source of grant received during the academic year. GRTCSTTotal grants received as a percentage of the total student budget. GRTLOANRatio of total grants to total loans received during the academic year. GRTRATIORatio of total grants to total grants and loans received during the academic year. GRTSNEEDGrant amount exceeding federal need. HIGHLVEXThe highest level of education that the student ever expects to complete. HISPANICStudent is of Hispanic or Latino origin. HISPTYPEType of Hispanic or Latino origin. HLOFFERHighest level of postsecondary degree or award offered at the NPSAS sample institution attended during the academic year. HOMESTUDThe student owns a home or pays a mortgage on a home. IMMIGENNumber of generations the student's family has been in the U.S. IMMIGRAStudent's immigrant status. INCPCT1The adjusted cost of attendance at the NPSAS institution as a percentage of total income in the year two years prior to the survey year. INCPCT2The tuition charged at the NPSAS institution as a percentage of total income. INJURISIndicates whether the tuition charged at public NPSAS institutions was in or out of jurisdiction. JOBEARNThe student's total amount earned from work (excluding work-study, assistantship, and traineeship) during the academic year. JOBENRThe student's intensity of work (excluding work-study/assistantship/traineeship) while enrolled during the academic year. JOBHOURThe average number of hours worked per week during the academic year (excluding work-study, fellowships, assistantships, and traineeships). JOBONOFFThe location of the student's job. JOBROLEThe student's primary role while enrolled at NPSAS and also working. JOBTYPE2Indicates the student has a work-study/assistantship job, a regular job, or both. LNREPAYThe student expects help with repaying their student loans, from an individual other than their spouse. LOANCSTTotal student loans (excluding Parent PLUS) received as a percentage of the total student budget. LOCALEThe degree of Urbanization in which the NPSAS institution is located. LOCALESTInput to this derived variable was the best-known current address after data collection. MFTNumber of months enrolled full time between July and June of the academic year. MHTNumber of months enrolled half-time between July and June of the academic year. MLTNumber of months enrolled less than half-time between July and June of the academic year. MNTRENTThe average monthly rent or mortgage the student paid during the academic year. MPTNumber of months enrolled part-time between July and June of the academic year. NETCST10Tuition and fees minus federal grants for the academic year. NETCST12Tuition and fees minus state grants for the academic year. NETCST14Tuition and fees minus non-federal grants for the academic year. NETCST15Tuition and fees minus all state and institutional grants for the academic year. NETCST16Net total price after all federal and state grants for the academic year. NETCST17Net total price after all grants and loans for the academic year. NETCST18Net total price after all financial aid except work-study for the academic year. NETCST2Net total price after all federal grants for the academic year. NETCST4Net total price after all grants and one-half of all loans for the academic year. NETCST41Net total price after all financial aid except private loans for the academic year four years prior to the survey year. NETCST9Tuition and fees minus all grants for the academic year. NFEDCUM1Cumulative non-federal loan amounts borrowed for undergraduate education through July 1 of the survey year. OBEREGRegion where NPSAS sample institution is located. OTHTYPE2Total amount of aid received during the academic year that was not classified by type as grants and loans to students, but included work-study. PCTPOVIndicates total income as a percentage of the federal poverty level thresholds for the year two years prior to the survey year. PDADEDFather's highest level of education. PELLFSTThe first year that a Pell grant was received during the years covered by the survey. PELLLSTThe last year that a Pell grant was received during the years covered by the survey. PELLYRSNumber of years that a Pell grant was received. PERKCUM1Indicates the cumulative amount of Perkins loans ever borrowed for undergraduate education through July 1 of the survey year. PMOMEDMother's highest level of education. PRIVCSTPrivate loans received as a percentage of the total student budget. PRIVLRATRatio of private loans to total loans received during the academic year. PRIVPACKLoan package by whether the loan received was private (alternative) or not during the academic year. PRIVPCTRatio of private loans to total aid received during the academic year. REANOAPAThe student did not apply for financial aid because he/she did not want to take on debt. REANOAPBThe student did not apply for financial aid because the application forms were too much work or too time consuming. REANOAPCThe student did not apply for financial aid he/she did not have enough information about how to apply for financial aid. REANOAPDThe student did not apply for financial aid because he/she did not need financial aid. REANOAPEThe student did not apply for financial aid because he/she thought he/she would be ineligible. SECTOR1Sector of the NPSAS sample institution attended during the academic year. SECTOR4Sector of the NPSAS sample institution attended during the academic year, for students who attended only one institution. SFEDTAXThe amount of federal income tax paid by the independent student in the year two years prior to the survey year. SINCOLNumber of persons in the independent student's household who attended college during the academic year. SJEARNThe total amount the student earned from his or her assistantship/fellowship/traineeship/work-study job during the academic year. SJHOURSThe average number of hours the student worked per week during the academic year. SNEED1The student's total need for need-based financial aid. SNEED2The remaining need after all financial aid (need-based and non-need-based) received. SNEED3The remaining need after all federal grants. Equal to the total student budget minus Expected Family Contribution, and minus federal grants. SNEED4The remaining need after all grants and federal need-based aid. Equal to the total student budget minus Expected Family Contribution, and minus aid subject to the federal EFC limitation. SNEED7The remaining need after federal and state grant aid. Equal to the total student budget minus expected family contribution, and minus federal and state grants. SNEED8The remaining need after all federal, state, and other grants. Equal to the total student budget minus the Expected Family Contribution, minus federal, state, and outside grants. SNEED9The remaining need after all financial aid received except private loans. SPINCOLThe student's spouse attended college or graduate school during the academic year. STAFFRATRatio of Stafford loans to total loans received during the academic year. STAFFSTThis variable indicates the first year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received. STAFLSTThis variable indicates the last year that a Direct Subsidized or Unsubsidized Loan (also known as subsidized and unsubsidized Stafford Loan) was received. STAFTYPEThis variable indicates the combination of subsidized and unsubsidized Stafford loans received at all institutions attended during the academic year. STAFYRSThe number of years that the student received Direct Subsidized Loans or Direct Unsubsidized Loans (also known as subsidized and unsubsidized Stafford Loans). STAPCTRatio of total state aid to total aid received during the academic year. STAXFILEWhether or not independent student filed federal income tax for the year two years prior to the survey year. STEMMAJThis variable indicates the student's major field of study during the academic year with a focus on science, technology, engineering, and mathematics (STEM) fields. STGRPCTRatio of total state grants to total aid received during the academic year. STSBCUM1Cumulative subsidized Stafford loan amounts borrowed for undergraduate education through July 1 of the survey year. STUDMULTNumber of institutions attended during the academic year. STYPELSTStudent type at the NPSAS sample institution during the academic year. SUBCUM1Cumulative federal subsidized loan amounts borrowed for undergraduate education through July 1 of the survey year. SUBLOANTotal amount of federal Title IV subsidized loans received during the academic year. TFEDGRT2Total amount of all federal grants, veterans benefits and Department of Defense aid received during the academic year. TGRTLNTotal amount of loans and grants received during the academic year. TITIVAMTTotal amount of federal Title IV financial aid received during the academic year. TNFEDAIDTotal amount of non-federal financial aid received during the academic year. TNFEDGRTTotal amount of non-federal grants received during the academic year. TNFEDLNTotal amount of non-federal loans received during the academic year. TOTAID5Total amount of all financial aid received except for work-study during the academic year. TOTAID7Total amount of all financial aid received during the academic year except for federal veterans benefits. TOTGRT2Total amount of all grants, veteran's benefits and Department of Defense aid received during the academic year. TOTGRT4Total amount of state and institutional grants received in the survey year. TOTLOAN3Total amount of all loans excluding private loans received during the academic year. UNSBLOANTotal amount of all unsubsidized loans from any source received during the academic year. USBORNThe student was born in the United States. VETBENIndicates the total amount of federal veterans' benefit received in the academic year. WORKPCTRatio of total work-study to total aid received during the academic year.
The following NPSAS Graduate variables are now available in TrendStats.
Tables from Persistence and Attainment of 2011–12 First-Time Postsecondary Students After 3 Years (BPS:12/14 First Look) are now available in the DataLab Tables Library.
Newly released study is now available in PowerStats and QuickStats. Learn more about the 2011-12 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:12/14).
Important update to financial aid variable documentation. Learn More.
Financial Aid Variables Unless otherwise specified, variables based on NSLDS data were derived with a filter that removed loans borrowed prior to July 1995. This loan-level filter was applied to balance the goal of including as many loans as possible while minimizing the use of partial loan information. However, since consolidated loans contain multiple loans, each with potentially different dates of origination, pre-1995 loans may be included in the total consolidated amount. See codebook entries for NSLDS derived variables for more information. Respondents who had taken out a loan before July 1995 can be identified with variable LOANBF071995.
Unless otherwise specified, variables based on NSLDS data were derived with a filter that removed loans borrowed prior to July 1995. This loan-level filter was applied to balance the goal of including as many loans as possible while minimizing the use of partial loan information. However, since consolidated loans contain multiple loans, each with potentially different dates of origination, pre-1995 loans may be included in the total consolidated amount. See codebook entries for NSLDS derived variables for more information. Respondents who had taken out a loan before July 1995 can be identified with variable LOANBF071995.
2008-12 Baccalaureate and Beyond (B&B:08/12) New variables now available in the 2008-12 Baccalaureate and Beyond PowerStats. The following variables have been added: Variable Name Variable Label B2DFROCRECONNumber of separate deferments granted for economic difficulty as of 2012 B2DFROCRENRNumber of separate deferments granted for student enrollment as of 2012 B2DFROCRFAMNumber of separate deferments granted for family or disability as of 2012 B2DFROCRGOVNumber of separate deferments granted for government program (Action, Peace Corps, Head Start, NOAA) as of 2012 B2DFROCRMILNumber of separate deferments granted for military or law enforcement as of 2012 B2DFROCRNUMTotal number of separate deferment incidents as of 2012 B2DFROCRREASReason for most frequently-granted deferment as of 2012 B2DFROCRTEANumber of separate deferments granted for teacher, medical, or non-profit as of 2012 LOANBF071995Borrowed federal loans before July 1995
New variables now available in the 2008-12 Baccalaureate and Beyond PowerStats. The following variables have been added:
2008-12 Baccalaureate and Beyond (B&B:08/12) variables revised. Learn More.
Variables With Revised Data in B&B:2012 Newly revised variables now available in the 2008-12 Baccalaureate and Beyond PowerStats and QuickStats. The following variables have been revised: Name Label How variable was revised # of cases changed B2FDDUE1Cumulative federal amount owed (principal and interest) for undergraduate as of 2012Cumulative amount was revised due to re-allocation of consolidated amounts to graduate level (B2FDCUM2)1,320 B2FDDUE2Cumulative federal amount owed (principal and interest) for graduate as of 2012Cumulative amount was revised due to re-allocation of consolidated amounts to graduate level (B2FDCUM2)1,310 B2DFR_AVGAverage number of deferments per loan as of 2012Consolidated and cancelled loan amounts were removed from the denominator50 B2DLQ_AVGAverage number of delinquencies per federal loan as of 2012Consolidated and cancelled loan amounts were removed from the denominator30 B2FBPERLNAverage number of forbearances per loan as of 2012Consolidated and cancelled loan amounts were removed from the denominator40 B2CNSCUMAmount of federal loans consolidated as of 2012Parent Plus loans and duplicate loans were removed from the total consolidated amount600 The values for the following variables have changed as a result of the changes to the variables above: Name Label # of cases changed B2BORATCumulative amount borrowed for education as of 201270 B2DATFBLatest forbearance date for borrower as of 201210 B2DEBTRTRatio of federal loans to annualized salary as of 201220 B2DEFEREver had a deferment on a loan as of 201210 B2DFR_ECONNumber of deferments for economic difficulty for all loans as of 201210 B2DFR_ENRNumber of deferments for student enrollment for all loans as of 201220 B2DFR_FAMNumber of deferments for family or disability for all loans as of 2012<10 B2DFR_GOVNumber of deferments for government program (Action, Peace Corps, Head Start, NOAA) for all loans as of 2012<10 B2DFR_MILNumber of deferments for military or law enforcement for all loans as of 2012<10 B2DFR_NUMTotal number of deferments for all loans as of 201220 B2DFR_REASMost common deferment reason for borrower for all loans as of 201210 B2DFR_TEANumber of deferments for teacher, medical, or non-profit for all loans as of 2012<10 B2DLQ_NOWCurrently in delinquent status - has a federal loan in delinquency in the 2011-12 academic year<10 B2EVERDAFBEver had loans in deferment or forbearance as of 201210 B2EVERPIFEver had a loan paid in full as of 2012<10 B2FDDUE3Cumulative federal amount owed (principal and interest) for all education as of 201210 B2FDOWE1Cumulative federal amount owed (principal) for undergraduate as of 2012 1,320 B2FEDCUM1Cumulative amount borrowed in federal loans as of 2012 - undergraduate level70 B2FEDCUM3Cumulative total amount borrowed in federal loans as of 201270 B2FEDFYEARFirst year borrowed federal loans as of 2012<10 B2FEDLYEARLast year borrowed federal loans as of 2012<10 B2FORBAREver had any loans in forbearance as of 201210 B2GP_USEBorrower consolidated loans as of 2012<10 B2LASTLEVGrade level when last federal loan was received as of 2012<10 B2LASTSTDTDate of status of latest federal loan as of 201220 B2LNSTATStatus of latest federal loan as of 201210 B2LOANPAIDAll federal loans were paid in full as of 2012<10 B2OWELRPTotal amount owed at time last entered repayment40 B2OWEPNLRPOutstanding principle amount at date last entered repayment40 B2OWEPRINLatest federal amount owed - principal as of 201250 B2PAYSTATRepayment status for any loans in 2012 (federal and private)<10 B2REPLNRepayment plan of latest federal loan in 201210 B2T4XDUECumulative Stafford and Perkins loan amount owed (principal and interest) as of 2012<10 B2TOTDUE3Cumulative amount owed for education loans as of 2012 (federal and private, principal and interest)10
Newly revised variables now available in the 2008-12 Baccalaureate and Beyond PowerStats and QuickStats. The following variables have been revised:
The values for the following variables have changed as a result of the changes to the variables above:
Now available in the 2008-12 Baccalaureate and Beyond PowerStats New variables now available in the 2008-12 Baccalaureate and Beyond PowerStats. The following variables have been added: Name Label B2DFROCRECONNumber of separate deferments granted for economic difficulty as of 2012 B2DFROCRENRNumber of separate deferments granted for student enrollment as of 2012 B2DFROCRFAMNumber of separate deferments granted for family or disability as of 2012 B2DFROCRGOVNumber of separate deferments granted for government program (Action, Peace Corps, Head Start, NOAA) as of 2012 B2DFROCRMILNumber of separate deferments granted for military or law enforcement as of 2012 B2DFROCRNUMTotal number of separate deferment incidents as of 2012 B2DFROCRREASReason for most frequently-granted deferment as of 2012 B2DFROCRTEANumber of separate deferments granted for teacher, medical, or non-profit as of 2012 LOANBF071995Borrowed federal loans before July 1995
Important update to financial aid variable documentation Unless otherwise specified, variables based on NSLDS data were derived with a filter that removed loans borrowed prior to July 1995. This loan-level filter was applied to balance the goal of including as many loans as possible while minimizing the use of partial loan information. However, since consolidated loans contain multiple loans, each with potentially different dates of origination, pre-1995 loans may be included in the total consolidated amount. See codebook entries for NSLDS derived variables for more information. Respondents who had taken out a loan before July 1995 can be identified with variable LOANBF071995.
Tables from Trends in Undergraduate Nonfederal Grant and Scholarship Aid by Demographic and Enrollment Characteristics, Selected Years: 1999-2000 to 2011-12 (NPSAS:96, NPSAS:2000, NPSAS:04, NPSAS:08 and NPSAS:12) are now available in the DataLab Tables Library.
Tables from Demographic and Enrollment Characteristics of Nontraditional Undergraduates: 2011-12 (NPSAS:12) are now available in the DataLab Tables Library.
Tables from Trends in Pell Grant Receipt and the Characteristics of Pell Grant Recipients: Selected Years, 1999-2000 to 2011-12 (NPSAS:2000, NPSAS:04, NPSAS:08 and NPSAS:12) are now available in the DataLab Tables Library.
Now available in PowerStats: the School Survey on Crime and Safety 2009-10 (SSOCS:2010).
Now available in PowerStats: Education Longitudinal Study of 2002 (ELS:2002).
Now available in DataLab: TrendStats.TrendStats allows users to compare estimates for like variables across multiple dataset years within a single table. Users can create percentage distribution tables or average, median, and percent greater than tables for both Undergraduate and Graduate respondents of the 1996, 2000, 2004, 2008, and 2012 National Postsecondary Student Aid Surveys (NPSAS).Visit the Learning Center to learn more.
2008-2012 Baccalaureate and Beyond (B&B:08/12) PowerStats updated. This update adds many new variables to the 2008-2012 Baccalaureate and Beyond PowerStats, including approximately 70 variables on teaching.
Tables from High School Dropouts and Stopouts: Demographic Backgrounds, Academic Experiences, Engagement, and School Characteristics (HSLS:09) are now available in the DataLab Tables Library.
Tables for Trends in Graduate Student Financing: Selected Years, 1995–96 to 2011–12 (NPSAS:96, NPSAS:2000, NPSAS:04, NPSAS:08 and NPSAS:12) are now available in the DataLab Tables Library.
Tables from What Is the Price of College? Total, Net, and Out-of-Pocket Prices by Type of Institution in 2011-12 (NPSAS:12) are now available in the DataLab Tables Library.
Tables from Baccalaureate Degree Recipients' Early Labor Market and Education Outcomes: 1994, 2001, and 2009 (B&B:93/94, B&B:2000/01, and B&B:08/09) are now available in the DataLab Tables Library.
Tables from Student Financing of Undergraduate Education Web Table: 2011-12 (NPSAS:12) are now available in the DataLab Tables Library.
New variables added to 2011-12 National Postsecondary Student Aid Study, Undergraduate Students and Graduate Students (NPSAS:12) PowerStats: DECMAJ, BUDGETBK, and MAJORCTE added to Undergraduate Students and BUDGETBK added to Graduate Students.
Tables for Profile and Financial Aid Estimates of Graduate Students: 2011-12 (NPSAS:12) are now available in the DataLab Tables Library.
Tables for Profile of Undergraduate Students: 2011-12 (Web Tables) (NPSAS:12) are now available in the DataLab Tables Library.
Tables for Transferability of Postsecondary Credit Following Student Transfer or Coenrollment (BPS:04/09) are now available in the DataLab Tables Library.
New variables added to 2007-08 National Postsecondary Student Aid Study, Undergraduate Students (NPSAS:2007-08). INSTCAT, DISTLOC2, TCHPLN, and TCHCRS are now available on PowerStats.
Now available in PowerStats: 2011-12 Schools and Staffing Survey (SASS). Data from the following questionnaires are included: Public and Private Schools; Public and Private School Teachers; Public and Private School Principals; Public School Districts; and Public School Library Media Centers.
Tables for Baccalaureate and Beyond: A First Look at the Employment Experiences and Lives of College Graduates, 4 Years On (B&B:08/12) are now available in the DataLab Tables Library.
Now available in PowerStats and QuickStats: 2008-2012 Baccalaureate and Beyond (B&B:08/12).
Among college graduates, 13% go into teaching within four years of graduation (Source: B&B:08/12)
Among first-generation college students, 40% were no longer enrolled 3 years after beginning their postsecondary education (Source: BPS:12/14)
As of 2012, the highest levels of education completed by 2002 High School Sophomores were: 33% bachelor’s degree or higher; 9% associate’s degree; 10% undergraduate certificate; 32% postsecondary attendance but no postsecondary credential; 13% high school diploma or equivalent; and 3% less than high school completion (Source: ELS:2002)
On average, fall 2009 ninth-graders had earned 3.6 credits in math and 3.3 credits in science by 2013. On average, students had earned 7.6 credits in STEM courses (Source: HSLS:09)
From 1996 to 2012, the percentage of undergraduates that applied for federal financial aid rose from 44.5% to 70.1% (Source: NPSAS:1995-96 & NPSAS:2011-12)
Part-time instructional faculty and staff averaged $44,800 in outside income other than consulting income in 2003 compared with $12,400 earned by full-time faculty (Source: NSOPF:2004)
Preschoolers with disabilities were disproportionately male, 70 percent versus 30 percent female (Source: PEELS)
The majority of private elementary schools in 2011-12 (13,459 of 30,861 total schools) enrolled less than 50 students (Source: PSS:2011-12)
More private school students in 2013–14 were enrolled in kindergarten (463,067) than in any other grade level (Source: PSS:2011-12)
Among Public School Teachers surveyed in 2011-12, 39.9% reported having a Bachelor’s Degree, 47.7% a Master’s Degree, and 8.7% reported having higher than a Master’s degree (Source: SASS:2011-12)
Early Childhood Program ParticipationNational Postsecondary Student Aid Study, UndergraduateNational Postsecondary Student Aid Study, GraduateParent and Family Involvement in EducationSchool Survey on Crime and Safety Pre-Elementary Education Longitudinal StudyPEELSPre-elementary students who received preschool special education services, as they progressed through the early elementary years Preschool special education, Programs and services received, Transitions between preschool and elementary school, Function and performance in preschool, kindergarten, and elementary schoolhttps://ies.ed.gov/ncser/projects/peels482003/2008qsOnpsOntsOff3,000ImputationImputation was conducted for selected items on the teacher questionnaire and parent interview data. In general, the item missing rate was low. The risk of imputation-related bias was judged to be minimal. The variance inflation due to imputation was also low due to the low imputation rate of 10 percent. Imputation for the supplemental sample increased the amount of data usable for analysis, offsetting the potential risk of bias.The methods of imputation included: hot-deck imputation, regression, external data source, and a derivation method, based on the internal consistency of inter-related variables.View methodology reportpeels_subject.pdf6.71 MBpeels_varname.pdf6.63 MB00148Private School Universe SurveyPSSPrivate schoolsSchool Affiliation/Associations, Enrollment, Grades Taught, Staffing, General Informationhttps://nces.ed.gov/surveys/pss/752011-2012qsOnpsOntsOff26,983WeightingThe final weights are needed to have the estimates reflect the population of private schools when analyzing the data. The data from the area frame component were weighted to reflect the sampling rates (probability of selection) of the PSUs. Survey data from both the list and area frame components were adjusted for school nonresponse. The final weight for PSS data items is the product of the Base Weight and the Nonresponse Adjustment Factor, where:Base Weight is the inverse of the probability of selection of the school. The base weight is equal to one for all list-frame schools. For area-frame schools, the base weight is equal to the inverse of the probability of selecting the PSU in which the school resides.Nonresponse Adjustment Factor is an adjustment that accounts for school nonresponse. It is the weighted (base weight) ratio of the total eligible in-scope schools (interviewed schools plus noninterviewed schools) to the total responding in-scope schools (interviewed schools) within cells. Noninterviewed and out-of-scope cases are assigned a nonresponse adjustment factor of zero.Because we have more information for list-frame schools, the cells used to compute the nonresponse adjustment were defined differently for list-frame and area-frame schools. For schools in the list frame, the cells were defined by affiliation (17 categories), locale type (4 categories), grade level (4 categories), Census region (4 categories), and enrollment (3 categories). The nonresponse adjustment cells for area frame schools were defined by three-level typology (3 categories) and grade level (4 categories). If the number of schools in a cell was fewer than 15 or the nonresponse adjustment factor was greater than 1.5, then that cell was collapsed into a similar cell. The variables used to collapse the cells and the collapse order varied according to whether the school was from the list or area frame and whether a school was a traditional or k-terminal school. The cells for traditional schools from the list frame were collapsed within enrollment category, locale type, grade level, and Census region. Cells for k-terminal schools from the list frame were collapsed within enrollment category, locale type, Census region, and affiliation. Cells for traditional schools from the area frame were collapsed within grade level and then within three-level typology. Cells for k-terminal schools from the area frame were collapsed within three level typology.ImputationAfter the data edit processing was complete, there were missing values within some records classified as interviews. These were cases where the respondent had not answered some applicable questionnaire items (and data for these items were not added in the pre-edit, consistency, or logic edit) or the response had been deleted during editing. Values were imputed to the missing data during imputation. Two types of imputation were employed: donor and analyst imputation.Donor ImputationIn donor imputation, values were created by extracting data from the record for a sample case (donor) with similar characteristics, using a procedure known as the “sequential nearest neighbor hot deck” (Kalton and Kasprzyk 1982, 1986; Kalton 1983; Little and Rubin 1987; Madow, Olkin, and Rubin 1983). In order to match incomplete records to those with complete data, “imputation” variables that identify certain characteristics of the school that were deemed to be important to the reporting of the data in each item (e.g., religious affiliation, enrollment, school level of instruction) were used. Items were grouped according to the perceived relevance of the imputation variables to the data collected by the item. For example, school level of instruction was used for matching incomplete records and donors to fill item 16 (length of school year) but was not used for item 7 (students by race).Analyst ImputationAfter the donor imputation was completed, there were records that still had missing values for 64 items. These were cases where the imputation failed to create a value because there was no suitable record to use as a donor, or the value imputed was deleted because it was outside the acceptable range for the item or was inconsistent with other data on the same record, or the religious orientation or purpose, or the religious orientation or affiliation, was not reported (items 14a and 14c) and no previous PSS information was available.For these cases, values were imputed by analysts to the items with missing data. That is, staff reviewed the data record, sample file record, and the questionnaire and identified a value consistent with the information from these sources for imputation.pss2012_subject.pdf377 KBpss2012_varname.pdf368 KB00260Schools and Staffing Survey, TeachersSASSPublic and private school teachersClass Organization, Education and Training, Certification, Professional Development, Working Conditions, School Climate and Teacher Attitudes, Employment and Background Informationhttps://nces.ed.gov/surveys/sass622011-2012qsOnpsOntsOff42,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass12teachpub_subject.pdf5.60 MBsass12teachpub_varname.pdf5.50 MB1332632011-2012qsOnpsOntsOff42,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass12teachpriv_subject.pdf4.90 MBsass12teachpriv_varname.pdf4.90 MB2332642011-2012qsOnpsOntsOff42,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass12teachcombined_subject.pdf5.60 MBsass12teachcombined_varname.pdf5.55 MB3332902007-2008qsOnpsOntsOff38,200PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass08teachpub_subject.pdf4.39 MBsass08teachpub_varname.pdf7.01 MB1337912007-2008qsOnpsOntsOff6,000PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass08teachpriv_subject.pdf3.27 MBsass08teachpriv_varname.pdf5.11 MB2337922007-2008qsOnpsOntsOff44,200PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass08teachcombined_subject.pdf3.25 MBsass08teachcombined_varname.pdf1.09 MB3337872003-2004qsOnpsOntsOff43,200PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass04teachpub_subject.pdf4.39 MBsass04teachpub_varname.pdf4.40 MB13312882003-2004qsOnpsOntsOff8,000PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass04teachpriv_subject.pdf6.73 MBsass04teachpriv_varname.pdf3.98 MB23312892003-2004qsOnpsOntsOff51,200PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass04teachcombined_subject.pdf1.15 MBsass04teachcombined_varname.pdf1.17 MB33312931999-2000qsOffpsOntsOff52,000PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass00teachpub_subject.pdf7.91 MBsass00teachpub_varname.pdf1.39 MB13316941999-2000qsOffpsOntsOff52,000PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass00teachpriv_subject.pdf6.34 MBsass00teachpriv_varname.pdf1.38 MB23316951999-2000qsOffpsOntsOff52,000PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass00teachcombined_subject.pdf6.60 MBsass00teachcombined_varname.pdf1.91 MB33316Schools and Staffing Survey, PrincipalsSASSPublic and private school principalsExperience, Training, Education, and Professional Development, Goals and Decision Making, Teacher and Aide Professional Development, School Climate and Safety, Instructional Time, Working Conditions and Principal Perceptions, Teacher and School Performancehttps://nces.ed.gov/surveys/sass652011-2012qsOnpsOntsOff9,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass12prinpub_subject.pdf1.99 MBsass12prinpub_varname.pdf1.97 MB1343662011-2012qsOnpsOntsOff9,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass12prinpriv_subject.pdf1.98 MBsass12prinpriv_varname.pdf1.90 MB2343672011-2012qsOnpsOntsOff9,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass12princombined_subject.pdf1.92 MBsass12princombined_varname.pdf2.05 MB33431022007-2008qsOnpsOntsOff7,500PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass08prinpub_subject.pdf2.00 MBsass08prinpub_varname.pdf1.83 MB13481032007-2008qsOnpsOntsOff1,900PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass08prinpriv_subject.pdf1.80 MBsass08prinpriv_varname.pdf1.61 MB23481042007-2008qsOnpsOntsOff9,400PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass08princombined_subject.pdf1.86 MBsass08princombined_varname.pdf1.61 MB3348992003-2004qsOnpsOntsOff8,100PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass04prinpub_subject.pdf553 KBsass04prinpub_varname.pdf2.20 MB134131002003-2004qsOnpsOntsOff2,400PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass04prinpriv_subject.pdf466 KBsass04prinpriv_varname.pdf445 KB234131012003-2004qsOnpsOntsOff10,500PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass04princombined_subject.pdf449 KBsass04princombined_varname.pdf433 KB33413961999-2000qsOffpsOntsOff12,000PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass00prinpub_subject.pdf1.90 MBsass00prinpub_varname.pdf1.61 MB13417971999-2000qsOffpsOntsOff12,000PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass00prinpriv_subject.pdf1.58 MBsass00prinpriv_varname.pdf1.25 MB23417981999-2000qsOffpsOntsOff12,000PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass00princombined_subject.pdf1.48 MBsass00princombined_varname.pdf1.26 MB33417Schools and Staffing Survey, SchoolsSASSPublic and private schoolsTeacher demand, teacher and principal characteristics, general conditions in schools, principals' and teachers' perceptions of school climate and problems in their schools, teacher compensation, district hiring and retention practices, basic characteristics of the student populationhttps://nces.ed.gov/surveys/sass592011-2012qsOnpsOntsOff9,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass12schoolpub_subject.pdf520 KBsass12schoolpub_varname.pdf530 KB1351602011-2012qsOnpsOntsOff9,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass12schoolpriv_subject.pdf720 KBsass12schoolpriv_varname.pdf675 KB2351612011-2012qsOnpsOntsOff9,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass12schoolcombined_subject.pdf1.60 MBsass12schoolcombined_varname.pdf1.55 MB33511172007-2008qsOnpsOntsOff7,600Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass08schoolspub_subject.pdf2.23 MBsass08schoolspub_varname.pdf2.52 MB135201182007-2008qsOnpsOntsOff2,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass08schoolspriv_subject.pdf2.51 MBsass08schoolspriv_varname.pdf3.07 MB235201192007-2008qsOnpsOntsOff9,500Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass08schoolscombined_subject.pdf1.92 MBsass08schoolscombined_varname.pdf2.27 MB335201142003-2004qsOnpsOntsOff8,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass04schoolspublic_subject.pdf2.27 MBsass04schoolspublic_varname.pdf2.30 MB135191152003-2004qsOnpsOntsOff2,500Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass04schoolsprivate_subject.pdf3.26 MBsass04schoolsprivate_varname.pdf1.55 MB235191162003-2004qsOnpsOntsOff10,400Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass04schoolscombined_subject.pdf1.90 MBsass04schoolscombined_varname.pdf2.00 MB335191111999-2000qsOffpsOntsOff9,300Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass00schoolspublic_subject.pdf2.00 MBsass00schoolspublic_varname.pdf2.07 MB135181121999-2000qsOffpsOntsOff2,600Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass00schoolsprivate_subject.pdf2.89 MBsass00schoolsprivate_varname.pdf3.18 MB235181131999-2000qsOffpsOntsOff11,900Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass00schoolscombined_subject.pdf2.20 MBsass00schoolscombined_varname.pdf2.50 MB33518Schools and Staffing Survey, DistrictsSASSPublic school districtsRecruitment and Hiring of Staff, Principal and Teacher Compensation, Student Assignment, Graduation Requirements, Migrant Education, District Performancehttps://nces.ed.gov/surveys/sass582011-2012qsOnpsOntsOff4,500Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass12district_subject.pdf1.15 MBsass12district_varname.pdf1.10 MB31641102007-2008qsOnpsOntsOff4,600PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass08district_subject.pdf0.51 MBsass08district_varname.pdf0.53 MB316261092003-2004qsOnpsOntsOff4,400PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass04district_subject.pdf0.88 MBsass04district_varname.pdf0.93 MB316251081999-2000qsOffpsOntsOff4,700PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass00district_subject.pdf1.10 MBsass00district_varname.pdf0.68 MB31624Schools and Staffing Survey, Library Media CentersSASSLibrary media centersSchool information, Facilities, services, and policies, Staffing information, Technology and information literacy, Collections and expenditureshttps://nces.ed.gov/surveys/sass572011-2012qsOnpsOntsOff7,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics. View methodology informationsass12LMC_subject.pdf675 KBsass12LMC_varname.pdf695 KB31751072007-2008qsOnpsOntsOff7,300PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass08LMC_subject.pdf0.59 MBsass08LMC_varname.pdf0.61 MB317231062003-2004qsOnpsOntsOff7,200PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass04LMC_subject.pdf0.80 MBsass04LMC_varname.pdf0.81 MB317221051999-2000qsOffpsOntsOff7,700PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.View methodology informationsass00LMC_subject.pdf1.16 MBsass00LMC_varname.pdf1.18 MB31721School Survey on Crime and SafetySSOCSElementary and secondary schoolsSchool Practices and Programs, Parent and Community Involvement at School, School Security, Staff Training, Limitations on Crime Prevention, Frequency of Crime and Violence, Frequency of hate and gang-related crimes, Disciplinary problems and actionshttps://nces.ed.gov/surveys/ssocs12832015-2016qsOnpsOntsOn3,500ImputationCompleted SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.SSOCS:2004 and Beyond: In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical. WeightingData are weighted to compensate for differential probabilities of selection and to adjust for the effects of nonresponse.Sample weights allow inferences to be made about the population from which the sample units are drawn. Because of the complex nature of the SSOCS sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error. An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Due to nonresponse, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias due to nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The same predictor variables from the SSOCS:2004 CHAID analysis were used for SSOCS:2006: instructional level, region, enrollment size, percent minority, student-to-FTE teaching staff ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time equivalent (FTE) teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted so that the weighted distribution of the responding schools resembled the initial distribution of the total sample. The nonresponse-adjusted weights were then poststratified to calibrate the sample to known population totals. Two dimension margins were set up for the poststratification—(1) instructional level and school enrollment size; and (2) instructional level and locale—and an iterative process known as the raking ratio adjustment brought the weights into agreement with known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. All three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2004). Miller, A.K. (2004). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.ssocs2016_subject.pdf375 KBssocs2016_varname.pdf375 KB008677032009-2010qsOnpsOntsOn2,600ImputationCompleted SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.SSOCS:2004 and Beyond: In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical. WeightingData are weighted to compensate for differential probabilities of selection and to adjust for the effects of nonresponse.Sample weights allow inferences to be made about the population from which the sample units are drawn. Because of the complex nature of the SSOCS sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error. An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Due to nonresponse, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias due to nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The same predictor variables from the SSOCS:2004 CHAID analysis were used for SSOCS:2006: instructional level, region, enrollment size, percent minority, student-to-FTE teaching staff ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time equivalent (FTE) teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted so that the weighted distribution of the responding schools resembled the initial distribution of the total sample. The nonresponse-adjusted weights were then poststratified to calibrate the sample to known population totals. Two dimension margins were set up for the poststratification—(1) instructional level and school enrollment size; and (2) instructional level and locale—and an iterative process known as the raking ratio adjustment brought the weights into agreement with known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. All three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2004). Miller, A.K. (2004). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.ssocs2010_subject.pdf565 KBssocs2010_varname.pdf365 KB008577432007-2008qsOnpsOntsOn2,560ImputationCompleted SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.SSOCS:2004 and Beyond: In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical. WeightingData are weighted to compensate for differential probabilities of selection and to adjust for the effects of nonresponse.Sample weights allow inferences to be made about the population from which the sample units are drawn. Because of the complex nature of the SSOCS sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error. An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Due to nonresponse, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias due to nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The same predictor variables from the SSOCS:2004 CHAID analysis were used for SSOCS:2006: instructional level, region, enrollment size, percent minority, student-to-FTE teaching staff ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time equivalent (FTE) teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted so that the weighted distribution of the responding schools resembled the initial distribution of the total sample. The nonresponse-adjusted weights were then poststratified to calibrate the sample to known population totals. Two dimension margins were set up for the poststratification—(1) instructional level and school enrollment size; and (2) instructional level and locale—and an iterative process known as the raking ratio adjustment brought the weights into agreement with known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. All three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2004). Miller, A.K. (2004). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.ssocs2008_subject.pdf1.96 MBssocs2008_varname.pdf912 KB008587332005-2006qsOnpsOntsOn2,720ImputationCompleted SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.SSOCS:2004 and Beyond: In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical. WeightingData are weighted to compensate for differential probabilities of selection and to adjust for the effects of nonresponse.Sample weights allow inferences to be made about the population from which the sample units are drawn. Because of the complex nature of the SSOCS sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error. An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Due to nonresponse, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias due to nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The same predictor variables from the SSOCS:2004 CHAID analysis were used for SSOCS:2006: instructional level, region, enrollment size, percent minority, student-to-FTE teaching staff ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time equivalent (FTE) teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted so that the weighted distribution of the responding schools resembled the initial distribution of the total sample. The nonresponse-adjusted weights were then poststratified to calibrate the sample to known population totals. Two dimension margins were set up for the poststratification—(1) instructional level and school enrollment size; and (2) instructional level and locale—and an iterative process known as the raking ratio adjustment brought the weights into agreement with known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. All three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2004). Miller, A.K. (2004). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.ssocs2006_subject.pdf8.82 MBssocs2006_varname.pdf3.58 MB008591382003-2004qsOnpsOntsOff2,800ImputationCompleted SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.SSOCS:2004 and Beyond. In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical.WeightingSample weights allow inferences to be made about the population from which the sample units were drawn. Because of the complex nature of the SSOCS:2004 sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error. The procedures used to create the SSOCS sampling weights are described below.An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Because some schools refused to participate, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias from nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (i.e., chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The predictor variables for the analysis were instructional level, region, enrollment size, percent minority, student-to-teacher ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time-equivalent teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted by dividing the base weight by the response rate in each class, so that the weighted distribution of the responding schools resembled the initial distribution of the total sample.The non-response-adjusted weights were then poststratified to calibrate the sample to known population totals. For SSOCS:2004, two dimension margins were set up for the poststratification: (1) instructional level and school enrollment size, and (2) instructional level and locale. An iterative process known as the raking ratio adjustment brought the weights into agreement with the known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. Similar to SSOCS:2000, all three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2003).Miller, A.K. (2003). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.View methodology reportssocs2004_subject.pdf1.2 MBssocs2004_varname.pdf2.1 MB008741391999-2000qsOnpsOntsOff2,300ImputationAll key data items with missing values were imputed using well-known procedures. Depending on the type of data to be imputed and the extent of missing values, logical imputation, poststratum means, or "hot-deck" imputation methods were employed. For three data items, imputation was done using information from the 1998-99 CCD file. Logical imputation is the assignment of data values based on other information in the data record. In the poststratum means method, a record with missing data was assigned the mean value of those cases in the same "poststratum" for which information on the item was available. The poststrata or "imputation classes" were defined on the basis of variables that were correlated with the item being imputed. Preliminary exploratory analyses (e.g., using chi-square tests of association, correlation analysis, and regression analysis) were carried out to identify the relevant classification variables. The strength of association of the variables in combination with subjective assessment was used to prioritize the importance of the variables in forming the imputation classes. In the "hot-deck" technique, cases with missing items were assigned the corresponding value of a "similar" respondent in the same "poststratum". Similar to the poststratum means approach, preliminary exploratory analyses were carried out to identify the relevant classification variables to be used to define the poststrata. The classification variables were separated into two groups -- "hard" and "soft" boundary variables. The hard boundary variables were considered to be so important that the imputation classes were always formed within those boundaries. The boundaries formed by the soft boundary variables were crossed, if necessary, to form the imputation class.WeightingA stratified random sample design was used to select schools for the SSOCS:2000. Over 3,000 schools were selected at rates that varied by sampling stratum; i.e., the classes formed by crossing instructional level (elementary, middle, secondary, combined), type of locale (city, urban fringe, town, rural), and enrollment size class (less than 300, 300-499, 500-999, 1,000+). Since the schools were selected with unequal probabilities, sampling weights are required for analysis to inflate the survey responses to population levels. Weighting is also used to reduce the potential bias resulting from nonresponse and possible undercoverage of the sampling frame.One method of computing sampling errors to reflect various aspects of the sample design and estimation procedures is the replication method. Under replication methods, a specified number of subsamples of the full sample (called "replicates") are created. The survey estimates can then be computed for each of the replicates by creating replicate weights that mimic the actual sample design and estimation procedures used in the full sample. The variability of the estimates computed from the replicate weights is then used to estimate the sampling errors of the estimates from the full sample. An important advantage of the replication methods is that they preclude the need to specify cumbersome variance formulas that are typically needed for complex sample designs (McCarthy, 1966).1 Another advantage is that they can readily be adapted to reflect the variance resulting from nonresponse (and other weight) adjustment procedures. The two most prevalent replication methods are balanced repeated replication (BRR) and jackknife replication. The two methods differ in the manner in which the replicates are constructed. For the SSOCS:2000, a variant of jackknife replication was used to develop replicate weights for variance estimation because the jackknife method is believed to perform somewhat better than BRR for estimates of moderately rare events (e.g., number of schools in which a serious crime was committed). Under the jackknife method, the replicates are formed by deleting specified subsets of units from the full sample. The jackknife method provides a relatively simple way of creating the replicates for variance estimation and has been used extensively in NCES surveys.1. McCarthy, P. (1966). Replication: An Approach to the Analysis of Data from Complex Surveys. Vital and Health Statistics, Series 2, No. 14. Washington, DC: U.S. Department of Health, Education and Welfare.View methodology reportssocs2000_subject.pdfX KBssocs2000_varname.pdfX KB00875Education Longitudinal StudyELSStudents who were high school sophomores in 2001-02 or high school seniors in 2003-04Student and Family Background, School and Classroom Characteristics, High School Completion and Dropout Status, Postsecondary Education Choice and Enrollment, Postsecondary Attainment, Employment, Transition to Adult Roleshttps://nces.ed.gov/surveys/els2002682002qsOnpsOntsOff14,000 to 16,000ImputationStochastic methods were used to impute the missing values for the ELS:2002 third follow-up data. Specifically, a weighted sequential hot-deck (WSHD) statistical imputation procedure (Cox 1980; Iannacchione 1982) using the final analysis weight (F3QWT) was applied to the missing values for the variables in table 12 in the order in which they are listed. The WSHD procedure replaces missing data with valid data from a donor record within an imputation class. In general, variables with lower item nonresponse rates were imputed earlier in the process.View methodology reportels2002sophomores_subject.pdf7.58 MBels2002sophomores_varname.pdf7.49 MB32954692002qsOffpsOntsOff14,000 to 16,000ImputationStochastic methods were used to impute the missing values for the ELS:2002 third follow-up data. Specifically, a weighted sequential hot-deck (WSHD) statistical imputation procedure (Cox 1980; Iannacchione 1982) using the final analysis weight (F3QWT) was applied to the missing values for the variables in table 12 in the order in which they are listed. The WSHD procedure replaces missing data with valid data from a donor record within an imputation class. In general, variables with lower item nonresponse rates were imputed earlier in the process.View methodology reportels2002seniors_subject.pdf6.22 MBels2002seniors_varname.pdf6.16 MB42954High School Longitudinal StudyHSLSStudents who were high school freshmen in the fall of 2009Student Background, Math and Science Education, Classroom Characteristics, The Changing Environment of High School, Postsecondary Education Choice and Enrollment, Transition to Adult Roleshttps://nces.ed.gov/surveys/hsls09722009qsOnpsOntsOff23,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, HSLS:09 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies. ImputationStochastic methods were used to impute the missing values. Specifically, a weighted sequential hot-deck (WSHD; statistical) imputation procedure (Cox 1980; Iannacchione 1982) using the final student analysis weight (W2STUDENT) was applied to the missing values for variables. The WSHD procedure replaces missing data with valid data from a donor record (i.e., first follow-up student [item] respondent) within an imputation class. In general, variables with lower item nonresponse rates were imputed earlier in the process. Skips and Missing Values The HSLS:09 data were edited using procedures developed and implemented for previous studies sponsored by NCES Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in HSLS:09. Please consult the methodology report (coming soon) for more information. Description of missing data codes Missing data code Description -1 Don't know -4 Item not administered: abbreviated interview -5 Suppressed -6 Component not applicable -7 Item legitimate skip/NA -8 Unit nonresponse -9 Missing hsls2009_subject.pdf5.34 MBhsls2009_varname.pdf8.91 MB001056Baccalaureate and BeyondB&BBachelor degree recipients who were surveyed at the time of graduation, one year after graduation, four years after graduation, and ten years after graduationOutcomes for bachelor's degree recipients, Graduate and professional program access, Labor market experiences, Rates of return on investment in education, Post-baccalaureate education, Teacher preparation, Certifications and licenses, Enrollment while employedhttps://nces.ed.gov/surveys/b&b1342016/2017qsOnpsOntsOff29,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values During and following data collection, the data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a number of explanations for missing data; for example, the item may not have been applicable to certain respondents or a respondent may not have known the answer to the question. With the exception of the not applicable codes, missing data were stochastically imputed. Moreover, for hierarchical analyses and developing survey estimates for faculty members corresponding to sample institutions that provided faculty lists and responded to the institution survey, contextual weights were produced for such subsets of the responding faculty members. The table below shows codes for missing values used. Please consult the methodology report for more information. Description of missing data codes Missing data code Description -3 Legitimate skip -7 Not reached -9 Missing 1Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 2Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable imputed and observed will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction. Methodology report coming soonbb2017_subject.pdf608 KBbb2017_varname.pdf420 KB001173542008/2012qsOnpsOntsOff15,500Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, B&B:08/12 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. B&B:08/12 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies. Imputation Variables with missing data were imputed for graduates who were respondents in a study wave . The imputation procedures employed a two-step process. The first step is a logical imputation . If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation. This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values The B&B: 08/12 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:08. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in B&B:08/12. Please consult the First Look for more information. Description of missing value codes Missing data codeDescription -1Don’t know -2Independent student -3Skipped -9Missing 1In other words, if a graduate was a respondent in B&B:09, he or she will have no missing data for variables created as part of the B&B:09 wave. Similarly, if a graduate was a respondent in B&B:12, he or she will have no missing data for variables created as part of the B&B:12 wave, but may have missing data for variables created as part of the B&B:09 wave if he or she was not a respondent in B&B:09. 2Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 3Sequential hot deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction. View methodology reportbb12_subject.pdf26.6 MBbb12_varname.pdf15.6 MB001149311993/2003qsOnpsOntsOff11,200Imputation Variables used in cross-sectional estimates in the Baccalaureate and Beyond descriptive reports were imputed. The variables identified for imputation were used in the two B&B:93/03 descriptive reports (Bradburn, Nevill, and Forrest Cataldi 2006; Alt and Henke 2007). The imputations were performed in three steps. First, the interview variables were imputed using the sequential hot deck imputation method.1 This imputation procedure involves identifying a relatively homogenous group of observations, and within the group selecting a random donor’s value to impute a value for the recipient. Second, using the interview variables, including the newly imputed variable values, derived variables were constructed. Skips and Missing Values Both during and upon completion of data collection, edit checks were performed on the B&B:93/03 data file to confirm that the intended skip patterns were implemented during the interview. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons. The Table below lists each missing value code and its associated meaning in the B&B:93/03 interview. For more information, see the Baccalaureate and Beyond Longitudinal Study (B&B:93/03) methodology report. Description of missing data codes Missing data code Description -1 Missing -2 Not applicable -3 Skipped -4 B&B:97 nonrespondent not sampled -6 Uncodeable, out of range -7 Not reached -8 Item was not reached due to an error -9 Missing, blank 1Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. Under this methodology, while each respondent record has a chance to be selected for use as a hot-deck donor, the number of times a respondent record can be used for imputation will be controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictor) for each item being imputed were defined. Imputation classes were developed by using a Chi-squared Automatic Interaction.View methodology reportbb03_subject.pdf4.56 MBbb03_varname.pdf3.98 MB001151202000/2001qsOffpsOntsOff10,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, B&B:01 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values Both during and upon completion of data collection, edit checks were performed on the B&B:00/01 data file to confirm that the intended skip patterns were implemented during the interview. Following data collection, the information collected in CATI was subjected to various checks and examinations. These checks were intended to confirm that the database reflected appropriate skip-pattern relationships and different types of missing data by inserting special codes. The Table below lists each missing value code and its associated meaning in the B&B:00/01 interview. For more information, see the Baccalaureate and Beyond Longitudinal Study (B&B:00/01) methodology report . Description of missing data codes Missing data code Description -1 Don’t know (CATI variables), Data not available (CADE variables) -2 Refused (CATI variables only) -3 Not applicable (CADE and CATI variables only) -4 B&B:97 nonrespondent not sampled -6 Bad data, out of range -7 Item was not reached (abbreviated and partial CATI interviews) -8 Item was not reached due to a CATI error -9 Data missing, reason unknown (CATI variables) View methodology reportbb01_subject.pdf3.44 MBbb01_varname.pdf3.38 MB001132Baccalaureate and Beyond, Graduate StudentsB&B:GRBachelor degree recipients who were surveyed at the time of graduation, one year after graduation, four years after graduation, and ten years after graduationOutcomes for bachelor's degree recipients, Graduate and professional program access, Labor market experiences, Rates of return on investment in education, Post-baccalaureate education, Teacher preparation, Certifications and licenses, Enrollment while employedhttps://nces.ed.gov/surveys/b&b561993/2003qsOffpsOntsOff4,000Imputation Variables used in cross-sectional estimates in the Baccalaureate and Beyond descriptive reports were imputed. The variables identified for imputation were used in the two B&B:93/03 descriptive reports (Bradburn, Nevill, and Forrest Cataldi 2006; Alt and Henke 2007). The imputations were performed in three steps. First, the interview variables were imputed using the sequential hot deck imputation method.1 This imputation procedure involves identifying a relatively homogenous group of observations, and within the group selecting a random donor’s value to impute a value for the recipient. Second, using the interview variables, including the newly imputed variable values, derived variables were constructed. Skips and Missing Values Both during and upon completion of data collection, edit checks were performed on the B&B:93/03 data file to confirm that the intended skip patterns were implemented during the interview. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons. The Table below lists each missing value code and its associated meaning in the B&B:93/03 interview. For more information, see the Baccalaureate and Beyond Longitudinal Study (B&B:93/03) methodology report. Description of missing data codes Missing data code Description -1 Missing -2 Not applicable -3 Skipped -4 B&B:97 nonrespondent not sampled -6 Uncodeable, out of range -7 Not reached -8 Item was not reached due to an error -9 Missing, blank 1Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. Under this methodology, while each respondent record has a chance to be selected for use as a hot-deck donor, the number of times a respondent record can be used for imputation will be controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictor) for each item being imputed were defined. Imputation classes were developed by using a Chi-squared Automatic Interaction. View methodology reportbb03_subject_students.pdf9.75 MBbb03_varname_students.pdf8.81 MB001252Beginning Postsecondary StudentsBPSBeginning students who were surveyed at the end of their first year, and then three and six years after first starting in postsecondary education. Students’ persistence, progress and attainment of a degree, Labor force experienceshttps://nces.ed.gov/surveys/bps/712012/2017qsOnpsOntsOff22,500Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, BPS:12/17 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:12/17 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values The BPS:12/17 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:12. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in BPS:12/17. Please consult the methodology report (coming soon) for more information. Description of missing data codes Missing data code Description -1 Not classified -2 Not applicable -3 Skipped -8 Double non-respondent -9 Data missing 1Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 2Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction. View methodology reportbps2017_subject.html9.99 MBbps2017_varname.html9.99 MB001353532004/2009qsOnpsOntsOff16,500Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, BPS:04/09 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:04/09 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values The BPS:04/09 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:04. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in BPS:04/09. Please consult the methodology report (coming soon) for more information. Description of missing data codes Missing data code Description -2 Independent student -3 Skipped -9 Data missing 1Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 2Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction. View methodology reportbps2009_subject.pdf7.50 MBbps2009_varname.pdf6.20 MB00133311996/2001qsOnpsOntsOff12,000Imputation Logical imputations were performed where items were missing but their values could be implicitly determined. Skips and Missing Values During and following data collection, the CATI/CAPI data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a variety of explanations for missing data within individual data elements. The table below shows codes for missing values used in BPS:01. Please consult the methodology report for more information. Description of missing data codes Missing data code Description -1 Don’t know -2 Refused -3 Legitimate skip (item was intentionally not collected because variable was not applicable to this student) -6 Bad data, out of range, uncodeable userexit string -7 Not reached -8 Missing, CATI error -9 Missing View methodology reportbps2001_subject.pdf9.20 MBbps2001_varname.pdf7.10 MB001334321990/1994qsOnpsOntsOff6,600Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, BPS:94 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:94 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values The BPS:94 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data.A variety of explanations are possible for missing data.The table below shows codes for missing values used in BPS:94. Please consult the methodology report for more information. Description of missing data codes Missing data code Description -2 Independent student -3 Skipped -9 Data missing View methodology reportbps1994_subject.pdf4.34 MBbps1994_varname.pdf4.17 MB001335National Postsecondary Student Aid Study, UndergraduateNPSAS:UGStudents who were undergraduates at the time of interviewGeneral demographics, Types of aid and amounts received, Cost of attending college, Combinations of work, study, and borrowing, Enrollment patternshttps://nces.ed.gov/surveys/npsas12112016qsOnpsOntsOn89,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. The table below shows the set of reserve codes for missing values used in NPSAS 2016. Please consult the data file documentation report for more information. Description of missing data codes Missing data code Description -3 Skipped -9 Missing 1Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 2Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variableimputed and observedwill resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction. View methodology reportnpsas2016ug_subject.pdf8.7 MBnpsas2016ug_varname.pdf6.7 MB0014628212012qsOnpsOntsOn95,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Missing Values and Imputation Following data collection, the data are subjected to various consistency and quality control checks before release. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Except for data that were missing for cases to which they did not apply (e.g., whether a spouse is enrolled in college for unmarried students) and in a small number of items describing institutional characteristics, missing data were imputed using a two-step process. The first step is a logical imputation.1 If a value could be calculated from the logical relationships with other variables, then that information was used to impute the value for the observation with a missing value. The second step is weighted hot deck imputation.2 This procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor's value to impute a value for the observation with a missing value. The table below shows the set of missing value codes for missing values that were not imputed in NPSAS:12. More information is available from the NPSAS:12 Data File Documentation (http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2014182). Description of missing value codes Missing data codeDescription -1Not classified -2Not applicable -3Skipped -9Missing 1Logical imputation is a process that aims to infer or deduce the missing values from values for other items. 2Sequential hot deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent's answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using the chi-square automatic interaction detection algorithm. View methodology reportnpsas2012ug_subject.pdf6.90 MBnpsas2012ug_varname.pdf5.45 MB0014365112008qsOnpsOntsOn113,500Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. The table below shows the set of reserve codes for missing values used in NPSAS 2008. Please consult the methodology report for more information. Description of missing data codes Missing data code Description -1 Not classified -2 Not applicable -6 Out of range -8 Item was not reached due to an error -9 Missing 1Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 2Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction. View methodology reportnpsas2008ug_subject.pdf8.10 MBnpsas2008ug_varname.pdf6.40 MB0014372412004qsOnpsOntsOn79,900 Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation The imputation procedures employed a two-step process. In the first step, the matching criteria and imputation classes that were used to stratify the dataset were identified such that all imputation was processed independently within each class. In the second step, the weighted sequential hot deck process1 was implemented, whereby missing data were replaced with valid data from donor records that match the recipients with respect to the matching criteria. Variables requiring imputation were not imputed simultaneously. However, some variables that were related substantively were grouped together into blocks, and the variables within a block were imputed simultaneously. Basic demographic variables were imputed first using variables with full information to determine the matching criteria. The order in which variables were imputed was also determined to some extent by the substantive nature of the variables. For example, basic demographics (such as age) were imputed first and these were used to process education variables (such as student level and enrollment intensity) which in turn were used to impute the financial aid variables (such as aid receipt and loan amounts). Skips and Missing Values Edit checks were performed on the NPSAS:04 student interview data and CADE data, both during and upon completion of data collection, to confirm that the intended skip patterns were implemented in both instruments. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons. The table below shows the set of reserve codes for missing values used in NPSAS 2004. Please consult the methodology report for more information. Description of missing data codes Missing data code Description -1 Not classified -3 Legitimate skip -9 Missing 1Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.View methodology reportnpsas2004ug_subject.pdf7.75 MBnpsas2004ug_varname.pdf6.00 MB0014383512000qsOffpsOntsOn50,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, NPSAS:00 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing ValuesThe NPSAS:00 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in NPSAS:00 Please consult the methodology report for more information. Description of missing data codes Missing data code Description -2 Independent student -3 Skipped -9 Data missing View methodology reportnpsas2000ug_subject.pdf8.68 MBnpsas2000ug_varname.pdf7.25 MB0014393611996qsOffpsOntsOn41,500Imputation Values for 22 analysis variables were imputed. The variables were imputed using a weighted hot deck procedure, with the exception of estimated family contribution (EFC), which was imputed through a multiple regression approach.The weighed hot deck imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing ValuesThe NPSAS:96 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in NPSAS:96 Please consult the methodology report for more information. Description of missing data codes Missing data code Description -1 Don't know -2 Refused -3 Skipped -8 Data source not available -9 Data missing View methodology reportnpsas1996ug_subject.pdf3.47 MBnpsas1996ug_varname.pdf3.09 MB001440221993qsOffpsOntsOff52,700Derived Variables and Imputed Values Approximately 800 variables have been constructed based on data collected in the NPSAS:93. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values. Skips and Missing Values Both the student and parent CATI programs were designed to accommodate responses of "refusal" and "don't know" to any single question. Typically, refusal responses are given for items considered too sensitive by the respondent. "Don't know" responses may be given for any one of several reasons: (1) the respondent misunderstands the question wording, and is not offered subsequent explanation by the interviewer; (2) the respondent is hesitant to provide "best guess" responses, with insufficient prompting from the interviewer; (3) the respondent truly does not know the answer; or (4) the respondent chooses to respond with "don't know" as an implicit refusal to answer the question. Whenever they occur, indeterminate responses in the data set must be resolved by imputation or otherwise dealt with during analysis. The table below shows the set of reserve codes for missing values used in NPSAS 1993. Please consult the data file documentation report for more information. Description of missing data codes Missingdata code Description -1 Legitimate skip -7 Missing, refused -8 Missing, don't know -9 Missing, blank View methodology reportnpsas93ug_subject.pdf500 KBnpsas93ug_varname.pdf500 KB001440141990qsOffpsOntsOff46,800Imputation Variables with more than 5 percent missing cases were imputed. After using information from all appropriate secondary sources, there remained eight variables which required some statistical imputation. Two methods of statistical imputation were used, regression-based or hot deck. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. The table below shows the set of reserve codes for missing values used in NPSAS 1990. Please consult the data file documentation report for more information. Description of missing data codes Missingdata codeDescription-1 Legitimate skip -9 Missing, blankView methodology reportnpsas90ug_subject.pdf500 KBnpsas90ug_varname.pdf500 KB001440171987qsOffpsOntsOff34,500Derived Variables and Imputed Values Approximately 800 variables have been constructed based on data collected in the NPSAS:87. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values. Skips and Missing Values Both the student and parent CATI programs were designed to accommodate responses of "refusal" and "don't know" to any single question. Typically, refusal responses are given for items considered too sensitive by the respondent. "Don't know" responses may be given for any one of several reasons: (1) the respondent misunderstands the question wording, and is not offered subsequent explanation by the interviewer; (2) the respondent is hesitant to provide "best guess" responses, with insufficient prompting from the interviewer; (3) the respondent truly does not know the answer; or (4) the respondent chooses to respond with "don't know" as an implicit refusal to answer the question. Whenever they occur, indeterminate responses in the data set must be resolved by imputation or otherwise dealt with during analysis. The table below shows the set of reserve codes for missing values used in NPSAS 1993. Please consult the data file documentation report for more information. Description of missing data codes Missingdata code Description -1 Legitimate skip -7 Missing, refused -8 Missing, don't know -9 Missing, blank View methodology reportnpsas87ug_subject.pdf500 KBnpsas87ug_varname.pdf500 KB001440National Postsecondary Student Aid Study, GraduateNPSAS:GRStudents who were graduate and first-professional students at the time of interview General demographics, Types of aid and amounts received, Cost of attending college, Combinations of work, study, and borrowing, Enrollment patternshttps://nces.ed.gov/surveys/npsas12222016qsOnpsOntsOn24,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. The table below shows the set of reserve codes for missing values used in NPSAS 2016. Please consult the data file documentation report for more information. Description of missing data codes Missing data code Description -3 Skipped -9 Missing 1Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 2Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variableimputed and observedwill resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction. View methodology reportnpsas2016gr_subject.pdf6.6 MBnpsas2016gr_varname.pdf5.4 MB0015638322012qsOnpsOntsOn16,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Missing Values and Imputation Following data collection, the data are subjected to various consistency and quality control checks before release. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Except for data that were missing for cases to which they did not apply (e.g., whether a spouse is enrolled in college for unmarried students) and in a small number of items describing institutional characteristics, missing data were imputed using a two-step process. The first step is a logical imputation.1 If a value could be calculated from the logical relationships with other variables, then that information was used to impute the value for the observation with a missing value. The second step is weighted hot deck imputation.2 This procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor's value to impute a value for the observation with a missing value. The table below shows the set of missing value codes for missing values that were not imputed in NPSAS:12. More information is available from the NPSAS:12 Data File Documentation (http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2014182). Description of missing value codes Missing data codeDescription -1Not classified -2Not applicable -3Skipped -9Missing 1Logical imputation is a process that aims to infer or deduce the missing values from values for other items. 2Sequential hot deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent's answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using the chi-square automatic interaction detection algorithm. View methodology reportnpsas2012gr_subject.pdf1.47 MBnpsas2012gr_varname.pdf4.20 MB0015415222008qsOnpsOntsOn14,200Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation.1 If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. The table below shows the set of reserve codes for missing values used in NPSAS 2008. Please consult the methodology report for more information. Description of missing data codes Missing data code Description -1 Not classified -3 Not applicable -6 Out of range -8 Item was not reached due to an error -9 Missing 1Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 2Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.View methodology reportnpsas2008gr_subject.pdf1.02 MBnpsas2008gr_varname.pdf748 KB0015421222004qsOnpsOntsOn10,900 Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation The imputation procedures employed a two-step process. In the first step, the matching criteria and imputation classes that were used to stratify the dataset were identified such that all imputation was processed independently within each class. In the second step, the weighted sequential hot deck process1 was implemented, whereby missing data were replaced with valid data from donor records that match the recipients with respect to the matching criteria. Variables requiring imputation were not imputed simultaneously. However, some variables that were related substantively were grouped together into blocks, and the variables within a block were imputed simultaneously. Basic demographic variables were imputed first using variables with full information to determine the matching criteria. The order in which variables were imputed was also determined to some extent by the substantive nature of the variables. For example, basic demographics (such as age) were imputed first and these were used to process education variables (such as student level and enrollment intensity) which in turn were used to impute the financial aid variables (such as aid receipt and loan amounts). Skips and Missing Values Edit checks were performed on the NPSAS:04 student interview data and CADE data, both during and upon completion of data collection, to confirm that the intended skip patterns were implemented in both instruments. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons. The table below shows the set of reserve codes for missing values used in NPSAS 2004. Please consult the methodology report for more information. Description of missing data codes Missing data code Description -1 Not classified -3 Legitimate skip -9 Missing 1Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.View methodology reportnpsas2004gr_subject.pdf1.06 MBnpsas2004gr_varname.pdf787 KB0015433722000qsOffpsOntsOn12,000Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, NPSAS:00 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values The NPSAS:00 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in NPSAS:00 Please consult the methodology report for more information. Description of missing data codes Missing data code Description -2 Independent student -3 Skipped -9 Data missing View methodology reportnpsas2000gr_subject.pdf1.71 MBnpsas2000gr_varname.pdf1.43 MB0015443821996qsOffpsOntsOn7,000Imputation Values for 22 analysis variables were imputed. The variables were imputed using a weighted hot deck procedure, with the exception of estimated family contribution (EFC), which was imputed through a multiple regression approach.The weighed hot deck imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing ValuesThe NPSAS:96 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in NPSAS:96 Please consult the methodology report for more information. Description of missing data codes Missing data code Description -1 Don''t know -2 Refused -3 Skipped -8 Data source not available -9 Data missing View methodology reportnpsas1996gr_subject.pdf2.53 MBnpsas1996gr_varname.pdf2.13 MB001545131993qsOffpsOntsOff13,400Derived Variables and Imputed Values Approximately 800 variables have been constructed based on data collected in the NPSAS:93. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values. Skips and Missing Values Both the student and parent CATI programs were designed to accommodate responses of "refusal" and "don't know" to any single question. Typically, refusal responses are given for items considered too sensitive by the respondent. "Don't know" responses may be given for any one of several reasons: (1) the respondent misunderstands the question wording, and is not offered subsequent explanation by the interviewer; (2) the respondent is hesitant to provide "best guess" responses, with insufficient prompting from the interviewer; (3) the respondent truly does not know the answer; or (4) the respondent chooses to respond with "don't know" as an implicit refusal to answer the question. Whenever they occur, indeterminate responses in the data set must be resolved by imputation or otherwise dealt with during analysis. The table below shows the set of reserve codes for missing values used in NPSAS 1993. Please consult the data file documentation report for more information. Description of missing data codes Missingdata code Description -1 Legitimate skip -7 Missing, refused -8 Missing, don't know -9 Missing, blank View methodology reportnpsas93gr_subject.pdf500 KBnpsas93gr_varname.pdf500 KB001540181987qsOffpsOntsOff8,600Derived Variables and Imputed Values Approximately 800 variables have been constructed based on data collected in the NPSAS:93. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values. Skips and Missing Values Both the student and parent CATI programs were designed to accommodate responses of "refusal" and "don't know" to any single question. Typically, refusal responses are given for items considered too sensitive by the respondent. "Don't know" responses may be given for any one of several reasons: (1) the respondent misunderstands the question wording, and is not offered subsequent explanation by the interviewer; (2) the respondent is hesitant to provide "best guess" responses, with insufficient prompting from the interviewer; (3) the respondent truly does not know the answer; or (4) the respondent chooses to respond with "don't know" as an implicit refusal to answer the question. Whenever they occur, indeterminate responses in the data set must be resolved by imputation or otherwise dealt with during analysis. The table below shows the set of reserve codes for missing values used in NPSAS 1993. Please consult the data file documentation report for more information. Description of missing data codes Missingdata code Description -1 Legitimate skip -7 Missing, refused -8 Missing, don't know -9 Missing, blank View methodology reportnpsas87gr_subject.pdf500 KBnpsas87gr_varname.pdf500 KB001540161990qsOffpsOntsOff14,300Imputation Variables with more than 5 percent missing cases were imputed. After using information from all appropriate secondary sources, there remained eight variables which required some statistical imputation. Two methods of statistical imputation were used, regression-based or hot deck. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. The table below shows the set of reserve codes for missing values used in NPSAS 1990. Please consult the data file documentation report for more information. Description of missing data codes Missingdata codeDescription-1 Legitimate skip -9 Missing, blankView methodology reportnpsas90gr_subject.pdf500 KBnpsas90gr_varname.pdf500 KB001540National Study of Postsecondary FacultyNSOPFPostsecondary facultyWorkload, Equity issues, Involvement in undergraduate teaching, Relationship between teaching and researchhttps://nces.ed.gov/surveys/nsopf282004qsOnpsOntsOff26,100Perturbation A restricted faculty-level data file was created for release to individuals who apply for and meet standards for such data releases. While this file does not include personally identifying information (i.e., name and Social Security number), other data (i.e., institution, Integrated Postsecondary Education Data System [IPEDS] ID, demographic information, and salary data) may be manipulated in such a way to seem to identify data records corresponding to a particular faculty member. To protect further against such situations, some of the variable values were swapped between faculty respondents. This procedure perturbed and added additional uncertainty to the data. Thus, associations made among variable values to identify a faculty respondent may be based on the original or edited, imputed and/or swapped data. For the same reasons, the data from the institution questionnaire were also swapped to avoid data disclosure. Imputation Item imputation for the faculty questionnaire was performed in several steps. In the first step, the missing values of gender, race, and ethnicity were filled—using cold-deck imputation1— based on the sampling frame information or institution record data. These three key demographic variables were imputed prior to any other variables since they were used as key predictors for all other variables on the data file. After all logical2 and cold-deck imputation procedures were performed, the remaining variables were imputed using the weighted sequential hot-deck method.3 Initially, variables were separated into two groups: unconditional and conditional variables. The first group (unconditional) consisted of variables that applied to all respondents, while the second group (conditional) consisted of variables that applied to only a subset of the respondents. That is, conditional variables were subject to “gate” questions. After this initial grouping, these groups were divided into finer subgroups. After all variables were imputed, consistency checks were applied to the entire faculty data file to ensure that the imputed values did not conflict with other questionnaire items, observed or imputed. This process involved reviewing all of the logical imputation and editing rules as well. Skips and Missing Values During and following data collection, the data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a number of explanations for missing data; for example, the item may not have been applicable to certain respondents or a respondent may not have known the answer to the question. With the exception of the not applicable codes, missing data were stochastically imputed. Moreover, for hierarchical analyses and developing survey estimates for faculty members corresponding to sample institutions that provided faculty lists and responded to the institution survey, contextual weights were produced for such subsets of the responding faculty members. The table below shows codes for missing values used in NSOPF:04. Please consult the methodology report for more information. Description of missing data codes Missing data code Description -3 Legitimate skip -7 Not reached -9 Missing 1Cold-deck imputation involves replacing the missing values with data from sources such as data used for sampling frame construction. While resource intensive, these methods often obtain the actual value that is missing. Stochastic imputation methods, such as sequential hot-deck imputation, rely on the observed data to provide replacing values (donors) for records with missing values. 2Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions. 3Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. Under this methodology, while each respondent record has a chance to be selected for use as a hot-deck donor, the number of times a respondent record can be used for imputation will be controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictor) for each item being imputed were defined. Imputation classes were developed by using a Chi-squared Automatic Interaction.View methodology reportnsopf04_subject.pdf1.16 MBnsopf04_varname.pdf926 KB001646261999qsOffpsOntsOff18,000Both the faculty and institution questionnaire data were edited using seven principles designed to improve data quality and consistency.Menu items. For many questions there were several sub-items listed where the respondent was asked to give a response for each sub-item. These questions were cleaned with several procedures. First if the main question had an “NA” (Not Applicable) check box and that box was checked, all of the sub-items were set to a value of “no” or “zero” depending on the wording of the question. Second, if the respondent had filled out one or more of the sub-items with a “yes” response or a positive number but had left other sub-items blank, the missing sub-items were set to “no,” “zero,” or “don’t know” depending on the question wording. If all sub-items were missing and there was no “NA” box, or the “NA” box was not checked, the case was flagged and the data values were imputed for that question. Examples of these types of questions are Question 21 in the institution questionnaire and Question 29 in the faculty questionnaire.Inter-item consistency checks. Many types of inter-item consistency checks were performed on the data. One procedure was to check groups of related items for internal consistency and to make adjustments to make them consistent. For example, in questions that asked about a spouse in the faculty questionnaire (Questions 66i, Q76i, and 77a) if respondents indicated that they did not have a spouse in one or more of the questions, the other questions were checked for consistency and corrected as necessary. Another procedure checked “NA” boxes. If the respondent had checked the “NA” box for a question but had filled in any of the sub-items for that question the “NA” box was set to blank. For example, this procedure was used with Question 21 in the institution questionnaire and Question 16 in the faculty questionnaire. A third procedure was to check filter items for which more detail was sought in a follow-up open-ended or closed-ended question. If detail was provided, then the filter question was checked to make sure the appropriate response was recorded. For example, this procedure was used with Question 11 in the institution questionnaire and Question 12E in the faculty questionnaire.Percent items. All items where respondents were asked to give a percentage were checked to make sure they summed to 100 percent. The editing program also looked for any numbers between 0 and 1 to make sure that respondents did not fill in the question with a decimal rather than a percentage. All fractions of a percent were rounded to the nearest whole percent. An example of this type of item is Question 31 in the faculty questionnaire.Data imputation for the faculty questionnaire was performed in four steps. The imputation method for each variable is specified in the labels for the imputation flags in the faculty dataset.Logical imputation. The logical imputation was conducted during the data cleaning steps as explained in the immediately preceding section. Cold deck. Missing responses were filled in with data from the sample frame whenever the relevant data were available. Examples include gender, race, and employment status.Hot deck. This procedure selected non-missing values from “sequential nearest neighbors” within the imputation class. All questions that were categorical and had more than 16 categories were imputed with this method. An example is Question Q14 – principal field of teaching. The imputation class for this question was created using faculty stratum and instructional duty status (Q1). Regression type. This procedure employed SAS PROC IMPUTE21. All items that were still missing after the logical, cold deck, and hot deck imputation procedures were imputed with this method. Project staff selected the independent variables by first looking through the questionnaire for logically related items and then by conducting a correlation analysis of the questions against each other to find the top correlates for each item.View methodology reportnsopf99_subject.pdf500 KBnsopf99_varname.pdf500 KB001646251993qsOffpsOntsOff31,000Depending on the scale of the variable being imputed, one of two methods were used:1) Regression imputation was used for continuous and dichotomous variables; and2) Hotdeck imputation was used for unordered polytomous variables.The regression method incorporated in NCES’s PROC IMPUTE was used to impute missing values for approximately 90 percent of the 395 items on the faculty questionnaire.Of the total of 395 items, 353 were imputed using the regression-based imputation procedures only.View methodology reportnsopf93_subject.pdf500 KBnsopf93_varname.pdf500 KB001646231988qsOffpsOntsOff25,000NSOPF:88 was conducted with a sample of 480 institutions (including 2-year, 4-year, doctoral-granting, and other colleges and universities), some 11,010 faculty, and more than 3,000 department chairpersons. Institutions were sampled from the 1987 IPEDS universe and were stratified by modified Carnegie Classifications and size (faculty counts). These strata were (1) public, research; (2) private, research; (3) public, other Ph.D. institution (not defined in any other stratum); (4) private, other Ph.D. institution (not defined in any other stratum); (5) public, comprehensive; (6) private, comprehensive; (7) liberal arts; (8) public, 2-year; (9) private, 2-year; (10) religious; (11) medical; and (12) “other” schools (not defined in any other stratum). Within each stratum, institutions were randomly selected. Of the 480 institutions selected, 450 (94 percent) agreed to participate and provided lists of their faculty and department chairpersons. Within 4-year institutions, faculty and department chairpersons were stratified by program area and randomly sampled within each stratum; within 2-year institutions, simple random samples of faculty and department chairpersons were selected; and within specialized institutions (religious, medical, etc.), faculty samples were randomly selected (department chairpersons were not sampled). At all institutions, faculty were also stratified on the basis of employment status—full-time and part-time. Note that teaching assistants and teaching fellows were excluded in NSOPF:88.Although NSOPF:88 consisted of three questionnaires, imputations were only performed for faculty item nonresponse. The within-cell random imputation method was used to fill in most Faculty Questionnaire items that had missing data.nsopf88_subject.pdf500 KBnsopf88_varname.pdf500 KB001646National Study of Postsecondary Faculty, InstitutionsNSOPFPostsecondary institutionsFaculty tenure policies, Union representation, and Faculty attritionhttps://nces.ed.gov/surveys/nsopf292004qsOnpsOntsOff900 Imputation The imputation process for the missing data from the institution questionnaire involved similar steps to those used for imputation of the faculty data. The missing data for variables were imputed using the weighted sequential hot-deck method.1 Analogous to the imputation process for the faculty data, the variables were partitioned into conditional and unconditional groups. The unconditional variables were sorted by percent missing and then imputed in the order from the lowest percent missing to the highest. The conditional group was partitioned into three subgroups based on the level of conditionality for each variable, and then imputed in that order. The imputation class for both unconditional and conditional variables consisted of the institution sampling stratum, and the sorting variables included the number of full-time and part-time faculty members. Skips and Missing Values During and following data collection, the data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a number of explanations for missing data; for example, the item may not have been applicable to certain respondents or a respondent may not have known the answer to the question. With the exception of the not applicable codes, missing data were stochastically imputed. Moreover, for hierarchical analyses and developing survey estimates for faculty members corresponding to sample institutions that provided faculty lists and responded to the institution survey, contextual weights were produced for such subsets of the responding faculty members. The table below shows codes for missing values used in NSOPF:04. Please consult the methodology report for more information. Description of missing data codes Missing data code Description -3 Legitimate skip -7 Not reached -9 Missing 1Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. Under this methodology, while each respondent record has a chance to be selected for use as a hot-deck donor, the number of times a respondent record can be used for imputation will be controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictor) for each item being imputed were defined. Imputation classes were developed by using a Chi-squared Automatic Interaction. View methodology reportnsopf04inst_subject.pdf543 KBnsopf04inst_varname.pdf471 KB001747National Teacher and Principal Survey, Public School PrincipalsNTPSPublic school principalsExperience, Training, Education, and Professional Development, Goals and Decision Making, Teacher and Aide Professional Development, School Climate and Safety, Instructional Time, Working Conditions and Principal Perceptions, Teacher and School Performancehttps://nces.ed.gov/surveys/ntps1262015-2016qsOnpsOntsOff8,300ImputationThe NTPS used two main approaches to impute data. First, donor respondent methods, such as hot-deck imputation, were used. Second, if no suitable donor case could be matched, the few remaining items were imputed using mean or mode from groups of similar cases to impute a value to the item with missing data. Finally, in rare cases for which imputed values were inconsistent with existing questionnaire data or out of the range of acceptable values, Census Bureau analysts looked at the items and tried to determine an appropriate value.WeightingWeighting of the sample units was carried out to produce national estimates for public schools, principals, and teachers. The weighting procedures used in NTPS had three purposes: to take into account the school's selection probability; to reduce biases that may result from unit nonresponse; and to make use of available information from external sources to improve the precision of sample estimates.ntps2016principals_subject.pdf1.53 MBntps2016principals_varname.pdf1.70 MB001965National Teacher and Principal Survey, Public SchoolsNTPSPublic schoolsTeacher demand, teacher and principal characteristics, general conditions in schools, principals' and teachers' perceptions of school climate and problems in their schools, teacher compensation, district hiring and retention practices, basic characteristics of the student populationhttps://nces.ed.gov/surveys/ntps1272015-2016qsOnpsOntsOff8,300ImputationThe NTPS used two main approaches to impute data. First, donor respondent methods, such as hot-deck imputation, were used. Second, if no suitable donor case could be matched, the few remaining items were imputed using mean or mode from groups of similar cases to impute a value to the item with missing data. Finally, in rare cases for which imputed values were inconsistent with existing questionnaire data or out of the range of acceptable values, Census Bureau analysts looked at the items and tried to determine an appropriate value.WeightingWeighting of the sample units was carried out to produce national estimates for public schools, principals, and teachers. The weighting procedures used in NTPS had three purposes: to take into account the school's selection probability; to reduce biases that may result from unit nonresponse; and to make use of available information from external sources to improve the precision of sample estimates.ntps2016schools_subject.pdf2.59 MBntps2016schools_varname.pdf3.35 MB002066Early Childhood Program ParticipationECPPChildren who were enrolled in some type of childcare programChildren's participation, Relative care, Nonrelative care, Center-based care, Head Start and Early Head start programs, time spent in care, number of children and care providershttps://nces.ed.gov/nhes13062016qsOnpsOntsOn5,800ImputationFour approaches to imputation were used in the NHES:2016: logic-based imputation, which was used whenever possible; unweighted sequential hot deck imputation, which was used for the majority of the missing data (i.e., for all variables that were not boundary and sort variables—described below); weighted random imputation, which was used for a small number of variables including boundary and sort variables; and manual imputation, which was used in a very small number of cases for a small number of variables.For more information about these approaches, please see the NHES: 2016 Data File User's Manual. ecpp2016_subject.pdfecpp2016_varname.pdf00216912962012qsOnpsOntsOn7,900ImputationThree approaches to imputation were used in the NHES:2012: unweighted sequential hot deck imputation, which was used for the majority of the missing data, that is, for all variables that were not required for Interview Status Recode (ISR) classification, as described in chapter 4; weighted random imputation, which was used for a small number of variables; and manual imputation, which was used in a very small number of cases for most variables.For more information about these approaches, please see the NHES: 2012 Data File User's Manual. ecpp2012_subject.pdfecpp2012_varname.pdf002168Adult Training and Education SurveyATESAdults who were enrolled in a training or literacy programEducation, Certifications and Licenses, Certificates, Work Experience Programs, Employment, Backgroundhttps://nces.ed.gov/nhes1332016qsOnpsOntsOff47,700ImputationFour approaches to imputation were used in the NHES:2016: logic-based imputation, which was used whenever possible; unweighted sequential hot deck imputation, which was used for the majority of the missing data (i.e., for all variables that were not boundary and sort variables—described below); weighted random imputation, which was used for a small number of variables including boundary and sort variables; and manual imputation, which was used in a very small number of cases for a small number of variables.For more information about these approaches, please see the NHES: 2016 Data File User's Manual. ates2016_subject.pdf2.84 MBates2016_varname.pdf2.90 MB002270Parent and Family Involvement in EducationPFIParents and families who were involved in their child's educationChildren's schooling, Families and schools, Homework, Family activities, Health, Background, Householdhttps://nces.ed.gov/nhes13272016qsOnpsOntsOn13,500ImputationFour approaches to imputation were used in the NHES:2016: logic-based imputation, which was used whenever possible; unweighted sequential hot deck imputation, which was used for the majority of the missing data (i.e., for all variables that were not boundary and sort variables—described below); weighted random imputation, which was used for a small number of variables including boundary and sort variables; and manual imputation, which was used in a very small number of cases for a small number of variables.For more information about these approaches, please see the NHES: 2016 Data File User's Manual. pfi2016_subject.pdf2.5 MBpfi2016_varname.pdf2.1 MB00237213172012qsOnpsOntsOn17,200ImputationThree approaches to imputation were used in the NHES:2012: unweighted sequential hot deck imputation, which was used for the majority of the missing data, that is, for all variables that were not required for Interview Status Recode (ISR) classification, as described in chapter 4; weighted random imputation, which was used for a small number of variables; and manual imputation, which was used in a very small number of cases for most variables.For more information about these approaches, please see the NHES: 2012 Data File User's Manual. pfi2012_subject.pdfpfi2012_varname.pdf002371High School and BeyondHSBStudents who were high school sophomores in 1980Social background, Test battery and school record, Home educational support system, Postsecondary education choice and enrollment, Employment, Outcomeshttps://nces.ed.gov/surveys/hsb/index.asp1351980qsOnpsOntsOff14,800Nonresponse Nonresponse inevitably introduces some degree of error into survey results. In examining the impact of nonresponse, it is useful to think of the survey population as including two strata--a respondent stratum that consists of all units that would have provided data had they been selected for the survey, and a nonrespondent stratum that consists of all units that would not have provided data had they been selected. The actual sample of respondents necessarily consists entirely of units from the respondent stratum. Thus, sample statistics can serve as unbiased estimates only for the respondent stratum; as estimates for the entire population, the sample statistics will be biased to the extent that the characteristics of the respondents differ from those of the entire population.In the High School and Beyond study, there were two stages of sample selection and therefore two stages of nonresponse. During the base year survey, sample schools were asked to permit the selection of individual sophomores and seniors from school rosters and to designate "survey days" for the collection of student questionnaire and test data. Schools that refused to cooperate in either of these activities were dropped from the sample. Individual students at cooperating schools could also fail to take part in the base year survey. Unlike "refusal" schools, nonparticipating students were not dropped from the sample; they remained eligible for selection into the follow-up samples.Estimates based on student data from the base year surveys include two components of nonresponse bias: bias introduced by nonresponse at the school level, and bias introduced by nonresponse on the part of students attending cooperating schools. Each component of the overall bias depends on two factors--the level of nonresponse and the difference between respondents and nonrespondents: Bias = P1(Y1R - Y1NR) + P2(Y2R - Y2NR)in which P1 = the proportion of the population of students attending schools that would have been nonrespondents,YlNR = the parameter describing the population of students attending nonrespondent schools, P2 = the proportion of students attending respondent schools who would have been nonrespondents, and Y2NR = the parameter describing this group of students.Nonresponse bias will be small if the nonrespondent strata constitute only a small portion of the survey population or if the differences between respondents and nonrespondents are small. The proportions P1 and P2 can generally be estimated from survey data using appropriately weighted nonresponse rates. The implications of the equation can be easily seen in terms of a particular base year estimate. On the average, sophomores got 10.9 items right on a standardized vocabulary test. This figure is an estimate of Y2R, the population mean for all participating students at cooperating schools. Now, suppose that sophomores at cooperating schools average two more correct than sophomores attending refusal schools (Y1R - Y1NR = 2), and suppose further that among sophomores attending cooperating schools, student respondents average one more correct answer than student nonrespondents (Y2R - Y2NR = 1). Noting that the base year school nonresponse rate was about .30 and the student nonresponse rate for sophomores was about .12, we can use these figures as estimates of P1 and P2 and we can use this equation to calculate the bias as: Bias = .30(2) + .12(1) = .72 That is, the sample estimate is biased by about .7 of a test score point.This example assumes knowledge of the relevant population means; in practice, of course, they are not known and, although Pl and P2 can generally be estimated from the nonresponse rates, the lack of survey data for nonrespondents prevents the estimation of the nonresponse bias. The High School and Beyond study is an exception to this general rule: during the first follow-up, school questionnaire data were obtained from most of the base year refusal schools, and student data were obtained from most of the base year student nonrespondents selected for the first follow-up sample. These data provide a basis for assessing the magnitude of nonresponse bias in base year estimates.The bias introduced by base year school-level refusals is of particular concern since it carries over into successive rounds of the survey. Students attending refusal schools were not sampled during the base year and have no chance for selection into subsequent rounds of observation. To the extent that these students differ from students from cooperating schools during later waves of the study, the bias introduced by base year school nonresponse will persist. Student nonresponse is not carried over in this way since student nonrespondents remain eligible for sampling in later waves of the study.The results of three types of analyses concerning nonresponse are described in an earlier report. Based on school questionnaire data, schools that participated during the base year were compared with all eligible schools. Based on the first follow-up student data, base year student respondents were compared with nonrespondents. Finally, student nonresponse during the first follow-up survey was analyzed. Taken together, these earlier analyses indicated that nonresponse had little effect on base year and first follow-up estimates. The results presented there suggest that the school-level component of the bias affected base year estimates by 2 percent or less and that the student-level component had even less impact.hsb1980_subject.pdf17.9 MBhsb1980_varname.pdf13.4 MB002556National Education Longitudinal Study of 1988NELS:88Students who were eighth graders in 1988School, work, home experiences, educational resources and support, the role in education of parents and peers, neighborhood characteristics, educational and occupational aspirations, other student perceptionshttps://nces.ed.gov/surveys/nels88/1361988qsOnpsOntsOff15,000NonresponseSchool-level nonresponse is a serious concern because it carries over into successive rounds of NELS:88. Students attending schools that did not cooperate in the base year were not sampled and had little or no chance of selection into the follow-up samples. To the extent that students at noncooperating schools differ from students at cooperating schools, the student-level bias introduced by base-year school noncooperation persists during subsequent waves. Nonresponse adjustments to weights are an attempt to compensate for bias in the estimate for a particular subgroup; they do not adjust for nonresponse bias within subgroups.In the base year, nonresponding schools were asked to supply information about key school questionnaire variables, and virtually all did so. Based on these data, analysis of school-level nonresponse suggests that, to the extent that schools can be characterized by size, control, organizational structure, student composition, and other characteristics, the impact of nonresponding schools on school level estimates is small.25 Readers interested in more information about the analyses of school nonresponse rates and bias for the NELS:88 base year should refer to the NELS:88 Base-Year Sample Design Report (Spencer et al. 1990). School nonresponse was not assessed in the first or second follow-ups for two reasons. First, there was practically no school-level nonresponse; institutional cooperation levels approached 99 percent in both rounds. Second, the first and second follow-up samples were student-driven, unlike the two-stage initial sample design in the base year. Hence, even if a school refused in either the first or second follow-ups, the individual student was pursued outside of school.25. The use of school questionnaire variables to assess bias in estimates concerning characteristics of the student population is not entirely straightforward. Still, to the extent that school characteristics are closely related to the characteristics of the students attending them, estimates based on school questionnaire data can serve as reasonable proxies for more direct estimates of student-level unit nonresponse bias.nels1988_subject.pdf6.9 MBnels1988_varname.pdf15.1 MB002657Adult Training and Education Survey: 20162016Adult EducationqsOnpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003Adult EducationqsOnpsOntsOffBaccalaureate and Beyond: 1993/2003 Graduate students1993/2003PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 1993/2003 Graduate students1993/2003Adult EducationqsOffpsOntsOffBaccalaureate and Beyond: 2000/20012000/2001PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 2000/20012000/2001Adult EducationqsOffpsOntsOffBaccalaureate and Beyond: 2008/20122008/2012PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2008/20122008/2012Adult EducationqsOnpsOntsOffBaccalaureate and Beyond: 2016/20172016/2017PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2016/20172016/2017Adult EducationqsOnpsOntsOffBeginning Postsecondary Students: 1990/19941990/1994PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 1996/20011996/2001PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 2004/20092004/2009PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 2012/20172012/2017PostsecondaryqsOnpsOntsOffEarly Childhood Program Participation: 20122012P-12qsOnpsOntsOn6Early Childhood Program Participation: 20162016P-12qsOnpsOntsOn6Education Longitudinal Study of 20022002PostsecondaryqsOnpsOntsOffEducation Longitudinal Study of 20022002P-12qsOnpsOntsOffHigh School and Beyond1980PostsecondaryqsOnpsOntsOffHigh School and Beyond1980P-12qsOnpsOntsOffHigh School Longitudinal Study of 20092009PostsecondaryqsOnpsOntsOffHigh School Longitudinal Study of 20092009P-12qsOnpsOntsOffNational Education Longitudinal Study of 19881988PostsecondaryqsOnpsOntsOffNational Education Longitudinal Study of 19881988P-12qsOnpsOntsOffNational Postsecondary Student Aid Study: 1987 Graduate Students1987PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1987 Undergraduates1987PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1990 Graduate Students1990PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1990 Undergraduates1990PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1993 Graduate Students1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1993 Undergraduates1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1996 Graduate Students1996PostsecondaryqsOffpsOntsOn2National Postsecondary Student Aid Study: 1996 Undergraduates1996PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 2000 Graduate Students2000PostsecondaryqsOffpsOntsOn2National Postsecondary Student Aid Study: 2000 Undergraduates2000PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 2004 Graduate Students2004PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2004 Undergraduates2004PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2008 Graduate Students2008PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2008 Undergraduates2008PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2012 Graduate Students2012PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2012 Undergraduates2012PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2016 Graduate Students2016PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2016 Undergraduates2016PostsecondaryqsOnpsOntsOn1National Study of Postsecondary Faculty: 1988 Faculty1988PostsecondaryqsOffpsOntsOffNational Study of Postsecondary Faculty: 1993 Faculty1993PostsecondaryqsOffpsOntsOffNational Study of Postsecondary Faculty: 1999 Faculty1999PostsecondaryqsOffpsOntsOffNational Study of Postsecondary Faculty: 2004 Faculty2004PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 2004 Institution2004PostsecondaryqsOnpsOntsOffNational Teacher and Principal Survey, 2015-16 Public School Principals2015-2016P-12qsOnpsOntsOffNational Teacher and Principal Survey, 2015-16 Public Schools2015-2016P-12qsOnpsOntsOffParent and Family Involvement in Education: 20122012P-12qsOnpsOntsOn7Parent and Family Involvement in Education: 20162016P-12qsOnpsOntsOn7Pre-Elementary Education Longitudinal Study, Waves 1-52003/2008P-12qsOnpsOntsOffPrivate School Universe Survey: 2011-122011-2012P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 1999-20001999-2000P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 2003-042003-2004P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 2005-062005-2006P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2007-082007-2008P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2009-102009-2010P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2015-162015-2016P-12qsOnpsOntsOn3Schools and Staffing Survey, Districts: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Districts: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Library Media Centers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Library Media Centers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Districts: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 1999-001999-2000P-12qsOffpsOntsOffSchool Survey on Crime and Safety: 2015-162015-2016P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2009-102009-2010P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2007-082007-2008P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2005-062005-2006P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2003-042003-2004P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 1999-20001999-2000P-12qsOnpsOntsOffPrivate School Universe Survey: 2011-122011-2012P-12qsOnpsOntsOffPre-Elementary Education Longitudinal Study, Waves 1-52003/2008P-12qsOnpsOntsOffParent and Family Involvement in Education: 20162016P-12qsOnpsOntsOn7Parent and Family Involvement in Education: 20122012P-12qsOnpsOntsOn7National Teacher and Principal Survey, 2015-16 Public Schools2015-2016P-12qsOnpsOntsOffNational Teacher and Principal Survey, 2015-16 Public School Principals2015-2016P-12qsOnpsOntsOffNational Study of Postsecondary Faculty: 2004 Institution2004PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 2004 Faculty2004PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 1999 Faculty1999PostsecondaryqsOffpsOntsOffNational Study of Postsecondary Faculty: 1993 Faculty1993PostsecondaryqsOffpsOntsOffNational Study of Postsecondary Faculty: 1988 Faculty1988PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 2016 Undergraduates2016PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2016 Graduate Students2016PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2012 Undergraduates2012PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2012 Graduate Students2012PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2008 Undergraduates2008PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2008 Graduate Students2008PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2004 Undergraduates2004PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2004 Graduate Students2004PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2000 Undergraduates2000PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 2000 Graduate Students2000PostsecondaryqsOffpsOntsOn2National Postsecondary Student Aid Study: 1996 Undergraduates1996PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 1996 Graduate Students1996PostsecondaryqsOffpsOntsOn2National Postsecondary Student Aid Study: 1993 Undergraduates1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1993 Graduate Students1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1990 Undergraduates1990PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1990 Graduate Students1990PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1987 Undergraduates1987PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1987 Graduate Students1987PostsecondaryqsOffpsOntsOffNational Education Longitudinal Study of 19881988PostsecondaryqsOnpsOntsOffNational Education Longitudinal Study of 19881988P-12qsOnpsOntsOffHigh School Longitudinal Study of 20092009PostsecondaryqsOnpsOntsOffHigh School Longitudinal Study of 20092009P-12qsOnpsOntsOffHigh School and Beyond1980PostsecondaryqsOnpsOntsOffHigh School and Beyond1980P-12qsOnpsOntsOffEducation Longitudinal Study of 20022002PostsecondaryqsOnpsOntsOffEducation Longitudinal Study of 20022002P-12qsOnpsOntsOffEarly Childhood Program Participation: 20162016P-12qsOnpsOntsOn6Early Childhood Program Participation: 20122012P-12qsOnpsOntsOn6Beginning Postsecondary Students: 2012/20172012/2017PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 2004/20092004/2009PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 1996/20011996/2001PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 1990/19941990/1994PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2016/20172016/2017PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2016/20172016/2017Adult EducationqsOnpsOntsOffBaccalaureate and Beyond: 2008/20122008/2012PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2008/20122008/2012Adult EducationqsOnpsOntsOffBaccalaureate and Beyond: 2000/20012000/2001PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 2000/20012000/2001Adult EducationqsOffpsOntsOffBaccalaureate and Beyond: 1993/2003 Graduate students1993/2003PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 1993/2003 Graduate students1993/2003Adult EducationqsOffpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003Adult EducationqsOnpsOntsOffAdult Training and Education Survey: 20162016Adult EducationqsOnpsOntsOffHigh School and Beyond1980PostsecondaryqsOnpsOntsOffHigh School and Beyond1980P-12qsOnpsOntsOffNational Postsecondary Student Aid Study: 1987 Undergraduates1987PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1987 Graduate Students1987PostsecondaryqsOffpsOntsOffNational Study of Postsecondary Faculty: 1988 Faculty1988PostsecondaryqsOffpsOntsOffNational Education Longitudinal Study of 19881988PostsecondaryqsOnpsOntsOffNational Education Longitudinal Study of 19881988P-12qsOnpsOntsOffNational Postsecondary Student Aid Study: 1990 Undergraduates1990PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1990 Graduate Students1990PostsecondaryqsOffpsOntsOffBeginning Postsecondary Students: 1990/19941990/1994PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 1993 Faculty1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1993 Undergraduates1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1993 Graduate Students1993PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 1993/2003 Graduate students1993/2003PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 1993/2003 Graduate students1993/2003Adult EducationqsOffpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003Adult EducationqsOnpsOntsOffNational Postsecondary Student Aid Study: 1996 Undergraduates1996PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 1996 Graduate Students1996PostsecondaryqsOffpsOntsOn2Beginning Postsecondary Students: 1996/20011996/2001PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 1999 Faculty1999PostsecondaryqsOffpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private Schools: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Library Media Centers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Districts: 1999-001999-2000P-12qsOffpsOntsOffSchool Survey on Crime and Safety: 1999-20001999-2000P-12qsOnpsOntsOffNational Postsecondary Student Aid Study: 2000 Undergraduates2000PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 2000 Graduate Students2000PostsecondaryqsOffpsOntsOn2Baccalaureate and Beyond: 2000/20012000/2001PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 2000/20012000/2001Adult EducationqsOffpsOntsOffEducation Longitudinal Study of 20022002PostsecondaryqsOnpsOntsOffEducation Longitudinal Study of 20022002P-12qsOnpsOntsOffPre-Elementary Education Longitudinal Study, Waves 1-52003/2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2003-042003-2004P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 2003-042003-2004P-12qsOnpsOntsOffNational Study of Postsecondary Faculty: 2004 Institution2004PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 2004 Faculty2004PostsecondaryqsOnpsOntsOffNational Postsecondary Student Aid Study: 2004 Undergraduates2004PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2004 Graduate Students2004PostsecondaryqsOnpsOntsOn2Beginning Postsecondary Students: 2004/20092004/2009PostsecondaryqsOnpsOntsOffSchool Survey on Crime and Safety: 2005-062005-2006P-12qsOnpsOntsOn3Schools and Staffing Survey, Public and Private Teachers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2007-082007-2008P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 2007-082007-2008P-12qsOnpsOntsOn3National Postsecondary Student Aid Study: 2008 Undergraduates2008PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2008 Graduate Students2008PostsecondaryqsOnpsOntsOn2Baccalaureate and Beyond: 2008/20122008/2012PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2008/20122008/2012Adult EducationqsOnpsOntsOffHigh School Longitudinal Study of 20092009PostsecondaryqsOnpsOntsOffHigh School Longitudinal Study of 20092009P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 2009-102009-2010P-12qsOnpsOntsOn3Schools and Staffing Survey, Public and Private Teachers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2011-122011-2012P-12qsOnpsOntsOffPrivate School Universe Survey: 2011-122011-2012P-12qsOnpsOntsOffParent and Family Involvement in Education: 20122012P-12qsOnpsOntsOn7National Postsecondary Student Aid Study: 2012 Undergraduates2012PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2012 Graduate Students2012PostsecondaryqsOnpsOntsOn2Early Childhood Program Participation: 20122012P-12qsOnpsOntsOn6Beginning Postsecondary Students: 2012/20172012/2017PostsecondaryqsOnpsOntsOffSchool Survey on Crime and Safety: 2015-162015-2016P-12qsOnpsOntsOn3National Teacher and Principal Survey, 2015-16 Public Schools2015-2016P-12qsOnpsOntsOffNational Teacher and Principal Survey, 2015-16 Public School Principals2015-2016P-12qsOnpsOntsOffParent and Family Involvement in Education: 20162016P-12qsOnpsOntsOn7National Postsecondary Student Aid Study: 2016 Undergraduates2016PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2016 Graduate Students2016PostsecondaryqsOnpsOntsOn2Early Childhood Program Participation: 20162016P-12qsOnpsOntsOn6Adult Training and Education Survey: 20162016Adult EducationqsOnpsOntsOffBaccalaureate and Beyond: 2016/20172016/2017PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2016/20172016/2017Adult EducationqsOnpsOntsOffBaccalaureate and Beyond: 2016/20172016/2017PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2016/20172016/2017Adult EducationqsOnpsOntsOffParent and Family Involvement in Education: 20162016P-12qsOnpsOntsOn7National Postsecondary Student Aid Study: 2016 Undergraduates2016PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2016 Graduate Students2016PostsecondaryqsOnpsOntsOn2Early Childhood Program Participation: 20162016P-12qsOnpsOntsOn6Adult Training and Education Survey: 20162016Adult EducationqsOnpsOntsOffSchool Survey on Crime and Safety: 2015-162015-2016P-12qsOnpsOntsOn3National Teacher and Principal Survey, 2015-16 Public Schools2015-2016P-12qsOnpsOntsOffNational Teacher and Principal Survey, 2015-16 Public School Principals2015-2016P-12qsOnpsOntsOffBeginning Postsecondary Students: 2012/20172012/2017PostsecondaryqsOnpsOntsOffParent and Family Involvement in Education: 20122012P-12qsOnpsOntsOn7National Postsecondary Student Aid Study: 2012 Undergraduates2012PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2012 Graduate Students2012PostsecondaryqsOnpsOntsOn2Early Childhood Program Participation: 20122012P-12qsOnpsOntsOn6Schools and Staffing Survey, Public and Private Teachers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2011-122011-2012P-12qsOnpsOntsOffPrivate School Universe Survey: 2011-122011-2012P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 2009-102009-2010P-12qsOnpsOntsOn3High School Longitudinal Study of 20092009PostsecondaryqsOnpsOntsOffHigh School Longitudinal Study of 20092009P-12qsOnpsOntsOffBaccalaureate and Beyond: 2008/20122008/2012PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2008/20122008/2012Adult EducationqsOnpsOntsOffNational Postsecondary Student Aid Study: 2008 Undergraduates2008PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2008 Graduate Students2008PostsecondaryqsOnpsOntsOn2Schools and Staffing Survey, Public and Private Teachers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2007-082007-2008P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 2007-082007-2008P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2005-062005-2006P-12qsOnpsOntsOn3Beginning Postsecondary Students: 2004/20092004/2009PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 2004 Institution2004PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 2004 Faculty2004PostsecondaryqsOnpsOntsOffNational Postsecondary Student Aid Study: 2004 Undergraduates2004PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2004 Graduate Students2004PostsecondaryqsOnpsOntsOn2Schools and Staffing Survey, Public and Private Teachers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2003-042003-2004P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 2003-042003-2004P-12qsOnpsOntsOffPre-Elementary Education Longitudinal Study, Waves 1-52003/2008P-12qsOnpsOntsOffEducation Longitudinal Study of 20022002PostsecondaryqsOnpsOntsOffEducation Longitudinal Study of 20022002P-12qsOnpsOntsOffBaccalaureate and Beyond: 2000/20012000/2001PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 2000/20012000/2001Adult EducationqsOffpsOntsOffNational Postsecondary Student Aid Study: 2000 Undergraduates2000PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 2000 Graduate Students2000PostsecondaryqsOffpsOntsOn2Schools and Staffing Survey, Public and Private Teachers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private Schools: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Library Media Centers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Districts: 1999-001999-2000P-12qsOffpsOntsOffSchool Survey on Crime and Safety: 1999-20001999-2000P-12qsOnpsOntsOffNational Study of Postsecondary Faculty: 1999 Faculty1999PostsecondaryqsOffpsOntsOffBeginning Postsecondary Students: 1996/20011996/2001PostsecondaryqsOnpsOntsOffNational Postsecondary Student Aid Study: 1996 Undergraduates1996PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 1996 Graduate Students1996PostsecondaryqsOffpsOntsOn2Baccalaureate and Beyond: 1993/2003 Graduate students1993/2003PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 1993/2003 Graduate students1993/2003Adult EducationqsOffpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003Adult EducationqsOnpsOntsOffNational Study of Postsecondary Faculty: 1993 Faculty1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1993 Undergraduates1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1993 Graduate Students1993PostsecondaryqsOffpsOntsOffBeginning Postsecondary Students: 1990/19941990/1994PostsecondaryqsOnpsOntsOffNational Postsecondary Student Aid Study: 1990 Undergraduates1990PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1990 Graduate Students1990PostsecondaryqsOffpsOntsOffNational Study of Postsecondary Faculty: 1988 Faculty1988PostsecondaryqsOffpsOntsOffNational Education Longitudinal Study of 19881988PostsecondaryqsOnpsOntsOffNational Education Longitudinal Study of 19881988P-12qsOnpsOntsOffNational Postsecondary Student Aid Study: 1987 Undergraduates1987PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1987 Graduate Students1987PostsecondaryqsOffpsOntsOffHigh School and Beyond1980PostsecondaryqsOnpsOntsOffHigh School and Beyond1980P-12qsOnpsOntsOffBaccalaureate and Beyond: 2016/20172016/2017Adult EducationqsOnpsOntsOffBaccalaureate and Beyond: 2008/20122008/2012Adult EducationqsOnpsOntsOffBaccalaureate and Beyond: 2000/20012000/2001Adult EducationqsOffpsOntsOffBaccalaureate and Beyond: 1993/2003 Graduate students1993/2003Adult EducationqsOffpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003Adult EducationqsOnpsOntsOffAdult Training and Education Survey: 20162016Adult EducationqsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Library Media Centers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Districts: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 1999-001999-2000P-12qsOffpsOntsOffSchool Survey on Crime and Safety: 2015-162015-2016P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2009-102009-2010P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2007-082007-2008P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2005-062005-2006P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2003-042003-2004P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 1999-20001999-2000P-12qsOnpsOntsOffPrivate School Universe Survey: 2011-122011-2012P-12qsOnpsOntsOffPre-Elementary Education Longitudinal Study, Waves 1-52003/2008P-12qsOnpsOntsOffParent and Family Involvement in Education: 20162016P-12qsOnpsOntsOn7Parent and Family Involvement in Education: 20122012P-12qsOnpsOntsOn7National Teacher and Principal Survey, 2015-16 Public Schools2015-2016P-12qsOnpsOntsOffNational Teacher and Principal Survey, 2015-16 Public School Principals2015-2016P-12qsOnpsOntsOffNational Education Longitudinal Study of 19881988P-12qsOnpsOntsOffHigh School Longitudinal Study of 20092009P-12qsOnpsOntsOffHigh School and Beyond1980P-12qsOnpsOntsOffEducation Longitudinal Study of 20022002P-12qsOnpsOntsOffEarly Childhood Program Participation: 20162016P-12qsOnpsOntsOn6Early Childhood Program Participation: 20122012P-12qsOnpsOntsOn6National Study of Postsecondary Faculty: 2004 Institution2004PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 2004 Faculty2004PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 1999 Faculty1999PostsecondaryqsOffpsOntsOffNational Study of Postsecondary Faculty: 1993 Faculty1993PostsecondaryqsOffpsOntsOffNational Study of Postsecondary Faculty: 1988 Faculty1988PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 2016 Undergraduates2016PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2016 Graduate Students2016PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2012 Undergraduates2012PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2012 Graduate Students2012PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2008 Undergraduates2008PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2008 Graduate Students2008PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2004 Undergraduates2004PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2004 Graduate Students2004PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2000 Undergraduates2000PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 2000 Graduate Students2000PostsecondaryqsOffpsOntsOn2National Postsecondary Student Aid Study: 1996 Undergraduates1996PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 1996 Graduate Students1996PostsecondaryqsOffpsOntsOn2National Postsecondary Student Aid Study: 1993 Undergraduates1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1993 Graduate Students1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1990 Undergraduates1990PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1990 Graduate Students1990PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1987 Undergraduates1987PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1987 Graduate Students1987PostsecondaryqsOffpsOntsOffNational Education Longitudinal Study of 19881988PostsecondaryqsOnpsOntsOffHigh School Longitudinal Study of 20092009PostsecondaryqsOnpsOntsOffHigh School and Beyond1980PostsecondaryqsOnpsOntsOffEducation Longitudinal Study of 20022002PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 2012/20172012/2017PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 2004/20092004/2009PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 1996/20011996/2001PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 1990/19941990/1994PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2016/20172016/2017PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2008/20122008/2012PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2000/20012000/2001PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 1993/2003 Graduate students1993/2003PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 2004 Institution2004PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 2004 Faculty2004PostsecondaryqsOnpsOntsOffNational Study of Postsecondary Faculty: 1999 Faculty1999PostsecondaryqsOffpsOntsOffNational Study of Postsecondary Faculty: 1993 Faculty1993PostsecondaryqsOffpsOntsOffNational Study of Postsecondary Faculty: 1988 Faculty1988PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 2016 Undergraduates2016PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2016 Graduate Students2016PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2012 Undergraduates2012PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2012 Graduate Students2012PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2008 Undergraduates2008PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2008 Graduate Students2008PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2004 Undergraduates2004PostsecondaryqsOnpsOntsOn1National Postsecondary Student Aid Study: 2004 Graduate Students2004PostsecondaryqsOnpsOntsOn2National Postsecondary Student Aid Study: 2000 Undergraduates2000PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 2000 Graduate Students2000PostsecondaryqsOffpsOntsOn2National Postsecondary Student Aid Study: 1996 Undergraduates1996PostsecondaryqsOffpsOntsOn1National Postsecondary Student Aid Study: 1996 Graduate Students1996PostsecondaryqsOffpsOntsOn2National Postsecondary Student Aid Study: 1993 Undergraduates1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1993 Graduate Students1993PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1990 Undergraduates1990PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1990 Graduate Students1990PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1987 Undergraduates1987PostsecondaryqsOffpsOntsOffNational Postsecondary Student Aid Study: 1987 Graduate Students1987PostsecondaryqsOffpsOntsOffNational Education Longitudinal Study of 19881988PostsecondaryqsOnpsOntsOffHigh School Longitudinal Study of 20092009PostsecondaryqsOnpsOntsOffHigh School and Beyond1980PostsecondaryqsOnpsOntsOffEducation Longitudinal Study of 20022002PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 2012/20172012/2017PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 2004/20092004/2009PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 1996/20011996/2001PostsecondaryqsOnpsOntsOffBeginning Postsecondary Students: 1990/19941990/1994PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2016/20172016/2017PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2008/20122008/2012PostsecondaryqsOnpsOntsOffBaccalaureate and Beyond: 2000/20012000/2001PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 1993/2003 Graduate students1993/2003PostsecondaryqsOffpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003PostsecondaryqsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Teachers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private Schools: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Public and Private School Principals: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Library Media Centers: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Library Media Centers: 1999-001999-2000P-12qsOffpsOntsOffSchools and Staffing Survey, Districts: 2011-122011-2012P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2007-082007-2008P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 2003-042003-2004P-12qsOnpsOntsOffSchools and Staffing Survey, Districts: 1999-001999-2000P-12qsOffpsOntsOffSchool Survey on Crime and Safety: 2015-162015-2016P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2009-102009-2010P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2007-082007-2008P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2005-062005-2006P-12qsOnpsOntsOn3School Survey on Crime and Safety: 2003-042003-2004P-12qsOnpsOntsOffSchool Survey on Crime and Safety: 1999-20001999-2000P-12qsOnpsOntsOffPrivate School Universe Survey: 2011-122011-2012P-12qsOnpsOntsOffPre-Elementary Education Longitudinal Study, Waves 1-52003/2008P-12qsOnpsOntsOffParent and Family Involvement in Education: 20162016P-12qsOnpsOntsOn7Parent and Family Involvement in Education: 20122012P-12qsOnpsOntsOn7National Teacher and Principal Survey, 2015-16 Public Schools2015-2016P-12qsOnpsOntsOffNational Teacher and Principal Survey, 2015-16 Public School Principals2015-2016P-12qsOnpsOntsOffNational Education Longitudinal Study of 19881988P-12qsOnpsOntsOffHigh School Longitudinal Study of 20092009P-12qsOnpsOntsOffHigh School and Beyond1980P-12qsOnpsOntsOffEducation Longitudinal Study of 20022002P-12qsOnpsOntsOffEarly Childhood Program Participation: 20162016P-12qsOnpsOntsOn6Early Childhood Program Participation: 20122012P-12qsOnpsOntsOn6Baccalaureate and Beyond: 2016/20172016/2017Adult EducationqsOnpsOntsOffBaccalaureate and Beyond: 2008/20122008/2012Adult EducationqsOnpsOntsOffBaccalaureate and Beyond: 2000/20012000/2001Adult EducationqsOffpsOntsOffBaccalaureate and Beyond: 1993/2003 Graduate students1993/2003Adult EducationqsOffpsOntsOffBaccalaureate and Beyond: 1993/20031993/2003Adult EducationqsOnpsOntsOffAdult Training and Education Survey: 20162016Adult EducationqsOnpsOntsOff1 Percentage distribution of 1995–96 beginning postsecondary students' highest degree attained by 2001, by work status Highest degree completed as of June 2001 Certificate(%) Associate(%) Bachelor(%) Never attained(%) Total Estimates Total 11.7 9.8 29.8 48.6 100% Job 1995–96: hours worked per week while enrolled Did not work while enrolled 14.0 9.8 38.5 37.8 100% Worked part time 8.9 11.6 35.5 44.0 100% Worked full time 14.5 7.2 8.3 69.9 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01). Computation by NCES QuickStats on 6/22/2009 ckeakb72 Percentage distribution of 1995–96 beginning postsecondary students' highest degree attained by 2001, by number of advanced placement tests taken Persistence and completion at any institution as of 2000-01 Never attained(%) Certificate(%) Associate(%) Bachelor(%) Total Estimates Total 48.6 11.7 9.9 29.8 100% Number of Advanced Placement tests taken 0 51.1 7.7 12.1 29.1 100% 1 38.1 2.6 6.0 53.4 100% 2 33.6 0.4 3.4 62.6 100% Three or more 13.8 0.1 1.4 84.8 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01). Computation by NCES QuickStats on 6/22/2009 ckeak193 Percentage of beginning postsecondary students who received Pell grants, by race/ethnicity: 1995–96 Pell Grant amount 1995-96(%>0) Estimates Total 26.4 Race/ethnicity White, non-Hispanic 19.0 Black, non-Hispanic 49.3 Hispanic 42.4 Asian/Pacific Islander 35.5 American Indian/Alaska Native 33.2 Other ‡ ‡ Reporting standards not met. Source: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01). Computation by NCES QuickStats on 3/10/2009 cgfak7e4 Percentage distribution of 1995–96 beginning postsecondary students' grade point average (GPA) through 2001, by income percentile rank Cumulative Grade Point Average (GPA) as of 2001 Mostly A’s (%) A’s and B’s (%) Mostly B’s (%) B’s and C’s (%) Mostly C’s (%) C’s and D’s (%) Mostly D’s or below (%) Total Estimates Total 13.3 31.8 35.3 14.4 4.4 0.7 0.1 100% Income percentile rank 1994 1-25 13.1 28.2 37.8 14.7 4.7 1.4 0.2 100% 26-50 13.5 30.2 37.3 12.8 5.8 0.3 0.2 100% 51-75 12.9 36.1 33.1 14.0 3.4 0.4 0.. 100% More than 75 13.7 32.7 33.0 16.3 3.7 0.7 0.0 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01). Computation by NCES QuickStats on 6/22/2009 ckeak03 5 Percentage distribution of 1995–96 beginning postsecondary students' persistence at any institution through 2001, by gender Persistence at any institution through 2001 Attained, still enrolled(%) Attained, not enrolled(%) Never attained, still enrolled(%) Never attained, not enrolled(%) Total Estimates Total 5.9 45.5 14.9 33.7 100% Gender Male 5.9 41.8 15.8 36.5 100% Female 5.8 48.5 14.2 31.5 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 1995–96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01). Computation by NCES QuickStats on 6/22/2009 cgeakd41 Percent of graduate students who borrowed, by type of graduate program: 2003–04 Loans: total student loans all sources(%>0) Estimates Total 40.0 Graduate study: program Business administration (MBA) 39.1 Education (any master's) 34.8 Other master of arts (MA) 41.3 Other master of science (MS) 31.8 Other master's degree 49.3 PhD except in education 19.9 Education (any doctorate) 27.1 Other doctoral degree 49.5 Medicine (MD) 77.3 Other health science degree 81.7 Law (LLB or JD) 81.0 Theology (MDiv, MHL, BD) 30.0 Post-baccalaureate certificate 30.1 Not in a degree program 28.0 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009 bbfak2a2 Percentage of graduate students with assistantships, by graduate field of study: 2003–04 Assistantships(%>0) Estimates Total 15.3 Graduate study: major field Humanities 20.8 Social/behavioral sciences 31.7 Life sciences 47.4 Math/Engineering/Computer science 37.9 Education 7.6 Business/management 7.9 Health 10.3 Law 5.8 Others 23.8 Undeclared or not in a degree program 5.4 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009 ckeak393 Percentage distribution of graduates students' student/employee role, by graduate field of study: 2003–04 Work: primarily student or employee A student working to meet expenses(%) An employee enrolled in school(%) No job(%) Total Estimates Total 35.8 45.1 19.1 100% Graduate study: major field Humanities 44.9 35.9 19.2 100% Social/behavioral sciences 58.9 24.6 16.5 100% Life sciences 61.0 20.7 18.3 100% Math/Engineering/Computer science 47.4 38.3 14.3 100% Education 26.3 63.3 10.4 100% Business/management 24.8 61.8 13.3 100% Health 39.4 19.0 41.6 100% Law 39.6 11.6 48.8 100% Others 47.0 38.5 14.5 100% Undeclared or not in a degree program 20.5 67.3 12.2 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009 ckeakce4 Percentage of graduate students who have ever borrowed loans, by institution type: 2003–04 Total loan debt (cumulative)(%>0) Estimates Total 65.2 Type of 4-year institution Public 4-year nondoctorate 61.4 Public 4-year doctorate 60.6 Private not-for-profit 4-yr nondoctorate 61.6 Private not-for-profit 4-year doctorate 71.3 Private for-profit 4-year 85.9 Attended more than one institution 68.9 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009 ckeak5f5 Average loan amount for graduate students, by parents' education, 2003–04 Loans: total student loans all sources(Mean[0]) Estimates Total 6,302.0 Parent's highest education Do not know parent's education level 7,677.5 High school diploma or less 5,878.7 Some college 6,016.3 Bachelor's degree 5,794.3 Master's degree or higher 7,185.9 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009 ckeakef1 Percentage of undergraduate students who applied for aid, by parents' income: 2003–04 Aid: applied for federal aid Yes(%) No(%) Total Estimates Total 58.3 41.7 100% Income: dependent student household income Less than $32,000 78.7 21.3 100% $32,000-59,999 66.6 33.4 100% $60,000-91,999 56.9 43.1 100% $92,000 or more 47.1 52.9 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009 cgeak8c2 Percentage distribution of undergraduates' cumulative grade point average (GPA) categories, by major field of study: 2003–04 Cumulative Grade Point Average (GPA) as of 2003-04 Less than 2.75(%) 2.75 - 3.74(%) 3.75 or higher(%) Total Estimates Total 34.4 49.0 16.7 100% College study: major Humanities 35.9 50.4 13.6 100% Social/behavioral sciences 35.0 52.1 12.8 100% Life sciences 34.9 52.7 12.4 100% Physical sciences 31.5 54.3 14.2 100% Math 29.1 55.3 15.6 100% Computer/information science 34.0 48.1 17.9 100% Engineering 37.4 48.1 14.5 100% Education 31.9 52.6 15.5 100% Business/management 35.6 49.3 15.1 100% Health 32.2 50.7 17.0 100% Vocational/technical 33.3 47.1 19.6 100% Other technical/professional 36.7 49.9 13.4 100% Undeclared or not in a degree program 33.2 44.1 22.8 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009 cgeake93 Mean net price of attendance for undergraduate students, by type of institution: 2003–04 Net price after all aid(Mean[0]) Estimates Total 6,656.0 Institution: type Public less-than-2-year 5,616.5 Public 2-year 4,716.3 Public 4-year nondoctorate 6,253.5 Public 4-year doctorate 7,564.1 Private not-for-profit less-than-4-year 7,382.3 Private not-for-profit 4-yr nondoctorate 9,208.7 Private not-for-profit 4-year doctorate 14,812.2 Private for-profit less-than-2-year 7,842.9 Private for-profit 2 years or more 6,737.6 Attended more than one institution ‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009 bcfak0c4 Percentage distribution of undergraduates' parents' highest level of education, by type of institution: 2003–04 Parent's highest education High school or less(%) Some college(%) Bachelor's degree or higher(%) Total Estimates Total 37.1 21.6 41.3 100% Institution: type Public less-than-2-year 54.2 17.4 28.4 100% Public 2-year 43.3 23.9 32.7 100% Public 4-year nondoctorate 28.7 20.5 50.8 100% Public 4-year doctorate 46.9 18.8 34.2 100% Private not-for-profit less than 4-year 29.6 18.1 52.3 100% Private not-for-profit 4-year nondoctorate 55.6 17.4 27.0 100% Private not-for-profit 4-year doctorate 53.8 20.2 25.9 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 10/14/2009 cgeakd55 Average amount of Pell grants received by undergraduates, by income and dependency status: 2003–04 Grants: Pell Grants(Avg>0) Estimates Total 2,449.7 Income: categories by dependency status Dependent: Less than $10,000 3,242.2 Dependent: $10,000-$19,999 3,176.1 Dependent: $20,000-$29,999 2,715.0 Dependent: $30,000-$39,999 1,958.3 Dependent: $40,000-$49,999 1,508.6 Dependent: $50,000-$59,999 1,309.0 Dependent: $60,000-$69,999 1,241.7 Dependent: $70,000-$79,999 1,404.4 Dependent: $80,000-$99,999 ‡ Dependent: $100,000 or more ‡ Independent: Less than $5,000 2,860.3 Independent: $5,000-$9,999 2,642.9 Independent: $10,000-$19,999 2,291.7 Independent: $20,000-$29,999 2,328.3 Independent: $30,000-$49,999 1,561.9 Independent: $50,000 or more 1,124.3 ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES QuickStats on 8/25/2009 cgeak381 Percentage distribution of instructional faculty and staff's employment status, by institution type, Fall 2003 Employment status at this job Full time(%) Part time(%) Total Estimates Total 56.3 43.7 100% Institution: type and control Public doctoral 77.8 22.2 100% Private not-for-profit doctoral 68.7 31.3 100% Public master's 63.3 36.7 100% Private not-for-profit master's 45.0 55.0 100% Private not-for-profit baccalaureate 63.2 36.8 100% Public associates 33.3 66.7 100% Other 49.2 50.8 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES QuickStats on 6/19/2009 ckeak012 Percentage distribution of full-time instructional faculty and staff, by race/ethnicity, institution type: Fall 2003 Race/ethnicity White, non-Hispanic(%) Black, non-Hispanic(%) Asian/Pacific Islander(%) Hispanic(%) Other(%) Estimates Total 80.3 5.9 8.6 3.4 1.2 Institution: type and control Public doctoral 79.4 4.5 12.0 3.0 1.0 Private not-for-profit doctoral 79.1 5.3 11.9 2.9 0.8 Public master’s 78.3 8.9 7.6 3.6 1.6 Private not-for-profit master’s 85.4 5.1 5.7 2.5 1.3 Private not-for-profit baccalaureate 85.8 6.8 4.2 2.2 1.1 Public associates 81.2 7.2 4.4 5.5 1.7 Other 86.9 4.6 5.8 1.7 1.0 NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES QuickStats on 6/19/2009 ckeak043 Percentage distribution of full-time instructional faculty and staff, by tenure status, institution type: Fall 2003 Tenure status Tenured(%) On tenure track but not tenured(%) Not on tenure track(%) Not tenured-no tenure system(%) Total Estimates Total 49.3 21.3 20.9 8.5 100% Institution: type and control Public doctoral 53.0 20.4 25.9 0.7 100% Private not-for-profit doctoral 47.1 19.6 28.8 4.5 100% Public master’s 53.7 28.3 16.9 1.0 100% Private not-for-profit master’s 41.9 28.1 21.5 8.6 100% Private not-for-profit baccalaureate 42.9 25.1 21.6 10.4 100% Public associates 49.1 15.6 9.3 26.0 100% Other 39.4 17.3 18.7 24.6 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES QuickStats on 6/19/2009 ckeak984 Percentage distribution of part-time instructional faculty and staff, by academic rank, institution type: Fall 2003 Academic rank Professor(%) Associate professor(%) Assistant professor(%) Instructor or lecturer(%) Other ranks/not applicable(%) Estimates Total 4.6 2.9 3.4 42.2 46.9 Institution: type and control Public doctoral 6.3 4.5 8.1 45.0 36.0 Private not-for-profit doctoral 5.6 4.9 9.1 31.9 48.5 Public master’s 6.4 2.3 2.0 40.7 48.7 Private not-for-profit master’s 2.7 3.4 2.6 30.3 60.9 Private not-for-profit baccalaureate 4.6 4.2 5.4 32.5 53.3 Public associates 3.4 1.5 1.0 49.5 44.6 Other 7.1 4.9 5.2 33.3 49.4 NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES QuickStats on 6/19/2009 ckeakff5 Average hours worked per week among full-time instructional faculty and staff, by tenure status: Fall 2003 Hours worked per week(Mean>0) Estimates Total 47.4 Tenure status Tenured 53.3 On tenure track but not tenured 53.7 Not on tenure track 43.0 Not tenured-no tenure system 45.4 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES QuickStats on 6/19/2009 bkfakf31 Percentage of institutions with full- or part-time faculty represented by a union, by institution type: Fall 2003 Faculty represented by a union Not represented by a union(%) Represented by a union(%) Total Estimates Total 68.1 31.9 100% Institution: type and control Public doctoral 69.1 30.9 100% Private not-for-profit doctoral 94.4 5.6 100% Public master’s 58.1 41.9 100% Private not-for-profit master’s 87.6 12.4 100% Private not-for-profit baccalaureate 86.7 13.3 100% Public associate’s 42.4 57.6 100% Other 78.3 21.7 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES QuickStats on 6/19/2009 ckeak162 Among institutions with a tenure system, average percentage of undergraduate student credit hours assigned to full-time faculty and instructional staff, by institution type: Fall 2003 Undergraduate instruction: percent full-time faculty(Mean[0]) Estimates Total 70.8 Institution: type and control Public doctoral 68.6 Private not-for-profit doctoral 71.6 Public master’s 75.7 Private not-for-profit master’s 68.8 Private not-for-profit baccalaureate 76.1 Public associate’s 58.7 Other 82.8 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES QuickStats on 6/19/2009 ckeaka03 Percentage of institutions who have downsized tenured faculty, by institution type: Fall 2003 Downsized tenured faculty No(%) Yes(%) Total Estimates Total 85.7 14.3 100% Institution: type and control Public doctoral 83.4 16.6 100% Private not-for-profit doctoral 93.9 6.1 100% Public master’s 90.7 9.3 100% Private not-for-profit master’s 99.6 0.4 100% Private not-for-profit baccalaureate 88.1 11.9 100% Public associate’s 87.7 12.3 100% Other 68.0 32.0 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES QuickStats on 6/19/2009 ckeak5c4 Percentage distribution of the maximum number of years full-time faculty and instructional staff can be on a tenure track without receiving tenure, by institution type: Fall 2003 Maximum years on tenure track No maximum(%) Less than 5 years(%) 5 years(%) 6 years(%) 7 years(%) More than 7 years(%) Total Estimates Total 17.5 17.4 8.5 27.0 26.0 3.6 100% Institution: type and control Public doctoral 7.5 0.0 1.1 37.3 45.9 8.2 100% Private not-for-profit doctoral 11.4 0.0 2.8 32.0 34.4 19.4 100% Public master’s 1.5 0.0 22.0 37.1 38.9 0.6 100% Private not-for-profit master’s 16.8 0.0 7.1 40.5 27.4 8.2 100% Private not-for-profit baccalaureate 9.9 0.7 0.0 53.5 32.2 3.7 100% Public associate’s 15.6 44.6 16.9 8.2 13.7 1.1 100% Other 41.9 27.1 1.9 10.3 18.5 0.2 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES QuickStats on 6/19/2009 ckeak135 Percentage of institutions in which over half of student instruction hours are assigned to part-time faculty, by institution type: Fall 2003 Undergraduate instruction: percent part-time faculty(%>50) Estimates Total 17.9 Institution: type and control Public doctoral 0.6 Private not-for-profit doctoral 9.9 Public master’s 1.6 Private not-for-profit master’s 15.6 Private not-for-profit baccalaureate 11.1 Public associate’s 23.9 Other 26.0 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES QuickStats on 6/19/2009 ckeakf71 Percentage distribution of 1992–93 bachelor's degree recipients' time-to-degree in years, by major field of study: 2003 Number of months to bachelor’s degree Within 4 years(%) 4–5 years(%) 5–6 years(%) 6–10 years(%) More than 10 years(%) Total Estimates Total 35.5 27.4 11.4 11.7 14.0 100% Undergraduate major Business and management 32.6 26.9 8.7 13.3 18.6 100% Education 32.9 30.4 10.7 11.0 15.0 100% Engineering 25.3 37.4 15.9 11.4 10.0 100% Health professions 22.0 27.3 13.5 14.2 23.1 100% Public affairs/social services 28.3 29.7 11.9 13.2 17.0 100% Biological sciences 53.5 21.7 10.9 8.4 5.5 100% Mathematics & science 38.9 24.9 11.7 11.2 13.3 100% Social science 47.5 25.3 11.4 10.2 5.6 100% History 40.1 26.3 20.0 5.3 8.3 100% Humanities 39.8 21.4 12.8 12.1 13.8 100% Psychology 39.8 26.1 7.3 12.0 14.8 100% Other 35.4 28.7 12.4 11.3 12.2 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 1993/03 Baccalaureate and Beyond Longitudinal Study (B&B:93/03). Computation by QuickStats on 6/24/2009 cgeak2a2 Percentage distribution of 1992–93 bachelor's degree receipient’s highest graduate degree attainment, by age at which student received bachelor's degree: 2003 Highest degree completed as of 2003 Bachelor’s degree(%) Master’s degree(%) First-professional degree(%) Doctoral degree(%) Total Estimates Total 73.8 20.2 4.0 2.0 100% Age when received bachelor's degree 22 or younger 65.5 24.6 6.7 3.1 100% 23–24 80.9 15.4 2.3 1.3 100% 25–29 85.0 13.7 0.6 0.7 100% 30 or older 78.5 19.4 1.3 0.8 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 1993/03 Baccalaureate and Beyond Longitudinal Study (B&B:93/03). Computation by QuickStats on 6/16/2009 ckeak4e3 Average annual salary among 1992–93 bachelor's degree recipients, by highest degree attained: 2003 Job 2003: annual salary(Mean[0]) Estimates Total 55,407.6 Highest degree attained by 2003 Bachelor’s degree 53,547.5 Master’s degree 56,241.6 First-professional degree 83,798.6 Doctoral degree 63,214.4 SOURCE: U.S. Department of Education, National Center for Education Statistics, 1993/03 Baccalaureate and Beyond Longitudinal Study (B&B:93/03). Computation by QuickStats on 6/16/2009 ckeak844 Percentage of 1992–93 bachelor degree recipients who were still paying undergraduate education loans, by occupation: 2003 Undergraduate loans: total owed as of 2003 (%>1) Estimates Total 16.6 Job 2003: occupation Educators 22.0 Business and management 12.7 Engineering/architecture 12.6 Computer science 10.8 Medical professionals 20.7 Editors/writers/performers 15.1 Human/protective service/legal profess 24.0 Research, scientists, technical 14.1 Administrative/clerical/legal support 24.7 Mechanics, laborers 18.1 Service industries 13.0 Other, military 13.6 SOURCE: U.S. Department of Education, National Center for Education Statistics, 1993/03 Baccalaureate and Beyond Longitudinal Study (B&B:93/03). Computation by QuickStats on 6/16/2009 bffak6c 5 Percentage distribution of 1992–93 bachelor's degree recipients' teaching status, by highest degree attained: 2003 Teaching status in 2003 Currently teaching (%) Left teaching (%) Never taught (%) Total Estimates Total 10.6 9.3 80.2 100% Highest degree completed as of 2003 Bachelor's degree 8.2 8.2 83.6 100% Master's degree 20.1 13.3 66.6 100% First-professional degree 0.6 4.9 94.5 100% Doctoral degree 1.0 9.8 89.2 100% NOTE: Rows may not add up to 100% due to rounding SOURCE: U.S. Department of Education, National Center for Education Statistics, 1993/03 Baccalaureate and Beyond Longitudinal Study (B&B:93/03). Computation by QuickStats on 6/16/2009 ckeake6123451 Disability as reported by teacher (parent if teacher data missing), by Child's race. Disability as reported by teacher (parent if teacher data missing), Wave 1 Autism(%) Learning Disability(%) Mental Retardation(%) Speech Or Language Impairment(%) Other impairment(%) Estimates Total7.2 2.4 4.3 47.1 39.0 Child's race Hispanic10.3 3.5 7.1 41.7 37.5 Black Or African American/Non-Hispanic9.7 4.7 6.6 35.1 43.9 White/Non-Hispanic5.7 1.6 2.9 51.4 38.4 NOTE: Rows may not add up to 100% due to rounding.SOURCE: Pre-Elementary Education Longitudinal Study (PEELS), Waves 1-5Computation by NCES QuickStats on 8/23/2011 cchbbf12 Child's main education setting, by Household income Child's main education setting, Wave 1 Regular Education Classroom(%) Special Education Setting(%) Home(%) Other Specify(%) Total Estimates Total74.4 21.2 2.7 1.7 100% Household income, Wave 1 $20,000 Or Less72.2 18.3 8.2 1.3 100% $20,001 - 40,00068.6 27.0 0.0 4.4 100% > $40,00080.3 19.0 0.6 0.0 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: Pre-Elementary Education Longitudinal Study (PEELS), Waves 1-5Computation by NCES QuickStats on 8/23/2011 cchbbf23 Overall academic skills (kindergarten), by District poverty/wealth category. Overall academic skills (kindergarten), Wave 1 Far Below Average(%) Below Average(%) Average(%) Above Average(%) Far Above Average(%) Total Estimates Total17.9 32.7 35.8 12.7 0.9 100% District poverty/wealth category High Wealth22.1 30.1 41.6 6.2 0.0 100% Medium Wealth9.0 27.7 53.1 8.6 1.6 100% Low Wealth22.8 32.7 23.8 20.7 0.0 100% Very Low Wealth18.9 41.2 24.3 13.5 2.1 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: Pre-Elementary Education Longitudinal Study (PEELS), Waves 1-5Computation by NCES QuickStats on 8/23/2011 cchbbf34 First professional license/certificate, 1 by Years teacher working with children with disabilities First professional license/certificate, Wave 1 Child Development(%) Early Childhood Education(%) Early Childhood Special Education(%) Special Education(%) Other(%) Estimates Total8.8 20.8 18.0 22.8 29.6 Years teacher working with children with disabilities, Wave 1 Less than 5 years9.4 27.9 17.4 14.3 31.0 6 - 10 years10.9 22.4 12.1 22.2 32.4 11 - 15 years11.1 28.0 19.8 18.0 23.2 15 years or more5.5 10.4 22.4 32.3 29.4 NOTE: Rows may not add up to 100% due to rounding.SOURCE: Pre-Elementary Education Longitudinal Study (PEELS), Waves 1-5Computation by NCES QuickStats on 8/23/2011 cchbbf45 Description of child's school, by Total hours/week child attends school. Description of child's school (kindergarten or higher), Wave 4 Regular School - Serves All Students(%) School Serves Only Disabled Students(%) Magnet School(%) >Other(%) Estimates Total94.0 2.9 1.1 2.0 Total hours/week child attends school (kindergarten), Wave 4 15 hours or less96.1 3.3 0.0 0.6 16 to 25 hours88.6 3.6 3.9 3.8 26 to 30 hours83.0 10.5 3.3 3.2 More than 30 hours96.9 0.0 1.9 1.2 NOTE: Rows may not add up to 100% due to rounding.SOURCE: Pre-Elementary Education Longitudinal Study (PEELS), Waves 1-5Computation by NCES QuickStats on 8/23/2011 cchbbf51 Percentage distribution of beginning postsecondary students who took distance education courses by student/employee role: 2003–04 Distance education courses in 2003-04 Yes(%) No(%) Total Estimates Total 9.3 90.7 100% Job 2003-04: primarily student or employee A student working to meet expenses 9.7 90.3 100% An employee enrolled in school 11.4 8.6 100% No job 7.6 92.7 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:04/06). Computation by NCES QuickStats on 6/17/2009 cehak172 Percentage distribution of 2003–04 beginning postsecondary students' persistence at any institution through 2006, by gender. Persistence at any institution through 2006 Attained, still enrolled(%) Attained, not enrolled(%) No degree, still enrolled(%) No degree, not enrolled(%) Total Estimates Total 7.0 8.9 50.7 33.5 100% Gender Male 6.5 7.5 50.4 35.6 100% Female 7.3 9.9 50.9 31.9 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:04/06). Computation by NCES QuickStats on 6/22/2009 cgeak593 Percentage of 2003–04 beginning postsecondary students who received financial aid, by undergraduate degree attainment and enrollment status through 2006 Aid: total student aid all sources in 2003-04(%>0) Estimates Total 70.6 Persistence at any institution through 2006 Attained a degree or certificate 80.1 No degree, still enrolled 70.6 No degree, not enrolled 66.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:04/06). Computation by NCES QuickStats on 6/17/2009 cgeak944 Percentage distribution of 2003–04 beginning postsecondary students' degree attainment and enrollment status through 2006, by grade point average (GPA) Persistence at any institution through 2006 Attained, still enrolled(%) Attained, not enrolled(%) No degree, still enrolled(%) No degree, not enrolled(%) Total Estimates Total 7.0 8.9 50.7 33.5 100% Cumulative Grade Point Average (GPA) as of 2003-04 Below 2.0 3.9 4.2 39.4 52.5 100% 2.1 to 2.50 5.1 5.4 50.7 38.8 100% 2.51 to 2.99 6.4 6.7 59.9 27.0 100% 3.0 and above 8.2 11.4 50.8 29.5 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:04/06). Computation by NCES QuickStats on 6/17/2009 cgeak4f5 Percentage distribution of 2003–04 beginning postsecondary students’ degree attainment and enrollment status through 2006, by highest degree expectations Persistence anywhere through 2006 Attained, still enrolled(%) Attained, not enrolled(%) No degree, still enrolled(%) No degree, not enrolled(%) Total Estimates Total 7.0 8.9 50.7 33.5 100% Highest degree expected, 2003-04 No degree or certificate 3.8 17.1 16.3 62.8 100% Certificate 6.9 41.5 10.3 41.3 100% Associate’s degree 8.7 17.3 25.3 48.8 100% Bachelor’s degree 6.9 7.9 45.2 40.0 100% Post-BA or post-master certificate 5.1 13.4 42.9 38.6 100% Master’s degree 7.1 4.8 60.6 27.4 100% Doctoral degree 7.1 4.2 67.9 20.8 100% First-professional degree 3.5 6.7 67.6 22.2 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003–04 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:04/06). Computation by NCES QuickStats on 6/22/2009 cgeak021 Percentage distribution of undergraduates' attendance intensity, by institution type: 2007–08 Attendance intensity Exclusively full-time(%) Exclusively part-time(%) Mixed full-time and part-time (%) Total Estimates Total 47.7 35.4 16.9 100% Institution: type Public less-than-2-year 64.5 31.5 4.0 100% Public 2-year 26.3 58.8 14.9 100% Public 4-year nondoctorate 54.5 27.9 17.6 100% Public 4-year doctorate 65.0 15.4 19.6 100% Private not-for-profit less than 4-year 55.2 28.9 15.8 100% Private not-for-profit 4-yr nondoctorate 69.0 18.4 12.5 100% Private not-for-profit 4-year doctorate 74.7 13.9 11.5 100% Private for-profit less-than-2-year 75.0 15.8 9.1 100% Private for-profit 2 years or more 67.0 18.7 14.4 100% Attended more than one institution 40.8 26.2 33.0 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 National Postsecondary Student Aid Study (NPSAS:08) Computation by NCES QuickStats on 6/22/2009 cgeakc92 Percentage of undergraduates who received Pell Grants, by income and dependency status: 2007–08 Grants: Pell Grants(%>0) Estimates Total 27.3 Income: categories by dependency status Dependent: Less than $10,000 63.2 Dependent: $10,000-$19,999 72.7 Dependent: $20,000-$29,999 64.9 Dependent: $30,000-$39,999 53.5 Dependent: $40,000-$49,999 32.0 Dependent: $50,000-$59,999 15.4 Dependent: $60,000-$69,999 2.3 Dependent: $70,000-$79,999 0.0 Dependent: $80,000-$99,999 0.0 Dependent: $100,000 or more 0.0 Independent: Less than $5,000 53.3 Independent: $5,000-$9,999 65.5 Independent: $10,000-$19,999 52.3 Independent: $20,000-$29,999 34.8 Independent: $30,000-$49,999 28.2 Independent: $50,000 or more 0.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 National Postsecondary Student Aid Study (NPSAS:08) Computation by NCES QuickStats on 6/26/2009 cgfak1b3 Average net price of attendance after all financial aid for full-time undergraduate students, by type of institution: 2007–08 Net price after all aid(Avg>0) Estimates Total 11,658.9 Type of institution Public less-than-2-year 9,667.4 Public 2-year 7,560.8 Public 4-year nondoctorate 8,922.5 Public 4-year doctorate 11,625.2 Private not-for-profit less-than-4-year 10,782.5 Private not-for-profit 4-year nondoctorate 14,462.2 Private not-for-profit 4-year doctorate 20,047.5 Private for-profit less-than-2-year 10,298.3 Private for-profit 2 years or more 14,406.9 Attended more than one institution ‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 National Postsecondary Student Aid Study (NPSAS:08) Computation by NCES QuickStats on 6/22/2009 cgeakf74 Percentage distribution of dependent undergraduates’ parents’ income, by type of institution: 2007–08 Parents’ income Less than $36,000(%) $36,000-66,999(%) $67,000-104,999(%) $105,000 or more(%) Total Estimates Total 24.8 25.5 25.0 24.7 100% Institution: sector Public 4-year 20.6 22.7 27.4 29.2 100% Private not-for-profit 4-year 17.5 20.9 25.3 36.4 100% Public 2-year 30.6 31.4 23.2 14.8 100% Private for-profit 50.1 25.1 15.9 8.9 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 National Postsecondary Student Aid Study (NPSAS:08) Computation by NCES QuickStats on 6/22/2009 cgeak3a5 Mean estimated student need for undegraduate students, by type of degree program: 2007–08 Aid: estimated student need(Mean[0]) Estimates Total 7,978.0 College study: degree program Certificate 8,696.4 Associate’s degree 5,248.0 Bachelor’s degree 10,890.9 Not in a degree program or others 2,909.0 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 National Postsecondary Student Aid Study (NPSAS:08) Computation by NCES QuickStats on 6/22/2009 cgeaka81 Percentage of graduate students who borrowed, by type of graduate program: 2007–08 Graduate loan debt (cumulative)(%>0) Estimates Total 53.2 Graduate degree: type Master's degree 52.8 Doctoral degree 46.5 First-professional degree 82.1 Post-BA or post-master's certificate 51.6 Not in a degree program 34.8 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 National Postsecondary Student Aid Study (NPSAS:08) Computation by NCES QuickStats on 6/19/2009 ckeake32 Percentage of graduate students with assistantships, by attendance intensity: 2007–08 Assistantships(%>0) Estimates Total 15.2 Attendance intensity Exclusively full-time 23.3 Exclusively part-time 6.5 Mixed full-time and part-time 19.8 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 National Postsecondary Student Aid Study (NPSAS:08) Computation by NCES QuickStats on 6/19/2009 ckeak413 Average tuition waiver received by graduate students, by type of graduate degree program: 2007–08 Tuition waivers(Avg>0) Estimates Total 6,785.2 Graduate degree: type Master's degree 6,387.0 Doctoral degree 7,826.7 First-professional degree 8,521.4 Post-BA or post-master's certificate ‡ Not in a degree program 2,206.9 ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 National Postsecondary Student Aid Study (NPSAS:08) Computation by NCES QuickStats on 6/19/2009 ckeak624 Percentage of graduate students who have ever borrowed loans, by income: 2007–08 Loans: total student loans all sources(%>0) Estimates Total 42.7 Income: total income Less than $13,200 55.3 $13,200-37,399 50.4 $37,400-71,599 38.6 $71,600 or more 26.4 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 National Postsecondary Student Aid Study (NPSAS:08) Computation by NCES QuickStats on 6/19/2009 ckeaka75 Average loan amount for graduate students, by type of institution attended: 2007–08 Loans: total student loans all sources(Avg>0) Estimates Total 18,494.7 Type of 4-year institution Public 4-year nondoctorate-granting 10,668.2 Public 4-year doctorate-granting 16,470.2 Private not-for-profit 4-yr nondoctorate-granting 14,748.3 Private not-for-profit 4-year doctorate-granting 23,496.8 Private for profit 4-year 17,680.3 Attended more than one institution 17,270.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 National Postsecondary Student Aid Study (NPSAS:08) Computation by NCES QuickStats on 6/19/2009 bbfakc71 Highest degree attained anywhere through 2009 by Single parent status in 2003-04. Highest degree attained anywhere through 2009 Certificate(%) Associate’s degree(%) Bachelor’s degree(%) No degree(%) Total Estimates Total 9.4 9.3 30.7 50.5 100% Single parent status in 2003-04 Single parent 17.9 6.3 3.3 72.5 100% Not a single parent 8.4 9.7 34.0 47.9 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, BPS:2009 Beginning Postsecondary Students Computation by QuickStats on 12/1/2010 epba072 Degree program when last enrolled 2009 by Cumulative federal student loan amount owed as of 2009. Degree program when last enrolled 2009 Associate's Degree(%) Bachelor's Program (4 year)(%) Not in a degree program(%) Estimates Total 23.2 68.8 8.1 Cumulative federal student loan amount owed as of 2009 $0 26.3 62.7 11.0 $1-4,899 36.5 53.1 10.5 $4,900-10,299 30.0 61.5 8.5 $10,300-17,999 14.7 81.8 3.5 $18,000 or more 9.8 88.2 2.0 NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, BPS:2009 Beginning Postsecondary StudentsComputation by NCES QuickStats on 5/27/2011epba443 Attainment or level of last institution enrolled through 2009 by Attendance intensity pattern through 2009. Attainment or level of last institution enrolled through 2009 Attained a degree or certificate(%) No degree, enrolled at 4-year(%) No degree, enrolled at less-than-4-year(%) No degree, not enrolled(%) Total Estimates Total 49.5 7.1 7.9 35.5 100% Attendance intensity pattern through 2009 Always full-time 62.6 4.8 2.8 29.7 100% Always part-time 15.7 1.8 11.3 71.3 100% Mixed 41.9 11.2 13.5 33.4 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, BPS:2009 Beginning Postsecondary Students Computation by QuickStats on 12/1/2010 epba324 Transfer and degree plans first year by Job 2004: Hours worked per week (incl work study). Transfer and degree plans first year Degree, no transfer(%) Degree and transfer(%) No degree, transfer(%) No degree, no transfer(%) Total Estimates Total 26.6 21.3 24.1 28.0 100% Job 2004: Hours worked per week (incl work study) Did not work 30.7 17.6 18.5 33.2 100% 1-19 22.4 23.7 31.3 22.6 100% 20-29 21.8 25.6 30.0 22.6 100% 30-39 23.8 22.0 27.6 26.6 100% 40 or more 30.1 20.1 19.0 30.8 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, BPS:2009 Beginning Postsecondary Students Computation by QuickStats on 12/1/2010 epba8e5 Retention and attainment at first institution 6-year total 2009 by Highest level of high school mathematics. Retention and attainment at first institution 6-year total 2009 Attained a degree or certificate(%) No degree, still enrolled(%) No degree, transferred(%) No degree, left without return(%) Total Estimates Total 38.8 6.1 26.8 28.4 100% Highest level of high school mathematics Algebra 2 31.9 7.3 32.6 28.2 100% Trigonometry/Algebra II 43.2 4.9 31.9 20.0 100% Pre-calculus 47.4 5.4 31.4 15.9 100% Calculus 65.4 3.3 21.4 9.9 100% None of these 25.4 7.1 28.1 39.4 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, BPS:2009 Beginning Postsecondary Students Computation by QuickStats on 12/1/2010 epba9e1 Family status in 2012 by Repayment status for any loans in 2012 (federal and private). Family status in 2012 Unmarried, no dependent children(%) Unmarried with dependent children(%) Married, no dependent children(%) Married with dependent children(%) Total Estimates Total54.0 5.4 20.9 19.6 100% Repayment status for any loans in 2012 (federal and private) Deferring payments on loans57.0 7.5 16.6 18.9 100% Repaying loans53.5 6.5 19.8 20.2 100% Loans are paid off or forgiven49.2 2.9 23.5 24.3 100% Defaulted53.7 13.8 12.4 20.2 100% Did not borrow55.2 2.9 24.5 17.3 100% NOTE: Rows may not add up to 100% due to rounding.U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).Computation by NCES QuickStats on 6/27/2014 cgbe2d2 Gender by Highest degree attained since bachelor's as of 2012. Gender Male(%) Female(%) Total Estimates Total42.6 57.4 100% Highest degree attained since bachelor's as of 2012 Undergraduate certificate or diploma36.9 63.1 100% Associate's degree33.2 66.8 100% Additional bachelor's degree36.7 63.3 100% Post-baccalaureate certificate36.3 63.7 100% Master's degree37.7 62.3 100% Post-master's certificate24.1 75.9 100% Doctoral degree - professional practice50.9 49.1 100% Doctoral degree - research/scholarship‡ ‡ 100% Doctoral degree - other‡ ‡ 100% {Skipped}45.3 54.7 100% {Missing}‡ ‡ 100% Did not earn degree39.2 60.8 100% ‡ Reporting standards not met.NOTE: Rows may not add up to 100% due to rounding.U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).Computation by NCES QuickStats on 6/27/2014 cgbe5a3 Income (dependents' parents and independents) in 2006 by Cumulative amount borrowed for education through 2012. Income (dependents' parents and independents) in 2006 0(%) $1-27,799(%) $27,800-62,099(%) $62,100-105,899(%) $105,900or more(%) Total Estimates Total2.3 24.7 24.3 24.8 23.8 100% Cumulative amount borrowed for education through 2012 $1-7,9991.9 31.7 26.6 19.8 19.9 100% $8,000-16,9991.3 25.1 25.2 25.0 23.5 100% $17,000<>-29,9992.1 25.0 26.6 26.4 19.9 100% $30,000 or more2.5 28.9 27.9 24.3 16.3 100% NOTE: Rows may not add up to 100% due to rounding.U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).Computation by NCES QuickStats on 6/27/2014 cgbed64 Transcript: Remedial courses: # taken by Annualized salary for primary job in 2012. Transcript: Remedial courses: # taken 0(%) 1(%) 2(%) 3 or more(%) Total Estimates Total74.1 15.5 5.6 4.9 100% Annualized salary for primary job in 2012 $1-15,59974.1 13.4 5.2 7.3 100% $15,600-26,99974.0 14.8 5.5 5.6 100% $27,000-39,99968.8 17.1 7.5 6.6 100% $40,000-75,99977.3 14.9 4.8 3.0 100% 76,000 or more77.2 16.4 4.4 2.0 100% NOTE: Rows may not add up to 100% due to rounding.U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).Computation by NCES QuickStats on 6/27/2014 cgbe3a5 Undergraduate GPA as of 2007-08 by Annualized salary for primary job in 2012. Undergraduate GPA as of 2007-08 Less than 2.00(%) 2.00-2.49(%) 2.50-2.99(%) 3.00-3.49(%) 3.50 or higher(%) Total Estimates Total0.3 6.3 21.2 35.8 36.4 100% Annualized salary for primary job in 2012 $1-15,5990.3 6.6 16.8 33.6 42.7 100% $15,600-26,9990.4 7.7 20.3 34.3 37.3 100% $27,000-39,9990.2 6.2 24.4 36.5 32.6 100% $40,000-75,9990.3 5.7 20.1 36.6 37.3 100% 76,000 or more0.0 4.0 16.7 33.5 45.8 100% NOTE: Rows may not add up to 100% due to rounding.U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12).Computation by NCES QuickStats on 6/27/2014 cgbe1c1 Four-category school level by Charter school identifier. Four-category school level Primary(%) Middle(%) High(%) Combined(%) Total Estimates Total58.416.518.96.2100% Charter school identifier School is a public charter school44.112.517.625.8100% School is not a public charter school58.816.619.05.6100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/1/2017 bpbhac02 Expenditures - total by Collapsed total K-12 and ungraded enrollment in school. Expenditures - total(Mean[0]) Estimates Total9,344.5 Collapsed total K-12 and ungraded enrollment in school 1-493,793.7 50-994,310.6 100-1494,746.6 150-1996,090.3 200-3496,098.4 350-4998,632.2 500-7499,868.7 750-99910,885.5 1,000-1,19913,228.2 1,200-1,49915,261.6 1,500-1,99923,558.4 2,000 or more21,157.6 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/1/2017 bpbha843 Number of students visits to LMC by Program type of school. Number of students visits to LMC None(%) 1 to 100(%) 101 to 500(%) 501 to 1,000(%) More than 1,000(%) Total Estimates Total1.229.435.533.9‡100% Program type of school Regular1.028.035.835.2‡100% Special program emphasis0.631.431.836.2‡100% Special Education‡61.237.6‡‡100% Career/Technical/Vocational Education‡46.935.217.9‡100% Alternative8.557.429.64.5‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/1/2017 bpbhdp464 Total K-12 and ungraded enrollment in school by Number of books - total. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total578.7 Number of books - total None‡ 1 to 5,000299.5 5,001 to 10,000450.3 10,001 to 15,000589.2 More than 15,000‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/1/2017 bpbha925 Number of computers with internet by Percentage of enrolled students approved for the NSLP at school. Number of computers with internet None(%) 1 to 5(%) 6 to 10(%) 11 to 15(%) 16 to 30(%) More than 30(%) Total Estimates Total0.927.627.815.328.3‡100% Percentage of enrolled students approved for the NSLP at school 0%0.524.233.010.232.1‡100% >0% to 15%1.424.924.216.133.5‡100% >15% to 30%‡19.327.816.536.1‡100% >30% to 50%‡27.125.816.130.9‡100% >50% to 75%0.925.332.614.926.4‡100% More than 75%‡‡‡‡‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/1/2017 bpbhd6d1 Q24b. Principal salary - Highest paid full-time by Collapsed urban-centric district locale code. Q24b. Principal salary - Highest paid full-time $1 to $75,000(%) $75,001 to $85,000(%) $85,001 to $100,000(%) $100,001 to $110,000(%) More than $110,000(%) Total Estimates Total22.917.425.513.121.2100% Collapsed urban-centric district locale code City3.74.523.322.745.7100% Suburb3.13.018.318.656.9100% Town13.922.836.115.911.3100% Rural38.923.724.68.04.9100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/21/2017 cbpbhkcf2 Q12. Count of Principals Employed by Number of schools in district, post-collapsing. Q12. Count of Principals Employed(Mean[0]) Estimates Total7.3 Number of schools in district, post-collapsing 1 to 22.0 3 to 53.5 6 to 106.8 11 to 2013.5 More than 20‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/21/2017 cbpbhke83 Q42. Community service requirement for High school graduates - hours by Q2. Total student enrollment- all grade levels. Q42. Community service requirement for High school graduates - hours 1 to 15(%) 16 to 25(%) 26 to 40(%) 41 to 60(%) More than 60(%) Total Estimates Total29.133.024.013.9‡100% Q2. Total student enrollment- all grade levels 1 to 1,00021.239.124.615.0‡100% 1,001 to 3,00032.734.123.110.1‡100% 3,001 to 7,00030.233.229.96.7‡100% 7,001 to 10,00032.729.427.210.7‡100% More than 10,000‡‡‡‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/21/2017 cbpbhke734 Q10. Teachers association or union agreement by Q3. Total student enrollment- K-12 grade levels. Q10. Teachers association or union agreement Yes, meet-and-confer(%) Yes, collective bargaining(%) No(%) Total Estimates Total14.083.72.4100% Q3. Total student enrollment- K-12 grade levels 1 to 1,00014.881.93.3100% 1,001 to 2,00012.486.21.4100% 2,001 to 5,00012.486.01.6100% 5,001 to 10,00015.183.01.9100% More than 10,000‡‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/21/2017 cbpbhkdc5 Q9f. Count of Teachers Employed - Total by Percentage of students in district approved for the National School Lunch Program. Q9f. Count of Teachers Employed - Total 1 to 50(%) 51 to 150(%) 151 to 400(%) 401 to 1,000(%) More than 1,000(%) Total Estimates Total47.028.217.77.0‡100% Percentage of students in district approved for the National School Lunch Program 0% to 20%24.728.634.412.4‡100% More than 20% to 40%39.733.919.27.1‡100% More than 40% to 60%39.435.718.16.7‡100% More than 60% to 80%44.927.218.89.1‡100% More than 80%70.815.79.24.3‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/21/2017 cbpbhka81 Q43a School participates in the National School Lunch Program by Collapsed urban-centric school locale code. Q43a School participates in the National School Lunch Program Yes(%) No(%) Total Estimates Total100.0‡100% Collapsed urban-centric school locale code City100.0‡100% Suburb100.0‡100% Town100.0‡100% Rural100.0‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/18/2017 bhgbhccc2 Q43a School participates in the National School Lunch Program by Three-category level of school based on grade levels offered. Q43a School participates in the National School Lunch Program Yes(%) No(%) Total Estimates Total100.0‡100% Three-category level of school based on grade levels offered Elementary100.0‡100% Secondary100.0‡100% Combined100.0‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/18/2017 bhgbhcp5a3 Percentage of teachers who are of a racial/ethnic minority by Q48a School is a public charter school. Percentage of teachers who are of a racial/ethnic minority(Mean[0]) Estimates Total16.0 Q48a School is a public charter school Yes30.2 No‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/18/2017 bhgbhc194 Percentage of students who are of a racial/ethnic minority by Q43a School participates in the National School Lunch Program. Percentage of students who are of a racial/ethnic minority(Avg>0) Estimates Total43.3 Q43a School participates in the National School Lunch Program Yes43.4 No‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/18/2017 bhgbhckd85 Estimated number of full-time equivalent teachers in the school by Q29a Number of full-time principals. Estimated number of full-time equivalent teachers in the school(%>40) Estimates Total31.4 Q29a Number of full-time principals Zero1.4 One32.9 Two52.4 More than two94.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/18/2017 bhgbhc901 Q52a School participates in the National School Lunch Program by Collapsed urban-centric school locale code. Q52a School participates in the National School Lunch Program Yes(%) No(%) Total Estimates Total100.0‡100% Collapsed urban-centric school locale code City100.0‡100% Suburb100.0‡100% Town100.0‡100% Rural100.0‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/18/2017 bhgbhccc2 Q52a School participates in the National School Lunch Program by Three-category level of school based on grade levels offered. Q52a School participates in the National School Lunch Program Yes(%) No(%) Total Estimates Total100.0‡100% Three-category level of school based on grade levels offered Elementary100.0‡100% Secondary100.0‡100% Combined100.0‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/18/2017 bhgbhcp5a3 Percentage of teachers who are of a racial/ethnic minority by Three-level private school typology. Percentage of teachers who are of a racial/ethnic minority(Mean[0]) Estimates Total16.0 Three-level private school typology Catholic13.5 Other religious15.7 Nonsectarian19.3 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/18/2017 bhgbhc194 Percentage of students who are of a racial/ethnic minority by Q52a School participates in the National School Lunch Program. Percentage of students who are of a racial/ethnic minority(Avg>0) Estimates Total35.7 Q52a School participates in the National School Lunch Program Yes37.1 No‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/18/2017 bhgbhckd85 Estimated number of full-time equivalent teachers in the school by Q39a Number of full-time principals. Estimated number of full-time equivalent teachers in the school(%>40) Estimates Total7.4 Q39a Number of full-time principals Zero‡ One5.8 Two18.8 More than two66.6 ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/18/2017 bhgbhc901 Q43a/Q52a School participates in the National School Lunch Program by Collapsed urban-centric school locale code. Q43a/Q52a School participates in the National School Lunch Program Yes(%) No(%) Total Estimates Total79.720.3100% Collapsed urban-centric school locale code City79.021.0100% Suburb77.422.6100% Town86.613.4100% Rural79.620.4100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 11/30/2017 bhgbhccc2 School sector (public, private) by Three-category level of school based on grade levels offered. School sector (public, private) Public(%) Private(%) Total Estimates Total77.422.6100% Three-category level of school based on grade levels offered Elementary80.519.5100% Secondary88.111.9100% Combined46.753.3100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/17/2017 bhgbhcp5a3 Percentage of enrolled students with an IEP by Three-category level of school based on grade levels offered. Percentage of enrolled students with an IEP(Mean[0]) Estimates Total12.9 Three-category level of school based on grade levels offered Elementary10.4 Secondary14.9 Combined21.7 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/17/2017 bhgbhc194 Percentage of enrolled students with an IEP by Collapsed urban-centric school locale code. Percentage of enrolled students with an IEP(Avg>0) Estimates Total14.2 Collapsed urban-centric school locale code City14.4 Suburb14.5 Town14.4 Rural13.8 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/17/2017 bhgbhckd85 Estimated number of full-time equivalent teachers in the school by Q43a/Q52a School participates in the National School Lunch Program. Estimated number of full-time equivalent teachers in the school(%>20) Estimates Total64.0 Q43a/Q52a School participates in the National School Lunch Program Yes74.0 No‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 7/17/2017 bhgbhc901 Q25a.Teacher currently holds a bachelor's degree by Census region, based on ANSI state code. Q25a.Teacher currently holds a bachelor's degree Yes(%) No(%) Total Estimates Total95.24.8100% Census region, based on ANSI state code Northeast95.05.0100% Midwest95.44.6100% South95.44.6100% West94.85.2100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhcg372 Estimated number of students per FTE teacher in the school by Four-category school level. Estimated number of students per FTE teacher in the school(Mean[0]) Estimates Total14.8 Four-category school level Primary14.7 Middle15.1 High16.0 Combined11.6 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhcd63 Q25a.Teacher currently holds a bachelor's degree by Full-time, part-time teaching status. Q25a.Teacher currently holds a bachelor's degree Yes(%) No(%) Total Estimates Total95.24.8100% Full-time, part-time teaching status Full-time95.44.6100% Part-time93.66.4100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhcb74 Q56.Number of hours spend on all teaching during a full-week by Highest degree earned. Q56.Number of hours spend on all teaching during a full-week(Mean[0]) Estimates Total51.0 Highest degree earned Associate's degree or no college degree49.3 Bachelor's degree51.1 Master's degree50.9 Education specialist or Certificate of Advanced Graduate Studies51.0 Doctorate or Professional degree51.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhch125 Highest degree earned by Percentage of students in the school who are of racial/ethnic minority. Highest degree earned Associate's degree or no college degree(%) Bachelor's degree(%) Master's degree(%) Education specialist or Certificate of Advanced Graduate Studies(%) Doctorate or Professional degree(%) Total Estimates Total4.440.946.27.31.2100% Percentage of students in the school who are of racial/ethnic minority 0%25.540.929.62.91.1100% >0% to 25%4.238.949.56.41.0100% >25% to 50%4.338.947.47.71.7100% >50% to 75%3.545.741.48.31.1100% >75% to 100%4.242.644.27.71.3100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhc441 Q25a.Teacher currently holds a bachelor's degree by Census region, based on ANSI state code. Q25a.Teacher currently holds a bachelor's degree Yes(%) No(%) Total Estimates Total95.24.8100% Census region, based on ANSI state code Northeast95.05.0100% Midwest95.44.6100% South95.44.6100% West94.85.2100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhcg372 Estimated number of students per FTE teacher in the school by Four-category school level. Estimated number of students per FTE teacher in the school(Mean[0]) Estimates Total14.8 Four-category school level Primary14.7 Middle15.1 High16.0 Combined11.6 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhcd63 Q25a.Teacher currently holds a bachelor's degree by Full-time, part-time teaching status. Q25a.Teacher currently holds a bachelor's degree Yes(%) No(%) Total Estimates Total95.24.8100% Full-time, part-time teaching status Full-time95.44.6100% Part-time93.66.4100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhcb74 Q56.Number of hours spend on all teaching during a full-week by Highest degree earned. Q56.Number of hours spend on all teaching during a full-week(Mean[0]) Estimates Total51.0 Highest degree earned Associate's degree or no college degree49.3 Bachelor's degree51.1 Master's degree50.9 Education specialist or Certificate of Advanced Graduate Studies51.0 Doctorate or Professional degree51.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhch125 Highest degree earned by Percentage of students in the school who are of racial/ethnic minority. Highest degree earned Associate's degree or no college degree(%) Bachelor's degree(%) Master's degree(%) Education specialist or Certificate of Advanced Graduate Studies(%) Doctorate or Professional degree(%) Total Estimates Total4.440.946.27.31.2100% Percentage of students in the school who are of racial/ethnic minority 0%25.540.929.62.91.1100% >0% to 25%4.238.949.56.41.0100% >25% to 50%4.338.947.47.71.7100% >50% to 75%3.545.741.48.31.1100% >75% to 100%4.242.644.27.71.3100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhc441 Q25a.Teacher currently holds a bachelor's degree by Census region, based on ANSI state code. Q25a.Teacher currently holds a bachelor's degree Yes(%) No(%) Total Estimates Total95.24.8100% Census region, based on ANSI state code Northeast95.05.0100% Midwest95.44.6100% South95.44.6100% West94.85.2100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhcg372 Estimated number of students per FTE teacher in the school by Four-category school level. Estimated number of students per FTE teacher in the school(Mean[0]) Estimates Total14.8 Four-category school level Primary14.7 Middle15.1 High16.0 Combined11.6 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhcd63 Q25a.Teacher currently holds a bachelor's degree by Full-time, part-time teaching status. Q25a.Teacher currently holds a bachelor's degree Yes(%) No(%) Total Estimates Total95.24.8100% Full-time, part-time teaching status Full-time95.44.6100% Part-time93.66.4100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhcb74 Q56.Number of hours spend on all teaching during a full-week by Highest degree earned. Q56.Number of hours spend on all teaching during a full-week(Mean[0]) Estimates Total51.0 Highest degree earned Associate's degree or no college degree49.3 Bachelor's degree51.1 Master's degree50.9 Education specialist or Certificate of Advanced Graduate Studies51.0 Doctorate or Professional degree51.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhch125 Highest degree earned by Percentage of students in the school who are of racial/ethnic minority. Highest degree earned Associate's degree or no college degree(%) Bachelor's degree(%) Master's degree(%) Education specialist or Certificate of Advanced Graduate Studies(%) Doctorate or Professional degree(%) Total Estimates Total4.440.946.27.31.2100% Percentage of students in the school who are of racial/ethnic minority 0%25.540.929.62.91.1100% >0% to 25%4.238.949.56.41.0100% >25% to 50%4.338.947.47.71.7100% >50% to 75%3.545.741.48.31.1100% >75% to 100%4.242.644.27.71.3100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2011-12 Computation by NCES QuickStats on 5/1/2017 bebhc441 Q51. Gender by Principal's age. Q51. Gender Male(%) Female(%) Total Estimates Total48.451.6100% Principal's age Under 4057.842.2100% Age 40 to 4950.949.1100% Age 50 to 5940.959.1100% Age 60 or older‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES QuickStats on 4/25/2017 bedbhpp6b2 Total K-12 and ungraded enrollment in school by Program type of school. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total550.8 Program type of school Regular582.5 Special program emphasis553.8 Special Education119.0 Career/Technical/Vocational Education742.6 Alternative158.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES QuickStats on 4/25/2017 bedbhba573 Q25a. Frequency of Problems: physical conflicts between students by Three-category school level (elementary/secondary/combined). Q25a. Frequency of Problems: physical conflicts between students Happens daily(%) Happens at least once a week(%) Happens at least once a month(%) Happens on occasion(%) Never happens(%) Total Estimates Total1.911.815.865.84.7100% Three-category school level (elementary/secondary/combined) Elementary2.314.415.364.73.4100% Secondary0.75.919.067.96.5100% Combined1.67.711.968.610.1100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES QuickStats on 4/25/2017 bedbhbd844 Estimated number of full-time equivalent teachers in the school by Total K-12 and ungraded enrollment in school for Program type of school (Regular). Estimated number of full-time equivalent teachers in the school(%>20) Estimates Total81.2 Total K-12 and ungraded enrollment in school 1 to 25017.0 251 to 50083.8 501 to 75098.4 751 to 100099.9 More than 1000‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES QuickStats on 4/25/2017 bedbhbhb595 Q16e. Influence: hiring new teachers by Q51. Gender. Q16e. Influence: hiring new teachers No Influence(%) Minor Influence(%) Moderate Influence(%) Major Influence(%) Not Applicable(%) Total Estimates Total1.33.410.184.30.9100% Q51. Gender Male0.92.910.584.80.9100% Female1.73.89.783.90.9100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES QuickStats on 4/25/2017 bedbhbkp7f1 Q48. Gender by Principal's age. Q48. Gender Male(%) Female(%) Total Estimates Total44.655.4100% Principal's age Under 4056.743.3100% Age 40 to 4945.754.3100% Age 50 to 5942.257.8100% Age 60 or older‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12 Computation by NCES QuickStats on 4/27/2017 bedbhb692 Total K-12 and ungraded enrollment in school by Program type of school. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total178.5 Program type of school Regular205.5 Special program emphasis91.0 Special Education59.6 Career/Technical/Vocational Education‡ Alternative58.5 ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12 Computation by NCES QuickStats on 4/27/2017 bedbhba93 Q25a. Frequency of Problems: physical conflicts between students by Three-category school level (elementary/secondary/combined). Q25a. Frequency of Problems: physical conflicts between students Happens daily(%) Happens at least once a week(%) Happens at least once a month(%) Happens on occasion(%) Never happens(%) Total Estimates Total0.72.73.665.727.3100% Three-category school level (elementary/secondary/combined) Elementary‡2.23.367.426.8100% Secondary‡4.32.058.033.9100% Combined0.83.24.765.325.9100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12 Computation by NCES QuickStats on 4/27/2017 bedbhb464 Estimated number of full-time equivalent teachers in the school by Total K-12 and ungraded enrollment in school for Program type of school (Regular). Estimated number of full-time equivalent teachers in the school(%>20) Estimates Total27.1 Total K-12 and ungraded enrollment in school 1 to 2506.4 251 to 50075.3 501 to 750100.0 751 to 1000100.0 More than 1000‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12 Computation by NCES QuickStats on 4/27/2017 bedbhb1c5 Q16e. Influence: hiring new teachers by Q48. Gender. Q16e. Influence: hiring new teachers No Influence(%) Minor Influence(%) Moderate Influence(%) Major Influence(%) Not Applicable(%) Total Estimates Total1.63.09.283.72.4100% Q48. Gender Male2.14.513.277.13.1100% Female1.21.85.989.11.9100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12 Computation by NCES QuickStats on 4/27/2017 bedbhbee1 Q51. Gender by Principal's age. Q51. Gender Male(%) Female(%) Total Estimates Total47.652.4100% Principal's age Under 4057.642.4100% Age 40 to 4950.149.9100% Age 50 to 5941.258.8100% Age 60 or older‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2011-12 Computation by NCES QuickStats on 4/27/2017 bedbhbcb2 Total K-12 and ungraded enrollment in school by Program type of school. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total467.9 Program type of school Regular504.0 Special program emphasis486.9 Special Education83.4 Career/Technical/Vocational Education742.6 Alternative140.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2011-12 Computation by NCES QuickStats on 4/27/2017 bedbhb963 Q25a. Frequency of Problems: physical conflicts between students by Three-category school level (elementary/secondary/combined). Q25a. Frequency of Problems: physical conflicts between students Happens daily(%) Happens at least once a week(%) Happens at least once a month(%) Happens on occasion(%) Never happens(%) Total Estimates Total1.69.813.165.89.7100% Three-category school level (elementary/secondary/combined) Elementary1.912.013.065.27.9100% Secondary0.85.717.066.89.6100% Combined1.25.48.266.918.2100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2011-12 Computation by NCES QuickStats on 4/27/2017 bedbhb444 Estimated number of full-time equivalent teachers in the school by Total K-12 and ungraded enrollment in school for Program type of school (Regular). Estimated number of full-time equivalent teachers in the school(%>20) Estimates Total70.0 Total K-12 and ungraded enrollment in school 1 to 25011.0 251 to 50082.8 501 to 75098.5 751 to 100099.9 More than 1000‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2011-12 Computation by NCES QuickStats on 4/27/2017 bedbhbp4e5 Q16e. Influence: hiring new teachers by Q51. Gender. Q16e. Influence: hiring new teachers No Influence(%) Minor Influence(%) Moderate Influence(%) Major Influence(%) Not Applicable(%) Total Estimates Total1.43.39.984.21.2100% Q51. Gender Male1.23.211.183.21.4100% Female1.63.48.885.11.1100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2011-12 Computation by NCES QuickStats on 4/27/2017 bedbhbc41 Percentage distribution of highest level of education earned as of June 2013, by Sex. High school credential or less(%) Some college(%) Bachelor's degree or post-baccalaureate certificate(%) Master's degree or higher(%) Total Estimates Total15.7 51.1 26.6 6.7 100% Sex Male19.7 50.3 25.1 4.9 100% Female11.8 51.8 28.1 8.3 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Sophomores. Computation by NCES QuickStats on 9/11/2015 bbkbfnee2 Percentage distribution of highest known degree attainment as of June 2013, by parent's highest level of education Some College(%) Bachelor's degree(%) Master's degree or higher(%) Total Estimates Total27.7 57.1 15.2 100% Parent's highest level of education High school diploma or less47.5 44.4 8.0 100% Some college36.4 53.6 10.0 100% Bachelor's degree19.4 63.1 17.5 100% Master's degree or higher10.7 60.3 29.1 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Sophomores. Computation by NCES QuickStats on 9/11/2015 bbkbfph5f3 Percentage distribution of Respondents' income from employment, by Employment status as of the third follow-up interview No 2011 employment income(%) Less than $9,000(%) $9,000 - 21,999(%) $22,000 - 35,999(%) $36,000 or more(%) Total Estimates Total11.5 12.4 24.8 26.3 25.0 100% Employment status as of the F3 interview Unemployed38.1 23.1 24.1 10.2 4.5 100% Out of the labor force55.9 13.3 15.4 10.2 5.3 100% Working 0-34 hours/week8.3 28.4 42.5 15.1 5.7 100% Working 35+ hours/week3.4 7.6 22.6 32.6 33.9 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Sophomores. Computation by NCES QuickStats on 9/11/2015 bbkbfpfc4 Percentage distribution of postsecondary GPA at all known institutions attended, by highest level of education student expected Lower than 2.00(%) 2.00 - 2.74(%) 2.75 - 3.24(%) 3.25 or higher(%) Total Estimates Total16.9 23.1 24.2 35.8 100% Student's expected achievement in school: base year Less than high school graduation35.6 26.3 20.5 17.6 100% High school graduation or GED only28.4 25.0 20.9 25.7 100% Attend or complete 2-year college/school22.4 21.8 24.6 31.1 100% Attend college, 4-year degree incomplete26.0 31.6 17.5 24.9 100% Graduate from college16.8 23.8 26.0 33.4 100% Obtain Master's degree or equivalent12.9 20.9 24.4 41.8 100% Obtain PhD, MD, or other advanced degree12.4 21.5 23.4 42.7 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Sophomores. Computation by NCES QuickStats on 9/11/2015 bbkbfnce45 Percentage distribution of number of months between High School completion and BA completion, by Student's race/ethnicity. Less than 48(%) 48 - 59(%) 60 - 71(%) 72 or more(%) Total Estimates Total23.2 39.6 18.4 18.8 100% Student's race/ethnicity Amer. Indian/Alaska Native, non-Hispanic‡ ‡ ‡ ‡ 100% Asian, Hawaii/Pac. Islander,non-Hispanic29.1 34.7 17.2 18.9 100% Black or African American, non-Hispanic16.3 33.1 22.3 28.3 100% Hispanic, no race specified27.0 27.1 15.1 30.7 100% Hispanic, race specified17.8 35.4 22.1 24.7 100% More than one race, non-Hispanic26.5 43.8 10.6 19.2 100% White, non-Hispanic23.7 41.5 18.3 16.5 100% ‡ Reporting standards not met.NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Sophomores. Computation by NCES QuickStats on 9/11/2015 bbkbfah9e1 Percent of schools with at least one violent incident recorded by urbanicity Total number of violent incidents recorded(%>0) Estimates Total73.8 Urbanicity - Based on Urban-centric location of school City74.9 Suburb73.5 Town80.3 Rural70.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2009–10 School Survey on Crime and Safety (SSOCS), 2010. Computation by NCES QuickStats on 6/24/2015 cefbfbee52 Percentage distribution of student bullying by grades offered Q20b. Disciplinary occurences: Student bullying Happens daily(%) Happens at least once a week(%) Happens at least once a month(%) Happens on occasion(%) Never happens(%) Total Estimates Total6.8 16.2 22.6 51.7 2.6 100% School grades offered - based on 07-08 CCD frame variables (School) Primary5.7 13.9 20.4 56.8 3.2 100% Middle13.2 25.4 26.1 35.0 0.3 100% High school3.7 16.1 25.0 53.3 1.9 100% Combined6.7 12.0 26.4 50.2 4.8 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2009–10 School Survey on Crime and Safety (SSOCS), 2010. Computation by NCES QuickStats on 6/24/2015 cefbfbh093 Percentage distribution of parent participation in parent-teacher conferences by school size Q5b. Parent participates in parent-teacher conference 0 to 25%(%) 26 to 50%(%) 51 to 75%(%) 76 to 100%(%) School does not offer(%) Total Estimates Total6.4 17.0 23.1 50.9 2.7 100% School size categories - based on 07-08 CCD frame variables (School) Less than 3006.6 14.6 22.0 54.2 2.7 100% 300 to 4993.8 15.5 23.3 56.2 1.2 100% 500 to 9996.0 16.6 22.6 52.5 2.3 100% 1,000 or more14.0 27.4 26.3 23.4 8.9 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2009–10 School Survey on Crime and Safety (SSOCS), 2010. Computation by NCES QuickStats on 6/24/2015 cefbfbk9d4 Percentage distribution of number of gang-related and hate crimes by urbanicity Total number of gang-related and hate crimes None(%) 1 to 25(%) 26 to 50(%) More than 50(%) Total Estimates Total93.0 6.6 0.2 0.1 100% Urbanicity - Based on Urban-centric location of school City88.0 10.9 0.7 0.4 100% Suburb93.0 6.9 0.1 # 100% Town94.9 5.0 0.1 # 100% Rural96.5 3.5 # # 100% # Rounds to zero. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2009–10 School Survey on Crime and Safety (SSOCS), 2010. Computation by NCES QuickStats on 6/24/2015 cefbfbk6d5 Average number of incidents recorded by urbanicity Total number of incidents recorded(Mean[0]) Estimates Total22.7 Urbanicity - Based on Urban-centric location of school City29.8 Suburb24.5 Town21.1 Rural15.6 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2009–10 School Survey on Crime and Safety (SSOCS), 2010. Computation by NCES QuickStats on 6/24/2015 cefbfbmc41Attainment or level of last institution enrolled through June 2017 by Control and level of first institution (IPEDS sector) 2011-12.Attainment or level of last institution enrolled through June 2017Attained bachelor's degree(%)Attained associate's degree or certificate(%)No degree, enrolled(%)No degree, not enrolled(%)TotalEstimatesTotal36.819.412.131.7100%Control and level of first institution (IPEDS sector) 2011-12Public 4-year59.48.212.120.3100%Private nonprofit 4-year73.64.17.514.7100%Private for profit 4-year14.121.114.650.2100%Public 2-year12.726.414.646.3100%Private nonprofit 2-year10.6 !41.3 !9.7 !38.4100%Private for profit 2-year0.6 !!60.48.230.7100%Public less-than-2-year‡59.58.0 !!32.5100%Private nonprofit less-than-2-year‡68.32.9 !!28.8 !!100%Private for profit less-than-2-year‡58.79.731.1100%! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).Computation by NCES QuickStats on 8/16/2019bghbmgn412Attainment or level of last institution enrolled through June 2017 by Transfer status through June 2017.Attainment or level of last institution enrolled through June 2017Attained bachelor's degree(%)Attained associate's degree or certificate(%)No degree, enrolled(%)No degree, not enrolled(%)TotalEstimatesTotal36.819.412.131.7100%Transfer status through June 2017Zero38.116.58.436.9100%One or more34.224.719.022.0100%NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).Computation by NCES QuickStats on 8/16/2019bghbmghafd3Retention at first institution through June 2017 by Race/ethnicity (with multiple).Retention at first institution through June 2017Attained bachelor's degree(%)Attained associate's degree or certificate(%)No degree, still enrolled(%)No degree, not enrolled or transferred(%)TotalEstimatesTotal27.416.75.650.2100%Race/ethnicity (with multiple)White32.316.74.846.3100%Black or African American16.912.95.564.8100%Hispanic or Latino16.921.58.153.6100%Asian43.910.37.438.5100%American Indian or Alaska Native10.318.1 !7.3 !64.3100%Native Hawaiian/other Pacific Islander14.7 !18.6 !7.2 !!59.4100%More than one race30.016.44.848.8100%! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).Computation by NCES QuickStats on 8/16/2019bghbmgf244Cumulative retention and attainment at first institution through 2016-17 by Bachelor's program intentions within 5 years 2012.Cumulative retention and attainment at first institution through 2016-17Attained bachelor's degree(%)Attained associate's degree or certificate(%)No degree, still enrolled(%)No degree, not enrolled or transferred(%)TotalEstimatesTotal27.416.75.650.2100%Bachelor's program intentions within 5 years 2012Yes0.827.18.263.9100%No0.6 !26.05.068.4100%! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).Computation by NCES QuickStats on 8/16/2019bghbmgm715Retention at first institution through June 2017 by Selectivity of first institution (4-year institutions) 2011-12.Retention at first institution through June 2017Attained bachelor's degree(%)Attained associate's degree or certificate(%)No degree, still enrolled(%)No degree, not enrolled or transferred(%)TotalEstimatesTotal27.416.75.650.2100%Selectivity of first institution (4-year institutions) 2011-12Very selective75.00.73.021.3100%Moderately selective58.50.93.437.1100%Minimally selective32.96.34.856.0100%Open admission11.818.69.360.2100%Not public or private nfp 4-year0.130.66.962.3100%NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).Computation by NCES QuickStats on 8/16/2019bghbmg551 Percentage distribution of total Carnegie credits earned in High School courses for fall 2009 ninth-graders, by parents' highest level of education. X3 Total credits earned 0 - 22(%) 22.5 - 26(%) 26.5 or more(%) Total Estimates Total21.029.349.7100% X2 Parents'/guardians' highest level of education Less than high school39.127.633.3 100% High school diploma or GED or alterntive HS credential24.630.644.7 100% Certificate/diploma from school providing occupational training25.028.646.3 100% Associate's degree18.629.052.3 100% Bachelor's degree16.428.754.9 100% Master's degree12.327.959.8 100% Ph.D/M.D/Law/other high lvl prof degree11.029.059.9 100% No bio/adoptive/step-parent in household‡‡‡ 100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09). Computation by NCES QuickStats on 2/17/2016 bhbbgd2f2 Percentage distribution of fall 2009 ninth-graders considering a STEM major, by highest level of mathematics course taken in high school. S3 C05C Major will be considering - STEM code Yes(%) No(%) Total Estimates Total22.977.1 100% X3 Highest level mathematics course taken - ninth grade Basic math18.881.2 100% Other math17.083.0 100% Pre-algebra12.088.0 100% Algebra I17.382.7 100% Geometry29.870.2 100% Algebra II36.064.0 100% Trigonometry50.349.7 100% Other advanced math26.573.5 100% Probability and statistics‡‡ 100% Other AP/IB math‡‡ 100% Precalculus50.849.2 100% Calculus‡‡ 100% AP/IB Calculus‡‡ 100% No Math10.189.9 100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09). Computation by NCES QuickStats on 2/18/2016 bkbbgkad3 Percentage distribution of High School GPA in all academic courses for fall 2009 ninth-graders, by socioeconomic status. X3 GPA for all academic courses 0 - 1.5(%) 2 - 2.5(%) 3(%) 3.5 - 4(%) Total Estimates Total24.749.326.0# 100% Socieoeconomic Status (Quintiles) First quintile (lowest)36.246.817.0# 100% Second quintile29.748.821.4# 100% Third quintile23.453.423.2# 100% Fourth quintile16.852.730.4# 100% Fifth quintile (highest)10.043.246.8# 100% # Rounds to zero. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09). Computation by NCES QuickStats on 2/18/2016 bkbbgm9a4 Percentage distribution of total credits earned in High School mathematics courses for fall 2009 ninth-graders, by respondent's sex. X3 Credits earned in: mathematics 0 - 2.5(%) 3 - 3.5(%) 4 or more(%) Total Estimates Total21.423.754.9 100% X2 Student's sex Male24.723.651.7 100% Female18.123.858.1 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09). Computation by NCES QuickStats on 2/18/2016 bkbbgmc895 Percentage distribution of total credits earned in High School STEM courses for fall 2009 ninth-graders, by mathematics quintile score. X3 Credits earned in: STEM 0 - 5.5(%) 6 - 7.5(%) 8 - 8.5(%) 9 or more(%) Total Estimates Total17.328.922.331.5 100% X2 Mathematics quintile score First (lowest) quintile34.128.816.620.5 100% Second quintile23.232.720.223.9 100% Third (middle) quintile16.134.322.327.2 100% Fouth quintile10.328.126.135.5 100% Fifth (highest) quintile5.421.625.447.6 100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09). Computation by NCES QuickStats on 2/18/2016 bkbbgma41 Total number of violent incidents recorded by School size categories - based on 03-04 CCD frame variables (School). Total number of violent incidents recorded(%>0) Estimates Total77.7 School size categories - based on 03-04 CCD frame variables (School) < 30063.7 300 - 49977.3 500 - 99982.1 1,000 +96.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006.Computation by NCES QuickStats on 2/10/2016 babbgadbe2 Q5b. Parent involvement: Parent participates in parent-teacher conference by Urbanicity - from 03-04 CCD (School). Q5b. Parent involvement: Parent participates in parent-teacher conference 0-25%(%) 26-50%(%) 51-75%(%) 76-100%(%) School does not offer(%) Total Estimates Total6.7 14.5 23.9 52.6 2.3 100% Urbanicity - from 03-04 CCD (School) City7.1 15.6 25.8 49.1 2.4 100% Urban Fringe5.7 10.9 21.1 59.7 2.7 100% Town6.6 15.3 24.7 51.8 1.7 100% Rural7.4 17.2 25.0 48.5 1.9 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006.Computation by NCES QuickStats on 2/10/2016 babbgakfe03 Q7. Presence of security guard, security personnel, or sworn law enforcement officer by Q30. Level of crime where school is located. Q7. Presence of security guard, security personnel, or sworn law enforcement officer Yes(%) No(%) Total Estimates Total41.7 58.3 100% Q30. Level of crime where school is located High level of crime49.4 50.6 100% Moderate level of crime49.6 50.4 100% Low level of crime39.1 60.9 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006.Computation by NCES QuickStats on 2/10/2016 babbgd0b 4 Total number of disruptions by Q1s. School practice: Security cameras monitor the school. Total number of disruptions None(%) 1 to 5(%) 6 to 10(%) More than 10(%) Total Estimates Total70.9 27.0 1.5 0.6 100% Q1s. School practice: Security cameras monitor the school Yes69.9 27.6 1.6 0.8 100% No71.6 26.5 1.4 0.5 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006.Computation by NCES QuickStats on 2/10/2016 babbgd28 5 Q25b. Percent students limited English proficient by School size categories - based on 03-04 CCD frame variables (School). Q25b. Percent students limited English proficient 0% to 25%(%) 26% to 50%(%) 51% to 75%(%) 76% to 100%(%) Total Estimates Total89.2 6.5 2.9 1.3 100% School size categories - based on 03-04 CCD frame variables (School) < 30093.2 3.4 2.1 1.3 100% 300 - 49991.0 6.3 2.1 0.6 100% 500 - 99985.5 8.3 4.1 2.0 100% 1,000 +87.6 8.4 3.1 0.9 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006.Computation by NCES QuickStats on 2/11/2016 bbbbgk96 1 Total number of violent incidents recorded by School size categories - based on 05-06 CCD frame variables (School). Total number of violent incidents recorded(%>0) Estimates Total75.5 School size categories - based on 05-06 CCD frame variables (School) Less than 30060.6 300 to 49969.1 500 to 99983.4 1,000 or more97.0 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 School Survey on Crime and Safety (SSOCS), 2008.Computation by NCES QuickStats on 2/9/2016 mbbgbd42 Parent participates in parent-teacher conference by Urbanicity - Based on Urban-centric location of school. Q5b. Parent participates in parent-teacher conference 0 to 25%(%) 26 to 50%(%) 51 to 75%(%) 76 to 100%(%) School does not offer(%) Total Estimates Total7.1 16.1 22.9 51.0 3.0 100% Urbanicity - Based on Urban-centric location of school City6.1 16.9 23.0 51.6 2.4 100% Suburb4.4 12.7 22.3 56.4 4.2 100% Town7.4 17.9 25.7 47.3 1.8 100% Rural10.2 17.7 22.1 47.1 2.9 100% NOTE: Rows may not add up to 100% due to rounding SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 School Survey on Crime and Safety (SSOCS), 2008.Computation by NCES QuickStats on 2/9/2016 mbbgb123 Total number of full-time security guards, SROs, or sworn law enforcement officers by Q30. Level of crime where school is located for School grades offered - based on 05-06 CCD frame variables (School) (High school). Total number of full-time security guards, SROs, or sworn law enforcement officers(Avg>0) Estimates Total4.5 Q30. Level of crime where school is located High level of crime5.8 Moderate level of crime6.5 Low level of crime3.8 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 School Survey on Crime and Safety (SSOCS), 2008.Computation by NCES QuickStats on 2/9/2016 mbbgc2c4 Total number of disruptions by Q1t. School practice: Security cameras monitor the school. Total number of disruptions None(%) 1 to 10(%) 11 to 20(%) 21 to 30(%) More than 30(%) Total Estimates Total70.3 29.4 0.3 # # 100% Q1t. School practice: Security cameras monitor the school Yes66.6 33.0 0.4 0.1 # 100% No74.9 25.0 0.2 # # 100% # Rounds to zero.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 School Survey on Crime and Safety (SSOCS), 2008.Computation by NCES QuickStats on 2/9/2016 mbbgcb15 Percent students limited English proficient by School size categories - based on 05-06 CCD frame variables (School) for School grades offered - based on 05-06 CCD frame variables (School) (Primary, Middle). Q25b. Percent students limited English proficient 0% to 25%(%) 26% to 50%(%) 51% to 75%(%) 76% to 100%(%) Total Estimates Total84.9 9.7 3.8 1.6 100% School size categories - based on 05-06 CCD frame variables (School) Less than 30089.4 6.5 1.5 2.6 100% 300 to 49988.3 8.9 2.4 0.4 100% 500 to 99980.7 12.0 6.2 1.1 100% 1,000 or more77.8 11.0 4.0 7.1 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2007–08 School Survey on Crime and Safety (SSOCS), 2008.Computation by NCES QuickStats on 2/9/2016 mbbgce521 Census Region by School Typology. Census Region Northeast(%) Midwest(%) South(%) West(%) Total Estimates Total24.125.829.820.2100% School Typology Catholic, parochial26.139.919.114.9100% Catholic, diocesan23.437.421.917.3100% Catholic, private34.625.721.118.6100% Other religious, conservative Christian11.320.445.822.4100% Other relig., affiliated w/ established denomination21.324.234.819.7100% Other relig., not affiliated w/ any denomination23.235.328.912.7100% Nonsectarian, regular school32.913.427.726.0100% Nonsectarian, special program20.718.229.032.0100% Nonsectarian, special education40.912.026.220.9100% Counts Total7447 7963 9203 6241 30861 School Typology Catholic, parochial761 1161 555 433 2910 Catholic, diocesan683 1093 640 506 2922 Catholic, private360 267 219 194 1041 Other religious, conservative Christian518 935 2097 1024 4574 Other relig., affiliated w/ established denomination653 741 1065 601 3060 Other relig., not affiliated w/ any denomination1526 2319 1900 833 6579 Nonsectarian, regular school1605 654 1352 1271 4882 Nonsectarian, special program679 598 952 1050 3280 Nonsectarian, special education660 197 423 337 1613 NOTE: Rows may not add up to 100% due to rounding. NOTE: Detail may not sum to totals because of rounding or missing values in cells with too few cases. SOURCE: U.S. Department of Education, National Center for Education Statistics, Private School Universe Survey (PSS), 2011–12. Computation by NCES QuickStats on 4/7/2017 hdbhgnh292 Student-Teacher Ratio by Percent to 4-Year College. Student-Teacher Ratio 0 to 1(%) 1 to 10(%) 10 to 20(%) Higher than 20(%) Total Estimates Total1.353.639.55.6100% Percent to 4-Year College 00.478.020.01.6100% 1% to 25%‡53.840.45.8100% 26% to 50%‡63.431.94.3100% 51% to 75%‡47.750.22.1100% 76% to 100%0.150.947.21.7100% Counts Total377 14273 10832 1499 26981 Percent to 4-Year College 06 1131 290 24 1450 1% to 25%‡ ‡ ‡ ‡ ‡ 26% to 50%‡ ‡ ‡ ‡ ‡ 51% to 75%‡ ‡ ‡ ‡ ‡ 76% to 100%4 2205 2045 74 4329 ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. NOTE: Detail may not sum to totals because of rounding or missing values in cells with too few cases. SOURCE: U.S. Department of Education, National Center for Education Statistics, Private School Universe Survey (PSS), 2011–12. Computation by NCES QuickStats on 4/7/2017 hdbhgpe93 Days in School Year by Hours in School Day for Students. Days in School Year Lowest quartile(%) Lower-middle quartile(%) Upper-middle quartile(%) Highest quartile(%) Total Estimates Total25.912.441.620.1100% Hours in School Day for Students 1 to 341.43.529.125.9100% 4 to 625.812.342.419.4100% 7 to 925.913.241.719.1100% 109.22.921.566.3100% Counts Total7984 3839 12830 6209 30861 Hours in School Day for Students 1 to 3200 17 141 125 482 4 to 64257 2034 6986 3202 16480 7 to 93483 1774 5601 2569 13427 1044 14 102 313 472 NOTE: Rows may not add up to 100% due to rounding. NOTE: Detail may not sum to totals because of rounding or missing values in cells with too few cases. SOURCE: U.S. Department of Education, National Center for Education Statistics, Private School Universe Survey (PSS), 2011–12. Computation by NCES QuickStats on 4/7/2017 hdbhgd94 Urban-Centric Community Type by Number of Male Students (Coeducational). Urban-Centric Community Type City (ulocale=11, 12, 13)(%) Suburb (ulocale=21, 22, 23)(%) Town (ulocale=31, 32, 33)(%) Rural (ulocale=41, 42, 43)(%) Total Estimates Total32.435.49.422.8100% Number of Male Students (Coeducational) Lowest quartile26.439.48.525.7100% Lower-middle quartile24.529.610.934.9100% Upper-middle quartile34.233.513.119.2100% Highest quartile42.239.86.211.8100% Counts Total1005 10911 2900 7045 30681 Number of Male Students (Coeducational) Lowest quartile1921 2867 618 1868 7274 Lower-middle quartile1915 2317 854 2730 7816 Upper-middle quartile2479 2431 951 1389 7251 Highest quartile3051 2881 449 856 7237 NOTE: Rows may not add up to 100% due to rounding. NOTE: Detail may not sum to totals because of rounding or missing values in cells with too few cases. SOURCE: U.S. Department of Education, National Center for Education Statistics, Private School Universe Survey (PSS), 2011–12. Computation by NCES QuickStats on 4/7/2017 hdbhgeg5 Size of School (K-12, UG) by School Typology. Size of School (K-12, UG) Lessthan 50students(%) 50-149students(%) 150-299students(%) 300-499students(%) 500-749students(%) 750studentsor more(%) Total Estimates Total43.624.817.87.93.62.3100% School Typology Catholic, parochial3.623.645.020.26.11.5100% Catholic, diocesan3.623.440.019.19.74.1100% Catholic, private18.218.320.016.913.912.8100% Other religious, conservative Christian38.333.017.46.32.92.0100% Other relig., affiliated w/ established denomination35.131.819.47.43.52.9100% Other relig., not affiliated w/ any denomination63.122.39.03.21.31.0100% Nonsectarian, regular school60.515.910.76.73.23.1100% Nonsectarian, special program68.823.85.31.50.50.2100% Nonsectarian, special education54.137.07.51.20.10.1100% Counts Total13459 7667 5488 2447 1103 698 30684 School Typology Catholic, parochial106 686 1310 589 178 44 2910 Catholic, diocesan106 684 1170 559 283 120 2922 Catholic, private189 190 209 176 145 133 1041 Other religious, conservative Christian1751 1511 796 290 135 92 4574 Other relig., affiliated w/ established denomination1074 973 593 227 106 88 3060 Other relig., not affiliated w/ any denomination4152 1470 594 214 85 65 6579 Nonsectarian, regular school2953 776 523 326 155 150 4882 Nonsectarian, special program2256 781 174 48 16 6 3280 Nonsectarian, special education‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Reporting standards not met NOTE: Rows may not add up to 100% due to rounding. NOTE: Detail may not sum to totals because of rounding or missing values in cells with too few cases. SOURCE: U.S. Department of Education, National Center for Education Statistics, Private School Universe Survey (PSS), 2011–12. Computation by NCES QuickStats on 4/7/2017 hdbhgf91 Undergraduate degree program by Total grants. Undergraduate degree program Certificate(%) Associate's degree(%) Bachelor's degree(%) Not in a degree program(%) Total Estimates Total8.0 42.3 46.4 3.3 100% Total grants $1-1,9999.0 54.3 33.0 3.7 100% $2,000-3,99911.5 48.7 38.7 1.0 100% $4,000-6,99910.1 42.4 46.7 0.7 100% $7,000 or more2.1 11.8 85.8 0.3 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES QuickStats on 6/23/2013 cahbd712 Federal Pell grant by NPSAS institution sector (4 with multiple). Federal Pell grant $100-1,999(%) $2,000-2,999(%) $3,000-4,499(%) $4,500-5,550(%) Total Estimates Total24.5 24.1 15.7 35.7 100% NPSAS institution sector (4 with multiple) Public 4-year18.7 20.0 15.3 46.0 100% Private not-for-profit 4-year19.1 20.8 16.0 44.2 100% Public 2-year30.9 27.6 16.5 25.0 100% Private for-profit24.9 26.4 14.0 34.7 100% More than one school22.1 21.2 17.2 39.5 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES QuickStats on 6/23/2013 cahbd223 Gender by Attendance intensity (all schools). Gender Male(%) Female(%) Total Estimates Total43.0 57.0 100% Attendance intensity (all schools) Exclusively full-time43.6 56.4 100% Exclusively part-time42.2 57.8 100% Mixed full-time and part-time42.9 57.1 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES QuickStats on 6/23/2013 cahbd1c4 Total federal aid (excludes Veterans'/DOD) by Race/ethnicity (with multiple). Total federal aid (excludes Veterans'/DOD) $100-3,699(%) $3,700-6,499(%) $6,500-11,599(%) $11,600 or more(%) Total Estimates Total24.5 23.8 26.6 25.1 100% Race/ethnicity (with multiple) White23.1 22.9 28.8 25.2 100% Black or African American24.9 21.5 25.2 28.4 100% Hispanic or Latino29.2 27.6 21.8 21.4 100% Asian22.7 30.5 25.5 21.3 100% American Indian or Alaska Native30.8 22.9 22.6 23.7 100% Native Hawaiian / other Pacific Islander20.4 28.5 23.7 27.4 100% Other22.0 23.2 26.2 28.6 100% More than one race‡ ‡ ‡ ‡ 100% ‡ Reporting standards not met.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES QuickStats on 6/23/2013 cahbd675 Citizenship by Total aid amount. Citizenship US citizen(%) Resident alien(%) Foreign or international student(%) Total Estimates Total94.0 4.2 1.8 100% Total aid amount $100-3,49994.3 4.9 0.8 100% $3,500-7,69995.0 4.4 0.6 100% $7,700-14,69995.8 3.7 0.5 100% $14,700 or more96.3 2.7 1.0 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES QuickStats on 6/23/2013 cahbd301 Institutional tuition & fee waivers by NPSAS institution type: Graduate (with multiple). Institutional tuition & fee waivers $100-1,899(%) $1,900-5,599(%) $5,600-11,299(%) $11,300 or more(%) Total Estimates Total24.4 26.4 24.9 24.3 100% NPSAS institution type: Graduate (with multiple) Public 4-year nondoctorate-granting30.6 45.3 20.6 3.5 100% Public 4-year doctorate-granting21.6 21.2 30.0 27.1 100% Private not-for-profit 4-yr nondoctorate-granting34.5 45.2 16.3 3.9 100% Private not-for-profit 4-year doctorate-granting16.0 30.3 17.8 35.9 100% Private for profit 4-year50.5 33.5 15.4 0.7 100% Attended more than one institution19.6 33.3 23.3 23.8 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study Graduate Students (NPSAS:12).Computation by NCES QuickStats on 6/23/2013 cahbd282 Age as of 12/31/2011 by Total income (continuous). Age as of 12/31/2011(Avg>0) Estimates Total32.3 Total income (continuous) Less than $10,80026.7 $10,800-32,69929.8 $32,700-67,19934.5 $67,200 or more38.3 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study Graduate Students (NPSAS:12).Computation by NCES QuickStats on 6/23/2013 cahbd9c3 Total loans by Graduate degree program. Total loans(Mean[0]) Estimates Total9,656.8 Graduate degree program Master's degree8,001.3 Doctoral degree4,249.0 First-professional degree4,583.1 Post-BA or post-master's certificate30,743.7 Not in a degree program13,175.0 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study Graduate Students (NPSAS:12).Computation by NCES QuickStats on 6/23/2013 cahbd9d4 Attendance intensity (all schools) by Employer aid (includes college staff). Attendance intensity (all schools) Exclusively full-time(%) Exclusively part-time(%) Mixed full-time and part-time(%) Total Estimates Total46.5 37.9 15.6 100% Employer aid (includes college staff) $1-1,99922.1 62.6 15.2 100% $2,000-4,99929.6 56.4 14.0 100% $5,000-10,09936.3 48.5 15.3 100% $10,100 or more55.3 28.9 15.8 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study Graduate Students (NPSAS:12).Computation by NCES QuickStats on 6/23/2013 cahbd4f5 State aid total by Graduate degree program. State aid total $100-1,399(%) $1,400-1,999(%) $2,000-3,999(%) $4,000 or more(%) Total Estimates Total24.8 18.2 27.5 29.5 100% Graduate degree program Master's degree24.5 20.1 23.2 32.2 100% Doctoral degree‡ ‡ ‡ ‡ 100% First-professional degree27.0 17.9 16.1 39.0 100% Post-BA or post-master's certificate25.9 14.5 33.8 25.8 100% Not in a degree program37.0 11.7 36.5 14.8 100% ‡ Reporting standards not met.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study Graduate Students (NPSAS:12).Computation by NCES QuickStats on 6/23/2013 cahbd631 Program type of school by Census region, based on ANSI state code. Program type of school Regular(%) Special program emphasis(%) Special Education(%) Vocational Education(%) Alternative(%) Total Estimates Total90.95.11.20.81.9100% Census region, based on ANSI state code Northeast90.64.62.31.41.1100% Midwest93.23.31.00.81.8100% South89.66.80.80.81.9100% West91.14.61.00.23.2100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhn2c2 Number of students taught by teachers of departmentalized classes by Q20.a. Teacher currently holds a bachelor's degree. Number of students taught by teachers of departmentalized classes(Mean[0]) Estimates Total119.7 Q20.a. Teacher currently holds a bachelor's degree Yes120.3 No89.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhn573 Program type of school by Percentage of teachers in the school who are of racial/ethnic minority. Program type of school Regular(%) Special program emphasis(%) Special Education(%) Vocational Education(%) Alternative(%) Total Estimates Total90.95.11.20.81.9100% Percentage of teachers in the school who are of racial/ethnic minority 0%94.11.71.21.02.0100% >0% to 25%91.94.91.30.61.3100% >25% to 50%78.614.11.30.25.6100% >50% to 75%85.58.80.72.62.4100% >75% to 100%87.59.00.30.23.0100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhn804 Average class size for teachers of departmentalized classes by Three-category school level. Average class size for teachers of departmentalized classes(Mean[0]) Estimates Total23.9 Three-category school level Elementary25.2 Secondary24.3 Combined17.9 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhnb025 Q22a Teacher currently holds a master's degree by Percentage of students in the school who are of racial/ethnic minority. Q22a Teacher currently holds a master's degree Yes(%) No(%) Total Estimates Total47.2 52.8 100% Percentage of students in the school who are of racial/ethnic minority 0%46.0 54.0 100% >0% to 25%50.8 49.2 100% >25% to 50%46.4 53.6 100% >50% to 75%42.6 57.4 100% >75% to 100%43.7 56.3 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2003-04Computation by NCES QuickStats on 9/21/2017 bebhnc91 Program type of school by Census region, based on ANSI state code. Program type of school Regular(%) Special program emphasis(%) Special Education(%) Vocational Education(%) Alternative(%) Total Estimates Total90.95.11.20.81.9100% Census region, based on ANSI state code Northeast90.64.62.31.41.1100% Midwest93.23.31.00.81.8100% South89.66.80.80.81.9100% West91.14.61.00.23.2100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhn2c2 Number of students taught by teachers of departmentalized classes by Q20.a. Teacher currently holds a bachelor's degree. Number of students taught by teachers of departmentalized classes(Mean[0]) Estimates Total119.7 Q20.a. Teacher currently holds a bachelor's degree Yes120.3 No89.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhn573 Program type of school by Percentage of teachers in the school who are of racial/ethnic minority. Program type of school Regular(%) Special program emphasis(%) Special Education(%) Vocational Education(%) Alternative(%) Total Estimates Total90.95.11.20.81.9100% Percentage of teachers in the school who are of racial/ethnic minority 0%94.11.71.21.02.0100% >0% to 25%91.94.91.30.61.3100% >25% to 50%78.614.11.30.25.6100% >50% to 75%85.58.80.72.62.4100% >75% to 100%87.59.00.30.23.0100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhn804 Average class size for teachers of departmentalized classes by Three-category school level. Average class size for teachers of departmentalized classes(Mean[0]) Estimates Total23.9 Three-category school level Elementary25.2 Secondary24.3 Combined17.9 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhnb025 Q22a Teacher currently holds a master's degree by Percentage of students in the school who are of racial/ethnic minority. Q22a Teacher currently holds a master's degree Yes(%) No(%) Total Estimates Total47.2 52.8 100% Percentage of students in the school who are of racial/ethnic minority 0%46.0 54.0 100% >0% to 25%50.8 49.2 100% >25% to 50%46.4 53.6 100% >50% to 75%42.6 57.4 100% >75% to 100%43.7 56.3 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2003-04Computation by NCES QuickStats on 9/21/2017 bebhnc91 Program type of school by Census region, based on ANSI state code. Program type of school Regular(%) Special program emphasis(%) Special Education(%) Vocational Education(%) Alternative(%) Total Estimates Total90.95.11.20.81.9100% Census region, based on ANSI state code Northeast90.64.62.31.41.1100% Midwest93.23.31.00.81.8100% South89.66.80.80.81.9100% West91.14.61.00.23.2100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhn2c2 Number of students taught by teachers of departmentalized classes by Q20.a. Teacher currently holds a bachelor's degree. Number of students taught by teachers of departmentalized classes(Mean[0]) Estimates Total119.7 Q20.a. Teacher currently holds a bachelor's degree Yes120.3 No89.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhn573 Program type of school by Percentage of teachers in the school who are of racial/ethnic minority. Program type of school Regular(%) Special program emphasis(%) Special Education(%) Vocational Education(%) Alternative(%) Total Estimates Total90.95.11.20.81.9100% Percentage of teachers in the school who are of racial/ethnic minority 0%94.11.71.21.02.0100% >0% to 25%91.94.91.30.61.3100% >25% to 50%78.614.11.30.25.6100% >50% to 75%85.58.80.72.62.4100% >75% to 100%87.59.00.30.23.0100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhn804 Average class size for teachers of departmentalized classes by Three-category school level. Average class size for teachers of departmentalized classes(Mean[0]) Estimates Total23.9 Three-category school level Elementary25.2 Secondary24.3 Combined17.9 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2003-04 Computation by NCES QuickStats on 5/1/2017 bebhnb025 Q22a Teacher currently holds a master's degree by Percentage of students in the school who are of racial/ethnic minority. Q22a Teacher currently holds a master's degree Yes(%) No(%) Total Estimates Total47.2 52.8 100% Percentage of students in the school who are of racial/ethnic minority 0%46.0 54.0 100% >0% to 25%50.8 49.2 100% >25% to 50%46.4 53.6 100% >50% to 75%42.6 57.4 100% >75% to 100%43.7 56.3 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2003-04Computation by NCES QuickStats on 9/21/2017 bebhnc91 School sector by Census region, based on ANSI state code. School sector Public school(%) Private school(%) Total Estimates Total87.412.6100% Census region, based on ANSI state code Northeast85.114.9100% Midwest87.412.6100% South88.311.7100% West88.411.6100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2007-08 Computation by NCES QuickStats on 5/1/2017 bebha5f2 Estimated number of students per FTE teacher in the school by School sector. Estimated number of students per FTE teacher in the school(Mean[0]) Estimates Total14.6 School sector Public school15.0 Private school11.8 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2007-08 Computation by NCES QuickStats on 5/1/2017 bebhaed3 Q23a Teacher currently holds a bachelor's degree by Four-category school level. Q23a Teacher currently holds a bachelor's degree Yes(%) No(%) Total Estimates Total99.2 0.8 100% Four-category school level Primary99.7 0.3 100% Middle99.8 0.2 100% High97.9 2.1 100% Combined99.1 0.9 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2007-08Computation by NCES QuickStats on 9/21/2017 bebha4d4 Average class size for teachers of departmentalized classes by New teacher flag-teacher has taught 3 or fewer years. Average class size for teachers of departmentalized classes(Mean[0]) Estimates Total23.0 New teacher flag-teacher has taught 3 or fewer years Teacher has taught three years or less22.5 Teacher has taught more than three years23.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2007-08 Computation by NCES QuickStats on 5/1/2017 bebhapa85 Q38 1st year teaching: participated in a teacher induction program by Four-category school level. Q38 1st year teaching: participated in a teacher induction program Yes(%) No(%) Total Estimates Total73.5 26.5 100% Four-category school level Primary74.2 25.8 100% Middle75.6 24.4 100% High72.8 27.2 100% Combined62.9 37.1 100% NOTE: Rows may not add up to 100% due to roundingSOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2007-08Computation by NCES QuickStats on 9/21/2017 bebha321 School sector by Census region, based on ANSI state code. School sector Public school(%) Private school(%) Total Estimates Total87.412.6100% Census region, based on ANSI state code Northeast85.114.9100% Midwest87.412.6100% South88.311.7100% West88.411.6100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2007-08 Computation by NCES QuickStats on 5/1/2017 bebha5f2 Estimated number of students per FTE teacher in the school by School sector. Estimated number of students per FTE teacher in the school(Mean[0]) Estimates Total14.6 School sector Public school15.0 Private school11.8 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2007-08 Computation by NCES QuickStats on 5/1/2017 bebhaed3 Q23a Teacher currently holds a bachelor's degree by Four-category school level. Q23a Teacher currently holds a bachelor's degree Yes(%) No(%) Total Estimates Total99.2 0.8 100% Four-category school level Primary99.7 0.3 100% Middle99.8 0.2 100% High97.9 2.1 100% Combined99.1 0.9 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2007-08Computation by NCES QuickStats on 9/21/2017 bebha4d4 Average class size for teachers of departmentalized classes by New teacher flag-teacher has taught 3 or fewer years. Average class size for teachers of departmentalized classes(Mean[0]) Estimates Total23.0 New teacher flag-teacher has taught 3 or fewer years Teacher has taught three years or less22.5 Teacher has taught more than three years23.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2007-08 Computation by NCES QuickStats on 5/1/2017 bebhapa85 Q38 1st year teaching: participated in a teacher induction program by Four-category school level. Q38 1st year teaching: participated in a teacher induction program Yes(%) No(%) Total Estimates Total73.5 26.5 100% Four-category school level Primary74.2 25.8 100% Middle75.6 24.4 100% High72.8 27.2 100% Combined62.9 37.1 100% NOTE: Rows may not add up to 100% due to roundingSOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2007-08Computation by NCES QuickStats on 9/21/2017 bebha321 School sector by Census region, based on ANSI state code. School sector Public school(%) Private school(%) Total Estimates Total87.412.6100% Census region, based on ANSI state code Northeast85.114.9100% Midwest87.412.6100% South88.311.7100% West88.411.6100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2007-08 Computation by NCES QuickStats on 5/1/2017 bebha5f2 Estimated number of students per FTE teacher in the school by School sector. Estimated number of students per FTE teacher in the school(Mean[0]) Estimates Total14.6 School sector Public school15.0 Private school11.8 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2007-08 Computation by NCES QuickStats on 5/1/2017 bebhaed3 Q23a Teacher currently holds a bachelor's degree by Four-category school level. Q23a Teacher currently holds a bachelor's degree Yes(%) No(%) Total Estimates Total99.2 0.8 100% Four-category school level Primary99.7 0.3 100% Middle99.8 0.2 100% High97.9 2.1 100% Combined99.1 0.9 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2007-08Computation by NCES QuickStats on 9/21/2017 bebha4d4 Average class size for teachers of departmentalized classes by New teacher flag-teacher has taught 3 or fewer years. Average class size for teachers of departmentalized classes(Mean[0]) Estimates Total23.0 New teacher flag-teacher has taught 3 or fewer years Teacher has taught three years or less22.5 Teacher has taught more than three years23.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Teachers Data File 2007-08 Computation by NCES QuickStats on 5/1/2017 bebhapa85 Q38 1st year teaching: participated in a teacher induction program by Four-category school level. Q38 1st year teaching: participated in a teacher induction program Yes(%) No(%) Total Estimates Total73.5 26.5 100% Four-category school level Primary74.2 25.8 100% Middle75.6 24.4 100% High72.8 27.2 100% Combined62.9 37.1 100% NOTE: Rows may not add up to 100% due to roundingSOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2007-08Computation by NCES QuickStats on 9/21/2017 bebha321 Q41. Gender by Principal's age. Q41. Gender Male(%) Female(%) Total Estimates Total52.447.6100% Principal's age Under 4062.837.2100% Age 40 to 4950.349.7100% Age 50 to 5950.649.4100% Age 60 or older‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcmac72 Total K-12 and ungraded enrollment in school by Program type of school. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total539.7 Program type of school Regular563.2 Special program emphasis697.6 Special Education156.6 Vocational Education546.0 Alternative139.9 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcme13 Q36a. Frequency of problems - physical conflicts by Three-category school level (elementary/secondary/combined). Q36a. Frequency of problems - physical conflicts Happens daily(%) Happens at least once a week(%) Happens at least once a month(%) Happens on occasion(%) Never happens(%) Total Estimates Total4.118.314.759.33.6100% Three-category school level (elementary/secondary/combined) Elementary5.221.213.357.33.1100% Secondary1.612.020.262.33.9100% Combined1.710.310.869.18.0100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcn1b4 Estimated number of full-time equivalent teachers in the school by Total K-12 and ungraded enrollment in school for Program type of school (Regular). Estimated number of full-time equivalent teachers in the school(%>20) Estimates Total79.5 Total K-12 and ungraded enrollment in school 1 to 25017.2 251 to 50086.6 501 to 75099.1 751 to 100099.8 More than 1000‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcpd05 Q15e. Influence on hiring teachers - principal by Q41. Gender. Q15e. Influence on hiring teachers - principal No influence(%) Minor influence(%) Moderate influence(%) Major influence(%) Total Estimates Total0.72.68.788.0100% Q41. Gender Male0.72.77.289.4100% Female0.72.510.386.6100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcd91 Q35. Gender by Principal's age. Q35. Gender Male(%) Female(%) Total Estimates Total43.756.3100% Principal's age Under 4054.345.7100% Age 40 to 4943.756.3100% Age 50 to 5941.358.7100% Age 60 or older‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhc3b2 Total K-12 and ungraded enrollment in school by Program type of school. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total184.9 Program type of school Regular209.6 Special program emphasis198.8 Special Education62.0 Vocational Education‡ Alternative69.9 ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcm103 Q30a. Frequency of problems - physical conflicts by Three-category school level (elementary/secondary/combined). Q30a. Frequency of problems - physical conflicts Happens daily(%) Happens at least once a week(%) Happens at least once a month(%) Happens on occasion(%) Never happens(%) Total Estimates Total1.13.64.368.122.9100% Three-category school level (elementary/secondary/combined) Elementary1.43.84.669.221.0100% Secondary‡2.93.661.232.3100% Combined0.93.33.868.024.0100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcca4 Estimated number of full-time equivalent teachers in the school by Total K-12 and ungraded enrollment in school for Program type of school (Regular). Estimated number of full-time equivalent teachers in the school(%>20) Estimates Total25.6 Total K-12 and ungraded enrollment in school 1 to 2504.9 251 to 50066.6 501 to 75098.9 751 to 1000100.0 More than 1000‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhc085 Q14e. Influence on hiring teachers - principal by Principal's age. Q14e. Influence on hiring teachers - principal No influence(%) Minor influence(%) Moderate influence(%) Major influence(%) Total Estimates Total1.71.65.990.8100% Principal's age Under 403.23.313.979.5100% Age 40 to 491.42.35.291.1100% Age 50 to 592.00.84.592.7100% Age 60 or older‡‡‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcc291 Q41. Gender by Principal's age. Q41. Gender Male(%) Female(%) Total Estimates Total50.349.7100% Principal's age Under 4060.639.4100% Age 40 to 4948.951.1100% Age 50 to 5948.651.4100% Age 60 or older‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcf22 Total K-12 and ungraded enrollment in school by Program type of school. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total454.5 Program type of school Regular483.8 Special program emphasis602.5 Special Education98.3 Vocational Education546.0 Alternative122.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcea03 Q36a. Frequency of problems - physical conflicts by Three-category school level (elementary/secondary/combined). Q36a. Frequency of problems - physical conflicts Happens daily(%) Happens at least once a week(%) Happens at least once a month(%) Happens on occasion(%) Never happens(%) Total Estimates Total3.414.812.261.48.3100% Three-category school level (elementary/secondary/combined) Elementary4.417.511.459.86.9100% Secondary1.410.918.362.27.1100% Combined1.26.36.868.517.1100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcdb4 Estimated number of full-time equivalent teachers in the school by Total K-12 and ungraded enrollment in school for Program type of school (Regular). Estimated number of full-time equivalent teachers in the school(%>20) Estimates Total67.4 Total K-12 and ungraded enrollment in school 1 to 25010.7 251 to 50083.9 501 to 75099.1 751 to 100099.8 More than 1000‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhcm375 Q15e. Influence on hiring teachers - principal by Principal's age. Q15e. Influence on hiring teachers - principal No influence(%) Minor influence(%) Moderate influence(%) Major influence(%) Total Estimates Total0.92.48.088.7100% Principal's age Under 401.52.79.286.6100% Age 40 to 490.53.07.688.9100% Age 50 to 591.11.88.488.7100% Age 60 or older‡‡‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2003-04 Computation by NCES QuickStats on 4/28/2017 bedbhc0a1 Q39. Gender by Principal's age. Q39. Gender Male(%) Female(%) Total Estimates Total49.750.3100% Principal's age Under 4061.538.5100% Age 40 to 4949.450.6100% Age 50 to 5944.855.2100% Age 60 or older‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhne02 Total K-12 and ungraded enrollment in school by Program type of school. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total537.0 Program type of school Regular570.4 Montessori‡ Special program emphasis541.3 Special Education141.5 Career/Technical/Vocational Education676.3 Alternative162.9 Early Childhood Program/Daycare Center‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhnb6c3 Q20A. Frequency of problems - physical conflicts by Three-category school level (elementary/secondary/combined). Q20A. Frequency of problems - physical conflicts Happens daily(%) Happens at least once a week(%) Happens at least once a month(%) Happens on occasion(%) Never happens(%) Total Estimates Total2.213.415.764.34.5100% Three-category school level (elementary/secondary/combined) Elementary2.715.515.262.83.6100% Secondary0.88.818.066.95.4100% Combined1.37.612.270.08.9100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhnc84 Estimated number of full-time equivalent teachers in the school by Total K-12 and ungraded enrollment in school for Program type of school (Regular). Estimated number of full-time equivalent teachers in the school(%>20) Estimates Total80.1 Total K-12 and ungraded enrollment in school 1 to 25018.8 251 to 50086.2 501 to 75099.9 751 to 100099.7 More than 1000‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhnk435 Q12E_4. Influence on hiring teachers - principal by Principal's age. Q12E_4. Influence on hiring teachers - principal No Influence(%) Minor Influence(%) Moderate Influence(%) Major Influence(%) Total Estimates Total0.31.87.490.4100% Principal's age Under 400.61.49.288.8100% Age 40 to 490.22.66.890.5100% Age 50 to 590.31.67.890.3100% Age 60 or older‡‡‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhn8a1 Q36. Gender by Principal's age. Q36. Gender Male(%) Female(%) Total Estimates Total46.753.3100% Principal's age Under 4056.743.3100% Age 40 to 4949.950.1100% Age 50 to 5940.859.2100% Age 60 or older‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhn522 Total K-12 and ungraded enrollment in school by Program type of school. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total183.8 Program type of school Regular209.1 Montessori69.1 Special program emphasis114.8 Special Education59.3 Career/Technical/Vocational Education‡ Alternative67.5 Early Childhood Program/Daycare Center‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhn133 Q20A. Frequency of problems - physical conflicts by Three-category school level (elementary/secondary/combined). Q20A. Frequency of problems - physical conflicts Happens daily(%) Happens at least once a week(%) Happens at least once a month(%) Happens on occasion(%) Never happens(%) {Valid Skip}(%) {Missing}(%) Total Estimates Total1.23.12.864.128.8‡‡100% Three-category school level (elementary/secondary/combined) Elementary1.02.92.365.428.4‡‡100% Secondary‡2.12.864.529.0‡‡100% Combined1.33.93.761.529.5‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhndf4 Estimated number of full-time equivalent teachers in the school by Total K-12 and ungraded enrollment in school for Program type of school (Regular). Estimated number of full-time equivalent teachers in the school(%>20) Estimates Total26.4 Total K-12 and ungraded enrollment in school 1 to 2505.6 251 to 50072.3 501 to 750100.0 751 to 1000100.0 More than 1000‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhn015 Q12E. Influence on hiring teachers - principal by Q36. Gender. Q12E. Influence on hiring teachers - principal No Influence(%) Minor Influence(%) Moderate Influence(%) Major Influence(%) Total Estimates Total0.71.23.994.2100% Q36. Gender Male1.31.65.491.8100% Female‡0.92.696.4100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhnc21 Q39. Gender by Principal's age. Q39. Gender Male(%) Female(%) Total Estimates Total49.051.0100% Principal's age Under 4060.539.5100% Age 40 to 4949.550.5100% Age 50 to 5943.956.1100% Age 60 or older‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhnp672 Total K-12 and ungraded enrollment in school by Program type of school. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total453.6 Program type of school Regular489.9 Montessori69.1 Special program emphasis440.7 Special Education97.8 Career/Technical/Vocational Education676.3 Alternative146.6 Early Childhood Program/Daycare Center‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhn673 Q20A. Frequency of problems - physical conflicts by Three-category school level (elementary/secondary/combined). Q20A. Frequency of problems - physical conflicts Happens daily(%) Happens at least once a week(%) Happens at least once a month(%) Happens on occasion(%) Never happens(%) Total Estimates Total1.910.912.664.310.2100% Three-category school level (elementary/secondary/combined) Elementary2.412.912.663.48.7100% Secondary0.98.016.266.68.2100% Combined1.35.57.365.120.8100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhn9b4 Estimated number of full-time equivalent teachers in the school by Total K-12 and ungraded enrollment in school for Program type of school (Regular). Estimated number of full-time equivalent teachers in the school(%>20) Estimates Total68.1 Total K-12 and ungraded enrollment in school 1 to 25011.8 251 to 50084.5 501 to 75099.9 751 to 100099.7 More than 1000‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhnab45 Q12E_4. Influence on hiring teachers - principal by Q39. Gender. Q12E_4. Influence on hiring teachers - principal No Influence(%) Minor Influence(%) Moderate Influence(%) Major Influence(%) Total Estimates Total0.41.76.691.3100% Q39. Gender Male0.51.57.090.9100% Female0.31.86.291.7100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), School Principals Data File 2007-08 Computation by NCES QuickStats on 4/27/2017 bfdbhnc11 Four-category school level by Charter school identifier. Four-category school level Primary(%) Middle(%) High(%) Combined(%) Total Estimates Total59.716.118.45.8100% Charter school identifier School is a public charter school56.24.615.324.0100% School is not a public charter school59.816.318.45.6100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Library Data File 2003-04 Computation by NCES QuickStats on 12/22/2017 ccpbhp632 Q22 Expenditures - total by Collapsed total K-12 and ungraded enrollment in school. Q22 Expenditures - total(Mean[0]) Estimates Total9,379.4 Collapsed total K-12 and ungraded enrollment in school 1-497,487.9 50-993,871.2 100-1493,912.3 150-1993,880.5 200-3496,431.8 350-4997,516.8 500-7499,681.3 750-99911,008.1 1,000-1,19915,921.8 1,200-1,49920,093.2 1,500-1,99922,298.0 2,000 or more30,531.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Library Data File 2003-04 Computation by NCES QuickStats on 12/22/2017 ccpbhpp2b3 Q41 Percent of teachers that collaborated with LMC staff by Program type of school. Q41 Percent of teachers that collaborated with LMC staff 0%(%) 1% to 20%(%) 21% to 40%(%) 41% to 60%(%) 61% to 80%(%) More than 80%(%) Total Estimates Total18.524.016.516.411.213.4100% Program type of school Regular18.324.316.316.611.113.4100% Special program emphasis14.824.319.718.512.99.8100% Special Education28.714.07.29.610.330.2100% Vocational Education‡28.940.1‡‡‡100% Alternative41.010.015.55.78.019.7100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Library Data File 2003-04 Computation by NCES QuickStats on 12/22/2017 ccpbhac0f4 Total K-12 and ungraded enrollment in school by Q18a Number of books - total. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total577.7 Q18a Number of books - total None‡ 1 to 5,000294.5 5,001 to 10,000459.1 10,001 to 15,000644.2 More than 15,000977.7 ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Library Data File 2003-04 Computation by NCES QuickStats on 12/22/2017 ccpbhafga35 Q14b Number of computers with internet by Percentage of enrolled students approved for the NSLP at school. Q14b Number of computers with internet None(%) 1 to 5(%) 6 to 10(%) 11 to 15(%) 16 to 30(%) More than 30(%) Total Estimates Total1.731.225.713.518.19.8100% Percentage of enrolled students approved for the NSLP at school 0%‡‡‡‡‡‡100% >0% to 15%1.420.921.615.421.719.0100% >15% to 30%1.124.327.416.019.112.2100% >30% to 50%1.431.226.112.520.18.7100% >50% to 75%1.738.726.911.915.55.2100% More than 75%2.442.127.912.612.62.5100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Library Data File 2003-04 Computation by NCES QuickStats on 12/22/2017 ccpbhakg431 Four-category school level by Charter school identifier. Four-category school level Primary(%) Middle(%) High(%) Combined(%) Total Estimates Total59.016.818.85.4100% Charter school identifier School is a public charter school51.215.419.314.2100% School is not a public charter school59.116.918.75.2100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Library Data File 2007-08 Computation by NCES QuickStats on 12/22/2017 ccpbhbm162 Q33 Expenditures - total by Collapsed total K-12 and ungraded enrollment in school. Q33 Expenditures - total(Mean[0]) Estimates Total9,357.8 Collapsed total K-12 and ungraded enrollment in school 1-494,065.5 50-992,743.3 100-1495,320.3 150-1994,659.1 200-3496,000.6 350-4997,417.7 500-7499,221.1 750-99912,969.1 1,000-1,19916,964.7 1,200-1,49920,341.3 1,500-1,99921,278.7 2,000 or more27,271.8 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Library Data File 2007-08 Computation by NCES QuickStats on 12/22/2017 ccpbhb3c3 Q32a(1) Number of books - total by Program type of school. Q32a(1) Number of books - total None(%) 1 to 5,000(%) 5,001 to 10,000(%) 10,001 to 15,000(%) More than 15,000(%) Total Estimates Total0.610.632.834.521.4100% Program type of school Regular0.69.233.035.321.9100% Special program emphasis‡13.136.030.819.9100% Special Education‡47.417.87.422.4100% Career/Technical/Vocational Education‡26.422.528.714.6100% Alternative‡51.226.716.54.0100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Library Data File 2007-08 Computation by NCES QuickStats on 12/22/2017 ccpbhbed4 Total K-12 and ungraded enrollment in school by Q32b(1) Number of audio/video - total. Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total577.0 Q32b(1) Number of audio/video - total None496.0 1 to 150478.2 151 to 300507.6 301 to 500544.9 501 to 1,000662.7 1,001 to 1,500858.2 More than 1,500972.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Library Data File 2007-08 Computation by NCES QuickStats on 12/22/2017 ccpbhb1e5 Q22b Number of computers with internet by Percentage of enrolled students approved for the NSLP at school. Q22b Number of computers with internet None(%) 1 to 5(%) 6 to 10(%) 11 to 15(%) 16 to 30(%) More than 30(%) Total Estimates Total0.526.725.413.721.012.7100% Percentage of enrolled students approved for the NSLP at school 0%‡‡‡‡‡‡100% >0% to 15%‡19.321.815.323.420.0100% >15% to 30%0.420.925.514.422.516.4100% >30% to 50%0.324.523.212.626.013.6100% >50% to 75%0.330.128.014.317.310.1100% More than 75%0.936.928.712.615.95.1100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Library Data File 2007-08 Computation by NCES QuickStats on 12/22/2017 ccpbhb481 Number of minority students in the district by Collapsed urban-centric district locale code. Number of minority students in the district Zero to 100(%) 101 to 300(%) 301 to 1,000(%) 1,001 to 5,000(%) More than 5,000(%) Total Estimates Total54.215.814.810.74.4100% Collapsed urban-centric district locale code Large or mid-size central city23.916.218.818.622.5100% Urban fringe of a large or mid-size central city42.918.319.514.35.0100% Small town/rural70.913.39.75.80.3100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 11/17/2017 bhnbhaf352 Q17. Teachers association or union agreement by Q44. School choice program for students to enroll in another District. Q17. Teachers association or union agreement Yes, collective bargaining(%) Yes, meet-and-confer(%) No(%) Total Estimates Total57.86.036.2100% Q44. School choice program for students to enroll in another District Yes76.37.316.4100% No‡‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 11/17/2017 bhnbha473 Number of schools in district, post-collapsing by Q45. School choice program for students to enroll in a private school. Number of schools in district, post-collapsing(Mean[0]) Estimates Total5.8 Q45. School choice program for students to enroll in a private school Yes10.4 No‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 11/17/2017 bhnbhaf434 Q2. Total student enrollment- all grade levels by Q10. Count of Principals Employed . Q2. Total student enrollment- all grade levels(%>3000) Estimates Total22.1 Q10. Count of Principals Employed Zero to two0.3 Three to five8.3 Six to ten69.3 Eleven to twenty98.1 More than twenty99.6 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 11/17/2017 bhnbha2d5 Q8. Count of Teachers Employed - FTE by Percentage of students in district approved for the National School Lunch Program. Q8. Count of Teachers Employed - FTE 1 to 100(%) 101 to 200(%) 201 to 500(%) 501 to 1,000(%) More than 1,000(%) Total Estimates Total60.917.714.04.52.9100% Percentage of students in district approved for the National School Lunch Program 0% to 20%49.221.721.35.22.6100% More than 20% to 40%55.922.414.24.72.8100% More than 40% to 60%64.815.111.84.93.5100% More than 60% to 80%66.911.113.84.83.5100% More than 80%62.319.810.53.53.8100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 11/17/2017 bhnbha9b1 Q24b. Principal salary - Highest paid full-time by Collapsed urban-centric district locale code. Q24b. Principal salary - Highest paid full-time(Mean[0]) Estimates Total88,550.7 Collapsed urban-centric district locale code City104,246.1 Suburb109,838.4 Town83,658.4 Rural76,619.8 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/14/2017 bepbhn432 Q12. Count of Principals Employed by Number of schools in district, post-collapsing. Q12. Count of Principals Employed Zero to two(%) Three to five(%) Six to ten(%) Eleven to twenty(%) More than twenty(%) Total Estimates Total48.227.814.35.83.9100% Number of schools in district, post-collapsing 1 to 293.56.00.4‡‡100% 3 to 517.177.65.10.2‡100% 6 to 102.720.273.43.5‡100% 11 to 200.81.618.276.33.1100% More than 201.6‡‡10.687.2100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/14/2017 bepbhaab93 Q42. Community service requirement for High school graduates - hours by Q2. Total student enrollment- all grade levels. Q42. Community service requirement for High school graduates - hours(Avg>0) Estimates Total32.8 Q2. Total student enrollment- all grade levels 1 to 1,00030.5 1,001 to 3,00029.6 3,001 to 7,00028.9 7,001 to 10,000‡ More than 10,00038.9 ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/14/2017 bepbha994 Q10. Teachers association or union agreement by Q3. Total student enrollment- K-12 grade levels. Q10. Teachers association or union agreement Yes, meet-and-confer(%) Yes, collective bargaining(%) No(%) Total Estimates Total10.953.535.6100% Q3. Total student enrollment- K-12 grade levels 1 to 1,00010.542.746.8100% 1,001 to 2,00011.267.321.5100% 2,001 to 5,00011.066.722.3100% 5,001 to 10,00011.164.224.7100% More than 10,00012.959.727.4100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/14/2017 bepbha695 Q30c. Salary Incentive to retain teachers in a less desirable location by Percentage of students in district approved for the National School Lunch Program. Q30c. Salary Incentive to retain teachers in a less desirable location Yes(%) No(%) Total Estimates Total100.0‡100% Percentage of students in district approved for the National School Lunch Program 0% to 20%‡‡100% More than 20% to 40%100.0‡100% More than 40% to 60%100.0‡100% More than 60% to 80%100.0‡100% More than 80%100.0‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Computation by NCES QuickStats on 12/14/2017 bepbha5a1 Percentage of enrolled students with an IEP by Collapsed urban-centric school locale code. Percentage of enrolled students with an IEP(Mean[0]) Estimates Total12.7 Collapsed urban-centric school locale code Large or mid-size central city12.5 Urban fringe of a large or mid-size central city12.6 Small town/rural12.9 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhp072 Three-category level of school based on grade levels offered by Collapsed urban-centric school locale code. Three-category level of school based on grade levels offered Elementary(%) Secondary(%) Combined(%) Total Estimates Total67.719.412.9100% Collapsed urban-centric school locale code Large or mid-size central city72.816.810.5100% Urban fringe of a large or mid-size central city70.018.811.3100% Small town/rural57.623.419.0100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdhmb43 Percentage of enrolled students approved for the NSLP at school by Collapsed urban-centric school locale code. Percentage of enrolled students approved for the NSLP at school(Avg>0) Estimates Total44.1 Collapsed urban-centric school locale code Large or mid-size central city57.8 Urban fringe of a large or mid-size central city34.8 Small town/rural47.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdk944 Percentage of enrolled students who are LEP by Three-category level of school based on grade levels offered. Percentage of enrolled students who are LEP(%>2) Estimates Total30.7 Three-category level of school based on grade levels offered Elementary35.3 Secondary27.2 Combined12.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdmec5 Q26h(3)/Q36g(3) Number of full-time special education instructional aides by Three-category level of school based on grade levels offered. Q26h(3)/Q36g(3) Number of full-time special education instructional aides Zero(%) One(%) Two(%) Three to five(%) More than five(%) Total Estimates Total49.310.210.217.912.4100% Three-category level of school based on grade levels offered Elementary46.910.511.019.911.7100% Secondary44.410.89.917.317.7100% Combined69.27.56.58.28.5100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdpn9a1 Percentage of enrolled students with an IEP by Collapsed urban-centric school locale code. Percentage of enrolled students with an IEP(Mean[0]) Estimates Total12.7 Collapsed urban-centric school locale code Large or mid-size central city12.5 Urban fringe of a large or mid-size central city12.6 Small town/rural12.9 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhp072 Three-category level of school based on grade levels offered by Collapsed urban-centric school locale code. Three-category level of school based on grade levels offered Elementary(%) Secondary(%) Combined(%) Total Estimates Total67.719.412.9100% Collapsed urban-centric school locale code Large or mid-size central city72.816.810.5100% Urban fringe of a large or mid-size central city70.018.811.3100% Small town/rural57.623.419.0100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdhmb43 Percentage of enrolled students approved for the NSLP at school by Collapsed urban-centric school locale code. Percentage of enrolled students approved for the NSLP at school(Avg>0) Estimates Total44.1 Collapsed urban-centric school locale code Large or mid-size central city57.8 Urban fringe of a large or mid-size central city34.8 Small town/rural47.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdk944 Percentage of enrolled students who are LEP by Three-category level of school based on grade levels offered. Percentage of enrolled students who are LEP(%>2) Estimates Total30.7 Three-category level of school based on grade levels offered Elementary35.3 Secondary27.2 Combined12.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdmec5 Q26h(3)/Q36g(3) Number of full-time special education instructional aides by Three-category level of school based on grade levels offered. Q26h(3)/Q36g(3) Number of full-time special education instructional aides Zero(%) One(%) Two(%) Three to five(%) More than five(%) Total Estimates Total49.310.210.217.912.4100% Three-category level of school based on grade levels offered Elementary46.910.511.019.911.7100% Secondary44.410.89.917.317.7100% Combined69.27.56.58.28.5100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdpn9a1 Percentage of enrolled students with an IEP by Collapsed urban-centric school locale code. Percentage of enrolled students with an IEP(Mean[0]) Estimates Total12.7 Collapsed urban-centric school locale code Large or mid-size central city12.5 Urban fringe of a large or mid-size central city12.6 Small town/rural12.9 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhp072 Three-category level of school based on grade levels offered by Collapsed urban-centric school locale code. Three-category level of school based on grade levels offered Elementary(%) Secondary(%) Combined(%) Total Estimates Total67.719.412.9100% Collapsed urban-centric school locale code Large or mid-size central city72.816.810.5100% Urban fringe of a large or mid-size central city70.018.811.3100% Small town/rural57.623.419.0100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdhmb43 Percentage of enrolled students approved for the NSLP at school by Collapsed urban-centric school locale code. Percentage of enrolled students approved for the NSLP at school(Avg>0) Estimates Total44.1 Collapsed urban-centric school locale code Large or mid-size central city57.8 Urban fringe of a large or mid-size central city34.8 Small town/rural47.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdk944 Percentage of enrolled students who are LEP by Three-category level of school based on grade levels offered. Percentage of enrolled students who are LEP(%>2) Estimates Total30.7 Three-category level of school based on grade levels offered Elementary35.3 Secondary27.2 Combined12.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdmec5 Q26h(3)/Q36g(3) Number of full-time special education instructional aides by Three-category level of school based on grade levels offered. Q26h(3)/Q36g(3) Number of full-time special education instructional aides Zero(%) One(%) Two(%) Three to five(%) More than five(%) Total Estimates Total49.310.210.217.912.4100% Three-category level of school based on grade levels offered Elementary46.910.511.019.911.7100% Secondary44.410.89.917.317.7100% Combined69.27.56.58.28.5100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2003-04 Computation by NCES QuickStats on 11/1/2017 bnbhdpn9a1 Percentage of enrolled students with an IEP by Collapsed urban-centric school locale code. Percentage of enrolled students with an IEP(Mean[0]) Estimates Total13.5 Collapsed urban-centric school locale code City13.3 Suburb14.2 Town12.5 Rural13.4 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhn672 Three-category level of school based on grade levels offered by Collapsed urban-centric school locale code. Three-category level of school based on grade levels offered Elementary(%) Secondary(%) Combined(%) Total Estimates Total66.220.713.0100% Collapsed urban-centric school locale code City69.419.311.4100% Suburb69.818.711.5100% Town64.225.310.6100% Rural60.921.917.1100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhpdbd3 Percentage of enrolled students approved for the NSLP at school by Collapsed urban-centric school locale code. Percentage of enrolled students approved for the NSLP at school(%>15) Estimates Total81.6 Collapsed urban-centric school locale code City87.9 Suburb68.3 Town88.0 Rural85.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhp604 Percentage of enrolled students who are LEP by Three-category level of school based on grade levels offered. Percentage of enrolled students who are LEP(Avg>0) Estimates Total11.7 Three-category level of school based on grade levels offered Elementary13.1 Secondary7.3 Combined11.0 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhp115 Q30g(3)/Q46h(3) Number of full-time special education instructional aides by Three-category level of school based on grade levels offered. Q30g(3)/Q46h(3) Number of full-time special education instructional aides Zero(%) One(%) Two(%) Three to five(%) More than five(%) Total Estimates Total47.69.99.118.415.0100% Three-category level of school based on grade levels offered Elementary45.310.19.820.514.4100% Secondary43.59.19.218.020.3100% Combined66.110.15.48.510.0100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/30/2017 dbmbhdf861 Percentage of enrolled students with an IEP by Collapsed urban-centric school locale code. Percentage of enrolled students with an IEP(Mean[0]) Estimates Total13.5 Collapsed urban-centric school locale code City13.3 Suburb14.2 Town12.5 Rural13.4 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhn672 Three-category level of school based on grade levels offered by Collapsed urban-centric school locale code. Three-category level of school based on grade levels offered Elementary(%) Secondary(%) Combined(%) Total Estimates Total66.220.713.0100% Collapsed urban-centric school locale code City69.419.311.4100% Suburb69.818.711.5100% Town64.225.310.6100% Rural60.921.917.1100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhpdbd3 Percentage of enrolled students approved for the NSLP at school by Collapsed urban-centric school locale code. Percentage of enrolled students approved for the NSLP at school(%>15) Estimates Total81.6 Collapsed urban-centric school locale code City87.9 Suburb68.3 Town88.0 Rural85.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhp604 Percentage of enrolled students who are LEP by Three-category level of school based on grade levels offered. Percentage of enrolled students who are LEP(Avg>0) Estimates Total11.7 Three-category level of school based on grade levels offered Elementary13.1 Secondary7.3 Combined11.0 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhp115 Q30g(3)/Q46h(3) Number of full-time special education instructional aides by Three-category level of school based on grade levels offered. Q30g(3)/Q46h(3) Number of full-time special education instructional aides Zero(%) One(%) Two(%) Three to five(%) More than five(%) Total Estimates Total47.69.99.118.415.0100% Three-category level of school based on grade levels offered Elementary45.310.19.820.514.4100% Secondary43.59.19.218.020.3100% Combined66.110.15.48.510.0100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/30/2017 dbmbhdf861 Percentage of enrolled students with an IEP by Collapsed urban-centric school locale code. Percentage of enrolled students with an IEP(Mean[0]) Estimates Total13.5 Collapsed urban-centric school locale code City13.3 Suburb14.2 Town12.5 Rural13.4 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhn672 Three-category level of school based on grade levels offered by Collapsed urban-centric school locale code. Three-category level of school based on grade levels offered Elementary(%) Secondary(%) Combined(%) Total Estimates Total66.220.713.0100% Collapsed urban-centric school locale code City69.419.311.4100% Suburb69.818.711.5100% Town64.225.310.6100% Rural60.921.917.1100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhpdbd3 Percentage of enrolled students approved for the NSLP at school by Collapsed urban-centric school locale code. Percentage of enrolled students approved for the NSLP at school(%>15) Estimates Total81.6 Collapsed urban-centric school locale code City87.9 Suburb68.3 Town88.0 Rural85.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhp604 Percentage of enrolled students who are LEP by Three-category level of school based on grade levels offered. Percentage of enrolled students who are LEP(Avg>0) Estimates Total11.7 Three-category level of school based on grade levels offered Elementary13.1 Secondary7.3 Combined11.0 SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/18/2017 bkmbhp115 Q30g(3)/Q46h(3) Number of full-time special education instructional aides by Three-category level of school based on grade levels offered. Q30g(3)/Q46h(3) Number of full-time special education instructional aides Zero(%) One(%) Two(%) Three to five(%) More than five(%) Total Estimates Total47.69.99.118.415.0100% Three-category level of school based on grade levels offered Elementary45.310.19.820.514.4100% Secondary43.59.19.218.020.3100% Combined66.110.15.48.510.0100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Schools Data File 2007-08 Computation by NCES QuickStats on 10/30/2017 dbmbhdf861 Undergraduate degree program by Total grants. Undergraduate degree program Certificate(%) Associate's degree(%) Bachelor's degree(%) Not in a degree program or others(%) Total Estimates Total8.941.446.73.0100% Total grants 010.149.135.15.7100% $1-2,09910.652.034.03.4100% $2,100-4,89911.648.138.91.4100% $4,900-8,7999.238.751.40.8100% $8,800 or more1.38.789.60.4100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES QuickStats on 3/6/2018 gcbkpb9a2 Federal Pell grant by Institution sector (4 with multiple). Federal Pell grant(Mean[0]) Estimates Total1,460.4 Institution sector (4 with multiple) Public 4-year1,555.0 Private not-for-profit 4-year1,439.6 Public 2-year1,094.7 Private for profit2,371.2 Others or attended more than one school1,670.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES QuickStats on 3/6/2018 gcbkaf8c3 Gender by Attendance intensity (all schools). Gender Male(%) Female(%) Total Estimates Total43.556.5100% Attendance intensity (all schools) Exclusively full-time43.856.2100% Exclusively part-time42.757.3100% Mixed full-time and part-time44.155.9100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES QuickStats on 3/6/2018 gcbka114 Total federal aid (excludes Veterans'/DOD) by Race/ethnicity (with multiple). Total federal aid (excludes Veterans'/DOD)(Mean[0]) Estimates Total4,681.4 Race/ethnicity (with multiple) White4,502.2 Black or African American6,464.9 Hispanic or Latino4,223.1 Asian3,308.7 American Indian or Alaska Native4,115.1 Native Hawaiian/other Pacific Islander4,346.3 More than one race5,174.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES QuickStats on 3/6/2018 gcbkafe5 Citizenship by Total aid amount. Citizenship US citizen(%) Resident alien(%) Foreign or international student(%) Total Estimates Total92.74.62.8100% Total aid amount 088.75.06.3100% $1-3,59992.45.71.9100% $3,600-8,19993.55.41.1100% $8,200-16,59995.23.90.9100% $16,600 or more95.62.51.8100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES QuickStats on 3/6/2018 gcbka541 Tuition and fees paid by Institution type: Graduate (with multiple). Tuition and fees paid Less than $5,000(%) $5,000-$9,999(%) $10,000-$19,999(%) $20,000 or more(%) Total Estimates Total27.025.923.623.5100% Institution type: Graduate (with multiple) Public 4-year non-doctorate-granting55.336.17.11.5100% Public 4-year doctorate-granting28.227.526.118.2100% Private nonprofit 4-year non-doctorate-granting39.327.321.112.3100% Private nonprofit 4-year doctorate-granting20.421.321.337.0100% Private for-profit 4-year25.330.832.011.9100% Attended more than one institution‡‡‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES QuickStats on 3/6/2018 gcbkhn4c2 Age as of 12/31/2015 by Total grants. Age as of 12/31/2015(Mean[0]) Estimates Total32.3 Total grants 033.0 $1-2,09933.1 $2,100-4,89932.9 $4,900-8,79931.3 $8,800 or more28.9 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES QuickStats on 3/6/2018 gcbkfac3 Total loans by Graduate degree program. Total loans(Avg>0) Estimates Total23,366.5 Graduate degree program Master's degree18,569.9 Post-baccalaureate or post-master's certificate18,612.0 Doctor's degree - research/scholarship17,984.2 Doctor's degree - professional practice45,135.7 Doctor's degree - other18,626.5 Not in a degree program‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES QuickStats on 3/6/2018 gcbkf024 Attendance intensity (all schools) by Employer aid. Attendance intensity (all schools) Exclusively full-time(%) Exclusively part-time(%) Mixed full-time and part-time(%) Total Estimates Total42.736.920.5100% Employer aid 045.433.820.8100% $1-1,99921.168.310.6100% $2,000-3,99923.853.422.8100% $4,000-6,49923.759.916.4100% $6,500 or more30.645.923.5100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES QuickStats on 3/6/2018 gcbkfd6c5 State aid total by Institution sector (11 categories). State aid total 0(%) $100-999(%) $1,000-2,599(%) $2,600-5,499(%) $5,500 or more(%) Total Estimates Total97.60.50.70.60.6100% Institution sector (11 categories) Public less-than-2-year‡‡‡‡‡100% Public 2-year‡‡‡‡‡100% Public 4-year non-doc-granting, primarily subbacc99.1‡‡‡‡100% Public 4-year non-doc-granting, primarily bacc97.40.40.40.61.2100% Public 4-year doctorate-granting96.20.80.81.30.9100% Private nonprofit less than 4-year‡‡‡‡‡100% Private nonprofit 4-year nondoctorate99.40.20.20.10.1100% Private nonprofit 4-year doctorate-granting98.30.30.80.10.4100% Private for profit less-than-2-year‡‡‡‡‡100% Private for profit 2-year‡‡‡‡‡100% Private for profit 4-year99.70.10.10.1‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES QuickStats on 3/6/2018 gcbkfd81 Total school-related yearly earnings by Highest degree earned. Total school-related yearly earnings(Mean[0]) Estimates Total56,477.8 Highest degree earned Associate's degree or no college degree52,579.0 Bachelor's degree48,949.4 Master's degree61,437.2 Education specialist or Certificate of Advanced Graduate Studies64,171.8 Doctorate or Professional degree67,942.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Teacher Data File, 2015–16 Computation by NCES QuickStats on 2/13/2018 bdbbkff2e2 Number of students taught by teachers of departmentalized classes by Four-category school level (primary/middle/high/combined). Number of students taught by teachers of departmentalized classes 1 to 50(%) 51 to 100(%) 101 to 150(%) More than 150(%) Total Estimates Total11.031.237.320.5100% Four-category school level (primary/middle/high/combined) Primary20.137.324.018.6100% Middle6.527.243.123.2100% High9.930.739.020.4100% Combined18.337.529.214.9100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Teacher Data File, 2015–16 Computation by NCES QuickStats on 2/26/2018 cgbbkh253 Average class size for teachers of departmentalized classes by Census region, based on ANSI state code. Average class size for teachers of departmentalized classes(Avg>0) Estimates Total26.0 Census region, based on ANSI state code Northeast23.4 Midwest25.6 South25.8 West29.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Teacher Data File, 2015–16 Computation by NCES QuickStats on 2/13/2018 bdbbkfbf4 Number of students taught by teachers of self-contained classes by Total K-12 and ungraded enrollment in school. Number of students taught by teachers of self-contained classes 1 to 10(%) 11 to 20(%) 21 to 30(%) More than 30(%) Total Estimates Total9.236.949.94.0100% Total K-12 and ungraded enrollment in school 1 to 35013.949.834.32.1100% 351 to 6006.837.752.43.1100% 601 to 1,0007.128.858.35.7100% More than 1,000‡‡‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Teacher Data File, 2015–16 Computation by NCES QuickStats on 2/26/2018 cgbbkk435 Q2-9 Number of students enrolled in class taught by Program type of school. Q2-9 Number of students enrolled in class taught(%>20) Estimates Total57.0 Program type of school Regular57.5 Special program emphasis68.1 Special Education1.7 Career/Technical/Vocational Education53.1 Alternative/other40.9 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Teacher Data File, 2015–16 Computation by NCES QuickStats on 2/13/2018 bdbbkf841 Principal's age by Q2-1 Educational goals: first most important. Principal's age Between 23 and 30(%) Between 31 and 40(%) Between 41 and 50(%) Between 51 and 60(%) Older than 60(%) Total Estimates Total1.021.842.427.67.2100% Q2-1 Educational goals: first most important Building basic literacy skills ( reading, math, writing, speaking)0.720.243.128.08.0100% Encouraging academic excellence0.722.341.228.47.5100% Preparing students for postsecondary education2.825.242.026.43.6100% Promoting occupational or vocational skills‡20.032.942.3‡100% Promoting good work habits and self-discipline2.023.040.627.56.8100% Promoting personal growth (self-esteem, self-knowledge, etc.)1.928.842.822.04.5100% Promoting human relations skills‡20.548.225.15.9100% Promoting specific moral values‡28.546.920.24.5100% Promoting multicultural awareness or understanding‡‡‡‡‡100% Fostering religious or spiritual development‡‡‡‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Principal Data File, 2015–16 Computation by NCES QuickStats on 2/13/2018 bdbbkfkhec2 Q1-4 Participated in program for aspiring principals by Q1-7 Highest degree earned. Q1-4 Participated in program for aspiring principals No(%) Yes(%) Total Estimates Total42.357.7100% Q1-7 Highest degree earned Associate degree‡‡100% Bachelor's degree (B.A., B.S., etc.)60.539.5100% Master's degree (M.A., M.A.T., M.B.A., M.Ed., M.S., etc.)42.457.6100% Education specialist or professional diploma (at least one year beyond master's level)41.258.8100% Doctorate or first professional degree (Ph.D., Ed.D., M.D., L.L.B., J.D., D.D.S.)40.359.7100% Do not have a degree‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Principal Data File, 2015–16 Computation by NCES QuickStats on 2/16/2018 bgbbkb303 Estimated number of full-time equivalent teachers in the school by Q3-1a Frequency of Problems: physical conflicts between students. Estimated number of full-time equivalent teachers in the school(Avg>0) Estimates Total35.7 Q3-1a Frequency of Problems: physical conflicts between students Happens daily33.4 Happens at least once a week36.8 Happens at least once a month39.6 Happens on occasion35.5 Never happens26.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Principal Data File, 2015–16 Computation by NCES QuickStats on 2/13/2018 bdbbkfpb84 Four-category school level (primary/middle/high/combined) by Collapsed total K-12 and ungraded enrollment in school. Four-category school level (primary/middle/high/combined) Primary(%) Middle(%) High(%) Combined(%) Total Estimates Total55.715.420.08.9100% Collapsed total K-12 and ungraded enrollment in school 1-4923.65.230.540.7100% 50-9936.46.127.929.7100% 100-14942.06.229.522.3100% 150-19948.114.123.614.1100% 200-34964.312.913.69.2100% 350-49971.711.412.34.6100% 500-74967.917.510.14.5100% 750-99946.630.319.14.1100% 1,000-1,19924.932.537.05.6100% 1,200-1,4998.724.159.18.1100% 1,500-1,999‡6.886.06.4100% 2,000 or more‡‡93.35.1100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Principal Data File, 2015–16 Computation by NCES QuickStats on 2/16/2018 bgbbkbb9b5 Q1-1 Number of years of teaching experience prior to becoming a principal by Q1-7 Highest degree earned. Q1-1 Number of years of teaching experience prior to becoming a principal(%>5) Estimates Total85.3 Q1-7 Highest degree earned Associate degree‡ Bachelor's degree (B.A., B.S., etc.)73.1 Master's degree (M.A., M.A.T., M.B.A., M.Ed., M.S., etc.)85.5 Education specialist or professional diploma (at least one year beyond master's level)87.3 Doctorate or first professional degree (Ph.D., Ed.D., M.D., L.L.B., J.D., D.D.S.)81.1 Do not have a degree‡ ‡ Reporting standards not met. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Principal Data File, 2015–16 Computation by NCES QuickStats on 2/13/2018 bdbbkfa2b1 Estimated number of students per FTE teacher in the school by Collapsed school locale code. Estimated number of students per FTE teacher in the school(Mean[0]) Estimates Total15.5 Collapsed school locale code City16.3 Suburb15.8 Town15.7 Rural14.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Data File, 2015–16 Computation by NCES QuickStats on 2/13/2018 bdbbkf632 Number of continuing teachers by Q2-6a Number of newly hired K-12 teachers. Number of continuing teachers 1 to 15(%) 16 to 30(%) 31 to 45(%) 46 to 60(%) More than 60(%) Total Estimates Total20.041.427.710.9‡100% Q2-6a Number of newly hired K-12 teachers Zero36.539.217.96.4‡100% One29.140.325.05.6‡100% Two to four16.945.429.18.6‡100% Five to ten10.939.132.517.6‡100% More than ten‡‡‡‡‡100% ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Data File, 2015–16 Computation by NCES QuickStats on 2/13/2018 bdbbkf093 Percentage of enrolled students with an IEP by School locale code. Percentage of enrolled students with an IEP(Avg>0) Estimates Total13.9 School locale code City, Large13.9 City, Midsize12.8 City, Small15.3 Suburb, Large13.5 Suburb, Midsize12.8 Suburb, Small14.2 Town, Fringe15.0 Town, Distant15.5 Town, Remote13.3 Rural, Fringe13.6 Rural, Distant14.2 Rural, Remote14.0 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Data File, 2015–16 Computation by NCES QuickStats on 2/13/2018 bdbbkfc24 Rate of limited English proficiency students who graduated within four years by School-wide Title I eligibility flag. Rate of limited English proficiency students who graduated within four years Zero(%) >0% to 50%(%) >50% to 75%(%) >75% to <100%(%) 100%(%) Total Estimates Total16.517.620.913.631.3100% School-wide Title I eligibility flag School is eligible for school-wide Title 1 program19.620.020.414.125.9100% School is not eligible for school-wide Title 1 program15.518.616.58.740.7100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Data File, 2015–16 Computation by NCES QuickStats on 2/13/2018 bdbbkf495 Q2-3a Number of full-time principals by Q4-4a School participates in the National School Lunch Program. Q2-3a Number of full-time principals(%>1) Estimates Total3.2 Q4-4a School participates in the National School Lunch Program No4.4 Yes3.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Data File, 2015–16 Computation by NCES QuickStats on 2/13/2018 bdbbkf801 Q33a. How often cyberbullying among students by School grades offered. Q33a. How often cyberbullying among students At least once a week(%) Less than once a week(%) Total Estimates Total12.0 88.0 100% School grades offered Primary4.2 95.8 100% Middle25.6 74.4 100% High25.9 74.1 100% Combined10.6 ! 89.4 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015ñ16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES QuickStats on 3/20/2018cbcbke9f2 Q28. Number of hate crimes by School size categories. Q28. Number of hate crimes Hate Crimes Recorded(%) No Hate Crimes Recorded(%) Total Estimates Total1.0 99.0 100% School size categories < 300á 99.5 100% 300 - 4991.1 ! 98.9 100% 500 - 9990.8 99.2 100% 1,000 +2.8 97.2 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.á Reporting standards not met.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015ñ16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES QuickStats on 3/20/2018cbcbke473 Total number of incidents reported to police by Q42. Level of crime where school is located. Total number of incidents reported to police(Mean[0]) Estimates Total5.4 Q42. Level of crime where school is located High level of crime6.7 Moderate level of crime8.1 Low level of crime4.5 SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015ñ16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES QuickStats on 3/20/2018cdbkg594 Q32d. Disciplinary occurrences: Student harassment based on sexual orientation by School grades offered. Happens at least once a month(%) Happens less than once a month(%) Total Estimates Total2.2 97.8 100% School grades offered Primaryá 99.5 100% Middle4.7 95.3 100% High5.2 94.8 100% Combined3.8 ! 96.2 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.á Reporting standards not met.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015ñ16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES QuickStats on 3/20/2018cbcbkea35 Total number of disciplinary actions recorded for use or possession of a firearm or explosive device by School size categories. Total number of disciplinary actions recorded for use or possession of a firearm or explosive device(%>1) Estimates Total1.3 School size categories < 300á 300 - 4991.5 ! 500 - 9990.9 ! 1,000 +3.2 ! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.á Reporting standards not met.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2015ñ16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES QuickStats on 3/20/2018cdbkge91 Q92 Enrolled in language program by Q91 Language spoken by child at home. Q92 Enrolled in language program Yes(%) No(%) Total Estimates Total11.388.7100% Q91 Language spoken by child at home Child has not started to speak‡‡100% English‡‡100% Spanish15.984.1100% Language other than English or Spanish9.590.5100% English and Spanish equally10.090.0100% English and another language equally3.4 !96.6100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012. Computation by NCES QuickStats on 6/2/2018 cfbkan6b2 Q22 Hours each week child receives non-relative care by Child currently has disability. Q22 Hours each week child receives non-relative care(Mean[0]) Estimates Total26.3 Child currently has disability Currently has a disability25.2 Does not currently have a disability26.4 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012. Computation by NCES QuickStats on 6/2/2018 cfbkab6c3 Number of siblings by Educational attainment of child's parent or guardian. Number of siblings Zero(%) One(%) Two(%) More than two(%) Total Estimates Total32.936.818.911.4100% Educational attainment of child's parent or guardian Less than high school credential28.827.422.321.4100% High school graduate or equivalent32.336.519.112.1100% Vocational/technical school after HS34.236.419.110.4100% College graduate34.940.617.76.7100% Graduate or professional school32.842.516.38.4100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012. Computation by NCES QuickStats on 6/2/2018 cfbkah554 Number of household members younger than age 18 by Q133 Total household income. Number of household members younger than age 18(Mean[0]) Estimates Total2.3 Q133 Total household income $0 to $10,0002.4 $10,001 to $20,0002.5 $20,001 to $30,0002.5 $30,001 to $40,0002.5 $40,001 to $50,0002.4 $50,001 to $60,0002.1 $60,001 to $75,0002.2 $75,001 to $100,0002.1 $100,001 to $150,0002.2 $150,001 or more2.2 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012. Computation by NCES QuickStats on 6/2/2018 cfbkac945 Q97 Language spoken most often at home by first parent or guardian by Work status of child's first parent or guardian. Q97 Language spoken most often at home by first parent or guardian English(%) Spanish(%) Language other than English or Spanish(%) English and Spanish equally(%) English and another language equally(%) Total Estimates Total22.438.214.913.611.0100% Work status of child's first parent or guardian Working 35 hours or more per week28.129.718.911.711.6100% Working less than 35 hours per week20.037.211.417.813.6100% Looking for work8.853.17.8 !22.18.3 !100% Not in the labor force18.946.212.512.59.9100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012. Computation by NCES QuickStats on 6/2/2018 cfbka681 Q95 Enrolled in language program by Q94 Language spoken by child at home. Q95 Enrolled in language program Yes(%) No(%) Total Estimates Total11.089.0100% Q94 Language spoken by child at home Child has not started to speak‡‡100% English‡‡100% Spanish9.190.9100% A language other than English or Spanish21.4 !78.6100% English and Spanish equally9.590.5100% English and another language equally7.3 !92.7100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016. Computation by NCES QuickStats on 6/4/2018 efbkhff2 Q22 Hours each week child receives non-relative care by Child currently has disability. Q22 Hours each week child receives non-relative care(Mean[0]) Estimates Total27.0 Child currently has disability Currently has a disability26.1 Does not currently have a disability27.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016. Computation by NCES QuickStats on 6/4/2018 efbkh633 Number of siblings by Educational attainment of child's parent or guardian. Number of siblings 0(%) 1(%) 2(%) 3 or more(%) Total Estimates Total29.037.820.612.7100% Educational attainment of child's parent or guardian Less than high school credential21.321.131.825.8100% High school graduate or equivalent32.437.120.69.8100% Vocational/technical school after HS29.036.420.614.0100% College graduate30.244.216.88.8100% Graduate or professional school28.644.617.39.5100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016. Computation by NCES QuickStats on 6/4/2018 efbkh114 Number of household members younger than age 18 by Q138 Total household income. Number of household members younger than age 18(Mean[0]) Estimates Total2.3 Q138 Total household income $0 to $10,0002.5 $10,001 to $20,0002.3 $20,001 to $30,0002.4 $30,001 to $40,0002.4 $40,001 to $50,0002.3 $50,001 to $60,0002.3 $60,001 to $75,0002.3 $75,001 to $100,0002.1 $100,001 to $150,0002.1 $150,001 or more2.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016. Computation by NCES QuickStats on 6/4/2018 efbkh1e5 Q105 Language spoken most often at home by first parent or guardian by Work status of child's first parent or guardian. Q105 Language spoken most often at home by first parent or guardian English(%) Spanish(%) A language other than English or Spanish(%) English and Spanish equally(%) English and another language equally(%) Total Estimates Total24.035.913.913.812.4100% Work status of child's first parent or guardian Working 35 hours or more per week27.728.416.612.115.1100% Working less than 35 hours per week25.540.24.5 !21.18.7100% Looking for work44.232.210.9 !10.2 !!‡100% Not in the labor force16.046.113.314.110.5100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016. Computation by NCES QuickStats on 6/4/2018 efbkh831 E40 Attended a religious event in the past month by E31 Time spent doing homework. E40 Attended a religious event in the past month Yes(%) No(%) Total Estimates Total54.345.7100% E31 Time spent doing homework Less than once a week47.752.3100% 1 to 2 days a week50.549.5100% 3 to 4 days a week57.043.0100% 5 or more days a week55.844.2100% Never36.463.6100% Child does not have homework36.963.1100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012. Computation by NCES QuickStats on 8/28/2018 ckhbkka42 Child's age by E58 Enrolled in language program. Child's age(Mean[0]) Estimates Total11.0 E58 Enrolled in language program Yes10.0 No11.2 {Valid Skip}11.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012. Computation by NCES QuickStats on 8/28/2018 ckhbkka03 E40 Visited a library in the past month by E105 Total household income. E40 Visited a library in the past month Yes(%) No(%) Total Estimates Total39.560.5100% E105 Total household income $0 to $10,00045.654.4100% $10,001 to $20,00038.861.2100% $20,001 to $30,00039.061.0100% $30,001 to $40,00039.160.9100% $40,001 to $50,00038.161.9100% $50,001 to $60,00038.661.4100% $60,001 to $75,00039.660.4100% $75,001 to $100,00036.163.9100% $100,001 to $150,00041.158.9100% $150,001 or more40.459.6100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012. Computation by NCES QuickStats on 8/28/2018 ckhbkkdc44 Number of siblings by E26 Adult in child's household has attended a parent-teacher organization meeting. Number of siblings(Mean[0]) Estimates Total1.4 E26 Adult in child's household has attended a parent-teacher organization meeting Yes1.4 No1.4 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012. Computation by NCES QuickStats on 8/28/2018 ckhbkk735 Child currently has disability by Census region where child lives. Child currently has disability Currently has a disability(%) Does not currently have a disability(%) Total Estimates Total17.382.7100% Census region where child lives Northeast18.082.0100% South17.282.8100% Midwest20.279.8100% West14.485.6100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012. Computation by NCES QuickStats on 8/28/2018 ckhbkkcd1 E32 Child's family received notes or emails from teachers or school administrators by Total school enrollment of students. E32 Child's family received notes or emails from teachers or school administrators Yes(%) No(%) Total Estimates Total62.237.8100% Total school enrollment of students Under 30067.132.9100% 300-59964.535.5100% 600-99962.237.8100% 1,000 or more56.943.1100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES QuickStats on 8/28/2018 ckhbkcd282 E35 Time spent doing homework by Race and ethnicity of child. E35 Time spent doing homework Less than once a week(%) 1 to 2 days a week(%) 3 to 4 days a week(%) 5 or more days a week(%) Never(%) Child does not have homework(%) Total Estimates Total5.914.739.733.73.22.9100% Race and ethnicity of child White, non-Hispanic6.716.241.030.13.22.9100% Black, non-Hispanic5.814.737.735.04.42.4100% Hispanic5.012.539.537.22.63.2100% All other races and multiple races, non-Hispanic4.312.537.240.22.73.1100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016. Computation by NCES QuickStats on 8/28/2018 ckhbkckda3 Number of siblings by Zip code classification by community type. Number of siblings(%>2) Estimates Total15.5 Zip code classification by community type City16.9 Suburban14.7 Town12.0 Rural16.6 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016. Computation by NCES QuickStats on 8/28/2018 ckhbkcp254 E44 Visited a library in the past month by Parent or guardian highest education including same sex partners. E44 Visited a library in the past month Yes(%) No(%) Total Estimates Total34.265.8100% Parent or guardian highest education including same sex partners Less than high school credential29.770.3100% High school graduate or equivalent29.071.0100% Vocational/technical school after HS31.768.3100% College graduate35.864.2100% Graduate or professional school44.455.6100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES QuickStats on 8/28/2018 ckhbkce9b5 Percentage of students in grades 6 through 12 whose parents reported expectations of specific educational attainment levels, by student sex: 2015-16 E60 Child Sex Male(%) Female(%) Total Estimates Total52.048.0100% E20 Expectations for child's future education Complete less than a high school diploma66.034.0100% Graduate from high school57.742.3100% Attend a vocational or technical school after high school71.228.8100% Attend two or more years of college52.747.3100% Earn a Bachelor's degree52.747.3100% Earn a graduate degree or professional degree beyond a Bachelor's45.854.2100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016. Computation by NCES QuickStats on 9/17/2018 ckhbkcec71 Census region by Age category. Census region Northeast(%) South(%) Midwest(%) West(%) Total Estimates Total17.936.322.423.5100% Age category 16 to 24 years old19.333.823.523.4100% 25 to 34 years old17.235.322.325.2100% 35 to 44 years old16.736.921.425.1100% 45 to 54 years old18.636.621.523.3100% 55 to 66 years old17.937.823.620.7100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Adult Training and Education Survey (ATES), 2016. Computation by NCES QuickStats on 7/27/2018 chgbkd2c2 Q78 Age by Q73 Sex. Q78 Age(Mean[0]) Estimates Total41.8 Q73 Sex Male41.5 Female42.0 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Adult Training and Education Survey (ATES), 2016. Computation by NCES QuickStats on 7/27/2018 chgbkdm8d3 Internet access at home and/or on cell phone by Race-ethnicity. Internet access at home and/or on cell phone Yes, at home and on a cell phone(%) Yes, at home only(%) Yes, on a cell phone only(%) No(%) Total Estimates Total78.37.77.66.4100% Race-ethnicity White, non-Hispanic80.88.75.45.1100% Black, non-Hispanic70.36.213.210.3100% Hispanic72.95.612.68.9100% All other and multiple races, non-Hispanic82.37.24.85.7100% NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Adult Training and Education Survey (ATES), 2016. Computation by NCES QuickStats on 7/27/2018 chgbkd074 Q12 Year received most important certification or license by Q1 Highest degree or level of school completed. Q12 Year received most important certification or license(Mean[0]) Estimates Total2,003.2 Q1 Highest degree or level of school completed No high school diploma or GED2,001.6 High school diploma2,002.3 GED or alternative high school credential2,003.7 Less than one year of college credit2,004.3 1 or more years of college credit, no degree2,004.9 Associate's degree (for example, AA, AS)2,003.4 Bachelor's degree (for example, BA, BS)2,003.7 Master's degree2,002.2 Professional degree beyond a bachelor's2,000.3 Doctorate degree (for example, PhD, EdD)2,001.7 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Adult Training and Education Survey (ATES), 2016. Computation by NCES QuickStats on 7/27/2018 chgbkd185 Q39 Ever completed work experience program by Q33 Number of hours of instruction completed to earn last post-secondary certificate. Q39 Ever completed work experience program No, and I am not in one now(%) No, but I am in one now(%) Yes, I have completed this type of program(%) Total Estimates Total77.31.721.0100% Q33 Number of hours of instruction completed to earn last post-secondary certificate 960 hours or more67.51.231.4100% 480 hours to 959 hours69.31.2 !!29.6100% 160 to 479 hours75.41.7 !22.9100% 40-159 hours71.12.0 !26.9100% Less than 40 hours71.70.7 !27.5100% Valid Skip78.21.720.0100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. NOTE: Rows may not add up to 100% due to rounding. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Adult Training and Education Survey (ATES), 2016. Computation by NCES QuickStats on 7/27/2018 chgbkd341Prior degree: 4-year bachelor's degree by Family status, 12 months after BA completion (considering all dependents).Prior degree: 4-year bachelor's degreeYes(%)No(%)TotalEstimatesTotal6.094.0100%Family status, 12 months after BA completion (considering all dependents)Unmarried, no dependents4.895.2100%Unmarried with dependents10.090.0100%Married, no dependents8.591.5100%Married with dependents9.990.1100%NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, B&B:17 Baccalaureate and Beyond Longitudinal StudyComputation by NCES QuickStats on 9/12/2019bckbmdm152Age, as of BA completion by Most recent job, within 12 months after BA completion: Employer offered any benefits (if worked) Age, as of BA completion(Mean[0])EstimatesTotal25.9Most recent job, within 12 months after BA completion: Employer offered any benefits (if worked)Yes26.5No24.8SOURCE: U.S. Department of Education, National Center for Education Statistics, B&B:17 Baccalaureate and Beyond Longitudinal StudyComputation by NCES QuickStats on 9/12/2019bckbmdnbf3Cumulative Pell amount by Teacher pipeline status, as of B&B:16/17 interview. Cumulative Pell amount(Avg>0)EstimatesTotal15,612.9Teacher pipeline status, as of B&B:16/17 interviewHas not taught, has not prepared, and has not considered teaching15,430.6Has not taught, has not prepared, and has considered teaching15,302.6Has not taught, has prepared, and is not certified15,823.6Has not taught, has prepared, and is certified18,066.8Has taught at the PreK-12th grade level16,457.9SOURCE: U.S. Department of Education, National Center for Education Statistics, B&B:17 Baccalaureate and Beyond Longitudinal StudyComputation by NCES QuickStats on 9/12/2019bckbmd934Number of institutions attended before BA completion by Dependency status. Number of institutions attended before BA completion(%>2)EstimatesTotal18.7Dependency statusDependent student8.0Independent student33.1SOURCE: U.S. Department of Education, National Center for Education Statistics, B&B:17 Baccalaureate and Beyond Longitudinal StudyComputation by NCES QuickStats on 9/12/2019bckbmdf15Cumulative Pell amount by Veteran status. Cumulative Pell amount(%>10000)EstimatesTotal34.0Veteran statusVeteran46.0Not a veteran33.5SOURCE: U.S. Department of Education, National Center for Education Statistics, B&B:17 Baccalaureate and Beyond Longitudinal StudyComputation by NCES QuickStats on 9/12/2019bckbmd4d1 Gender by Accepted at first choice college.GenderMale(%)Female(%)TotalEstimatesTotal49.750.3100%Accepted at first choice collegeNo application44.056.0100%Yes/attended‡‡100%Yes/didn't attend41.158.9100%No/not accepted51.148.9100%‡ Reporting standards not met.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, High School and Beyond (HS&B).Computation by NCES QuickStats on 5/22/2019ccebmgb332 High school academic GPA by Urbanicity of high school area. High school academic GPA(Mean[0])EstimatesTotal2.5Urbanicity of high school areaUrban2.3Suburban2.5Rural2.6SOURCE: U.S. Department of Education, National Center for Education Statistics, High School and Beyond (HS&B).Computation by NCES QuickStats on 5/22/2019ccebmgd083Ever attended 4-year college as undergraduate by Senior test quartile.Ever attended 4-year college as undergraduateYes(%)No(%)TotalEstimatesTotal59.840.2100%Senior test quartileLow30.669.4100%Low/Medium40.659.4100%Medium/High58.841.2100%High83.516.5100%NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, High School and Beyond (HS&B).Computation by NCES QuickStats on 5/22/2019ccebmgf534Annual earnings in 1992 by Applied for student financial aid at first choice. Annual earnings in 1992(Mean[0])EstimatesTotal9,402.3Applied for student financial aid at first choiceNo/did not apply10,236.0Yes/offered aid11,649.8Yes/didn't receive aid10,566.6SOURCE: U.S. Department of Education, National Center for Education Statistics, High School and Beyond (HS&B).Computation by NCES QuickStats on 6/11/2019bcfbmdn7e5Senior test quartile by Annual earnings in 1992.Senior test quartileLow(%)Low/Medium(%)Medium/High(%)High(%)TotalEstimatesTotal13.721.628.436.3100%Annual earnings in 1992Less than $1,50021.924.825.427.9100%$1,500 to $4,99914.921.931.431.8100%$5,000 to $9,99910.920.728.739.7100%$10,000 to $24,99910.520.729.739.1100%$25,000 or more8.516.527.147.9100%NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, High School and Beyond (HS&B).Computation by NCES QuickStats on 5/22/2019ccebmgmh261 Housing status (2000) by Gender (2000). Housing status (2000) Own/buying living quarters(%) Rent from someone, not a relative(%) Rent from a relative(%) Live in residence without paying rent(%) Total Estimates Total 30.0 46.3 9.3 14.4 100% Gender (2000) Male 26.7 47.2 10.5 15.6 100% Female 33.2 45.4 8.1 13.3 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88).Computation by NCES QuickStats on 4/3/2019 cdbmpcda2 Income of respondent in 1999 (asked in 2000) by Housing status (2000). Income of respondent in 1999 (asked in 2000)(Mean[0]) Estimates Total 24,476.6 Housing status (2000) Own/buying living quarters 27,653.0 Rent from someone, not a relative 24,147.6 Rent from a relative 22,496.7 Live in residence without paying rent 20,305.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88).Computation by NCES QuickStats on 4/3/2019 cdbmp643 GPA in the 1st calendar year of attendance by Sector for first institution attended as of 2000. GPA in the 1st calendar year of attendance(%>3) Estimates Total 32.0 Sector for first institution attended as of 2000 Private for-profit 55.8 Private nonprofit, less-than-4-year 29.1 Public, less-than-2-year ‡ Public, 2-year 25.2 Private nonprofit, 4-year or above 47.2 Public, 4-year or above 28.2 ‡ Reporting standards not met.SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88).Computation by NCES QuickStats on 4/3/2019 cdbmkg254 Ever attended a 4-year institution as of 2000 by Race of respondent-multiple choice (2000). Ever attended a 4-year institution as of 2000 Yes(%) No(%) Total Estimates Total 65.0 35.0 100% Race of respondent-multiple choice (2000) American Indian or Alaska Native 34.8 65.2 100% Asian or Pacific Islander 78.1 21.9 100% Black, not Hispanic 57.7 42.3 100% White, not Hispanic 68.5 31.5 100% Hispanic or Latino 51.4 48.6 100% More than one race 63.5 36.5 100% NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88).Computation by NCES QuickStats on 4/3/2019 cdbmkd35 Income of spouse/partner in 1999 by Sector for first institution attended as of 2000. Income of spouse/partner in 1999(Avg>0) Estimates Total 28,945.9 Sector for first institution attended as of 2000 Private for-profit 28,884.4 Private nonprofit, less-than-4-year 27,146.9 Public, less-than-2-year 33,567.3 Public, 2-year 28,594.4 Private nonprofit, 4-year or above 31,947.8 Public, 4-year or above 31,813.1 SOURCE: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88).Computation by NCES QuickStats on 4/3/2019 cdbmk2b1Total number of violent incidents recorded by School grades offered - based on 03-04 SASS frame variables (School). Total number of violent incidents recorded(%>0)EstimatesTotal81.4School grades offered - based on 03-04 SASS frame variables (School)Primary74.2Middle93.6Secondary95.9Combined84.7SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003-04 School Survey on Crime and Safety (SSOCS), 2004.Computation by NCES QuickStats on 10/18/2019bkmbmp802Q17e1i. Number of threats of attack with a weapon by School grades offered - based on 03-04 SASS frame variables (School).Q17e1i. Number of threats of attack with a weaponNone(%)1(%)2 or more(%)TotalEstimatesTotal91.44.54.2100%School grades offered - based on 03-04 SASS frame variables (School)Primary93.04.03.0100%Middle88.25.36.5100%Secondary88.45.46.2100%Combined91.24.3 !4.4 !!100%! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003-04 School Survey on Crime and Safety (SSOCS), 2004.Computation by NCES QuickStats on 10/18/2019bkmbmpd43Q20a. Disciplinary occurrences: Student racial tensions by School size categories - based on 03-04 SASS frame (School).Q20a. Disciplinary occurrences: Student racial tensionsHappens daily(%)Happens at least once a week(%)Happens at least once a month(%)Happens on occasion(%)Never happens(%)TotalEstimatesTotal0.41.72.848.246.8100%School size categories - based on 03-04 SASS frame (School)Less than 300‡‡1.0 !!33.165.3100%300 to 499‡0.9 !!2.7 !47.948.2100%500 to 9990.5 !!2.23.153.740.5100%1,000 or more1.34.66.463.324.4100%! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003-04 School Survey on Crime and Safety (SSOCS), 2004.Computation by NCES QuickStats on 10/18/2019bkmbmp9d4Q5b. Parent involvement: Parent participates in parent-teacher conference by Urbanicity - from 03-04 SASS frame variable (School).Q5b. Parent involvement: Parent participates in parent-teacher conference0-25%(%)26-50%(%)51-75%(%)76-100%(%)School does not offer(%)TotalEstimatesTotal6.616.223.450.03.7100%Urbanicity - from 03-04 SASS frame variable (School)City7.917.527.245.42.0100%Urban Fringe5.614.522.053.44.6100%Town7.019.521.449.13.0 !100%Rural6.615.822.850.64.3100%! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003-04 School Survey on Crime and Safety (SSOCS), 2004.Computation by NCES QuickStats on 10/18/2019bkmbmp435School has written plans for responding to at least one crisis situation by Urbanicity - from 03-04 SASS frame variable (School).School has written plans for responding to at least one crisis situationYes(%)No(%)TotalEstimatesTotal98.51.5100%Urbanicity - from 03-04 SASS frame variable (School)City98.61.4 !!100%Urban Fringe99.20.8 !!100%Town98.91.1 !!100%Rural97.32.7 !100%! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 2003-04 School Survey on Crime and Safety (SSOCS), 2004.Computation by NCES QuickStats on 10/18/2019bkmbmpa981Number of violent incidents reported by School grade offered, based on 98-99 CCD.Number of violent incidents reportedNone(%)Any(%)TotalEstimatesTotal28.871.2100%School grade offered, based on 98-99 CCDElementary39.061.0100%Middle12.887.2100%Secondary8.391.7100%Combined23.476.6100%NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 1999-2000 School Survey on Crime and Safety (SSOCS), 2000.Computation by NCES QuickStats on 12/3/2019dpbmca092Number of serious violent incidents by School grade offered, based on 98-99 CCD.Number of serious violent incidentsNone(%)Any(%)TotalEstimatesTotal80.319.7100%School grade offered, based on 98-99 CCDElementary85.614.4100%Middle70.729.3100%Secondary71.128.9100%Combined79.620.4100%NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 1999-2000 School Survey on Crime and Safety (SSOCS), 2000.Computation by NCES QuickStats on 12/3/2019dpbmcbk003Q16C2_1. Number of attacks without a weapon by Urbanicity, based on 98-99 CCD. Q16C2_1. Number of attacks without a weapon(Mean[0])EstimatesTotal9.8Urbanicity, based on 98-99 CCDCity14.3Urban fringe8.9Town12.7Rural6.3SOURCE: U.S. Department of Education, National Center for Education Statistics, 1999-2000 School Survey on Crime and Safety (SSOCS), 2000.Computation by NCES QuickStats on 12/3/2019dpbmcdg864Q19B. Disciplinary occurrences: Student bullying by Urbanicity, based on 98-99 CCD.Q19B. Disciplinary occurrences: Student bullyingHappens daily(%)Happens at least once a week(%)Happens at least once a month(%)Happens on occasion(%)Never happens(%)TotalEstimatesTotal10.718.619.248.72.8100%Urbanicity, based on 98-99 CCDCity12.719.516.848.32.7 !100%Urban fringe11.217.719.748.13.3 !100%Town9.621.423.041.94.1 !100%Rural9.017.818.952.32.0 !100%! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 1999-2000 School Survey on Crime and Safety (SSOCS), 2000.Computation by NCES QuickStats on 12/3/2019dpbmcea65Q1R. School practice: Require faculty/staff to wear badges by Total students (categorical).Q1R. School practice: Require faculty/staff to wear badgesYes(%)No(%)TotalEstimatesTotal25.474.6100%Total students (categorical)Less Than 30014.086.0100%300 To 49919.780.3100%500 To 99933.866.2100%1,000 Or More38.062.0100%NOTE: Rows may not add up to 100% due to rounding.SOURCE: U.S. Department of Education, National Center for Education Statistics, 1999-2000 School Survey on Crime and Safety (SSOCS), 2000.Computation by NCES QuickStats on 12/3/2019dpbmcgc21 Highest degree attained as of 2001 by First year: Hours per week enrolled 1995-96. Certificate(%) Associate(%) Bachelor(%) Never attained(%) Total Estimates Total 11.7 9.8 29.8 48.6 100% First year: Hours per week enrolled 1995-96 Did not work while enrolled 14.0 9.8 38.5 37.8 100% Worked part time 9.1 11.5 33.5 45.8 100% Worked full time 15.3 6.4 7.6 70.7 100% The names of the variables used in this table are: J1HOURY1 and DGREHI2B. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01).Computation by NCES PowerStats on 10/1/2010. bhabhcfa2 Cumulative persistence outcome 2000-01 by AP tests: Number taken (student). Never attained(%) Certificate(%) Associate(%) Bachelor(%) Total Estimates Total 48.6 11.7 9.9 29.8 100% AP tests: Number taken (student) 0 51.1 7.7 12.1 29.1 100% 1 38.1 2.6 ! 6.0 ! 53.4 100% 2 33.6 0.4 !! 3.4 ! 62.6 100% Three or more 13.8 0.1 !! 1.4 !! 84.8 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent.The names of the variables used in this table are: TEAPNUMB and PROUTYX6. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01).Computation by NCES PowerStats on 10/1/2010. bhabhc973 PELL grant received 1995-96 with (percent > 0) by Race/ethnicity and citizenship status. PELL grant received 1995-96(%>0) Estimates Total 26.4 Race/ethnicity and citizenship status White, non-Hispanic 19.0 Black, non-Hispanic 49.3 Hispanic 42.4 Asian/Pacific Islander 35.5 American Indian/Alaska Native 33.2 ! Other ‡ ‡ Reporting standards not met.! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.The names of the variables used in this table are: PELL96 and SBRACECI. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01).Computation by NCES PowerStats on 10/1/2010. bhabhc664 Grade point average 2001 by Income percentile rank (dependent & independent) 1994. Mostly A’s (%) A’s and B’s (%) Mostly B’s (%) B’s and C’s (%) Mostly C’s (%) C’s and D’s (%) Mostly D’s or below (%) Total Estimates Total 13.2 31.6 35.4 14.4 4.5 0.7 ! 0.1 ! 100% Income percentile rank (dependent & independent) 1994 1-25 12.5 28.8 38.1 14.2 4.8 1.4 ! 0.2 !! 100% 26-50 12.9 30.7 37.0 12.9 5.9 0.4 ! 0.2 ! 100% 51-75 13.1 34.5 34.1 14.3 3.6 0.4 !! 0.1 !! 100% More than 75 14.1 32.7 32.5 16.2 3.9 0.7 !! 0.0 !! 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent.The names of the variables used in this table are: SEGPA2B and PCTALL2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01).Computation by NCES PowerStats on 10/1/2010. bhabhcpea5 Persistence and attainment 6-year total by Gender. Attained, still enrolled(%) Attained, not enrolled(%) Never attained, still enrolled(%) Never attained, not enrolled(%) Total Estimates Total 5.9 45.5 14.9 33.7 100% Gender Male 5.9 41.8 15.8 36.5 100% Female 5.8 48.5 14.2 31.5 100% The names of the variables used in this table are: SBGENDER and PRAT2B. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:96/01).Computation by NCES PowerStats on 10/1/2010. bhabhd1f1 Total loans with (percent > .5) by Graduate programs. Total loans(%>0.5) Estimates Total 40.0 Graduate programs Not in a degree program 28.0 Business administration (MBA) 39.1 Education (any master's) 34.8 Other master of arts (MA) 41.3 Other master of science (MS) 31.8 Other master's degree 49.3 PhD except in education 19.9 Education (any doctorate) 27.1 Other doctoral degree 49.5 Medicine (MD) 77.3 Other health science degree 81.7 Law (LLB or JD) 81.0 Theology (MDiv, MHL, BD) 30.0 Post-baccalaureate certificate 30.1 The names of the variables used in this table are: GRADPGM and TOTLOAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES PowerStats on 10/1/2010. bmabhec922 Total amount from assistantships with (percent > .5) by Graduate field of study. Total amount from assistantships(%>0.5) Estimates Total 15.3 Graduate field of study Undeclared or not in a degree program5.4 Humanities20.8 Social/behavioral sciences31.7 Life sciences47.4 Math/Engineering/Computer science37.9 Education7.6 Business/management7.9 Health10.3 Law5.8 Others23.8 The names of the variables used in this table are: GRASTAMT and MAJORSGR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04).NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.Computation by NCES PowerStats on 10/1/2010. bmabhed8b3 Primary role as student or employee (includes work-study or assistantship) by Graduate field of study. A student working to meet expenses(%) An employee enrolled in school(%) No job(%) Total Estimates Total 35.8 45.1 19.1 100% Graduate field of study Undeclared or not in a degree program20.5 67.3 12.2 100% Humanities44.9 35.9 19.2 100% Social/behavioral sciences58.9 24.6 16.5 100% Life sciences61.0 20.7 18.3 100% Math/Engineering/Computer science47.4 38.3 14.3 100% Education26.3 63.3 10.4 100% Business/management24.8 61.8 13.3 100% Health39.4 19.0 41.6 100% Law39.6 11.6 48.8 100% Others47.0 38.5 14.5 100% The names of the variables used in this table are: JOBROLE2 and MAJORSGR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04).NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES PowerStats on 10/1/2010. bmabhee014 Total loan debt (cumulative) with (percent > .5) by Type of 4-year graduate institution. Total loan debt (cumulative) (%>0.5) Estimates Total65.2 Type of 4-year graduate institution Public 4-year nondoctorate61.4 Public 4-year doctorate60.6 Private not-for-profit 4-yr nondoctorate61.6 Private not-for-profit 4-year doctorate71.3 Private for-profit 4-year85.9 Attended more than one institution68.9 The names of the variables used in this table are: AIDSECTG and BORAMT3. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04).NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.Computation by NCES PowerStats on 10/1/2010. bmabheff045 Average Total loans by Parent's highest education level. Total loans (Avg) Estimates Total6,302.0 Parent's highest education level Do not know parent's education level7,677.5 ! High school diploma or less5,878.7 Some college6,016.3 Bachelor's degree5,794.3 Master's degree or higher7,185.9 ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.The names of the variables used in this table are: PAREDUC and TOTLOAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details.Computation by NCES PowerStats on 10/1/2010. bmabheh251 Job related to current major by Graduate major. Not working(%) Job not related to major(%) Job related to major(%) Total Estimates Total15.8 18.4 65.8 100% Graduate major Uncodable9.5 30.1 60.3 100% Humanities15.2 28.9 55.8 100% Social/behavioral sciences16.2 24.9 58.9 100% Life and physical sciences23.6 13.9 62.5 100% Engineering/computer science/math19.1 12.8 68.1 100% Education7.9 12.4 79.7 100% Business/management11.3 18.8 69.9 100% Health31.6 17.1 51.3 100% Law22.7 26.1 51.3 100% Other13.4 25.3 61.3 100% The names of the variables used in this table are: JOBMAJOR and MAJORS4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1992-93 National Postsecondary Student Aid Study (NPSAS:93). cgbcabd22 Highest level of education ever expect to complete by Graduate major and What does student plan to be doing next year?. Master's(%) Doctor's(%) First-professional(%) Other graduate(%) Total Estimates Total43.1 40.9 12.2 3.8 100% Graduate major Uncodable56.9 31.0 3.9 ! 8.1 100% Humanities41.2 47.5 8.7 2.5 100% Social/behavioral sciences32.6 57.3 7.8 2.3 ! 100% Life and physical sciences24.1 62.1 12.1 1.7 !! 100% Engineering/computer science/math41.7 49.9 5.3 3.1 100% Education53.6 36.3 5.0 5.1 100% Business/management61.3 27.8 6.7 4.2 100% Health29.0 35.6 31.9 3.5 100% Law3.4 28.9 67.2 0.5 ! 100% Other44.3 41.1 8.7 5.8 100% What does student plan to be doing next year? Enrolled in school only19.0 55.9 23.6 1.5 100% Working at a job only48.9 32.2 12.5 6.5 100% Full-time student going to work23.0 54.6 20.2 2.2 100% Part-time student going to work47.6 40.8 6.7 4.9 ! 100% Full-time worker going to school55.4 36.1 5.7 2.7 100% Part-time worker going to school42.9 46.9 8.0 2.3 ! 100% Something else42.0 37.9 10.8 ! 9.3 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: FUTRPLAN, ANYHILVL and MAJORS4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1992-93 National Postsecondary Student Aid Study (NPSAS:93).cgbcabb63 Amount received as research assistant 1, Amount received for fellowships 1, Amount received for teaching assistantships 1 by Gender of student. Amount received as research assistant(Avg>0) Amount received for fellowships(Avg>0) Amount received for teaching assistantships(Avg>0) Estimates Total8,038.2 7,391.5 6,582.4 Gender of student Male8,677.6 8,475.1 6,751.7 Female6,536.2 5,861.2 6,337.3 The names of the variables used in this table are: FELLAMT, RESAMT, GENDER and TEACHAMT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1992-93 National Postsecondary Student Aid Study (NPSAS:93). cgbcab794 Institutional grant total with (percent >0.5), Total federal grant amount with (percent >0.5), State grant total with (percent >0.5) by Level of education to be completed at sample schl, for Received federal financial aid in 1992-93 (Yes). Institutional grant total(%>0.5) Total federal grant amount(%>0.5) State grant total(%>0.5) Estimates Total30.7 5.7 7.9 Level of education to be completed at sample schl Master's29.5 5.0 8.6 Doctor's38.5 10.3 6.2 First-professional31.5 5.1 7.7 Other graduate19.5 5.8 ! 7.8 ! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: FEDFIN, SAMHILVL, TFEDGRT, INGRTAMT and STGTAMT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1992-93 National Postsecondary Student Aid Study (NPSAS:93). cgbcacgfab5 Percentile exclude zeros for Institutional grant total by Graduate major. Percentile [i] 10th25th50th75th90th Zero Estimates Total396.0 1,000.0 2,375.0 5,043.0 9,500.0 85.9 Graduate major Uncodable210.0 ! 360.0 595.0 !! 1,638.0 !! 6,400.0 90.7 Humanities400.0 1,000.0 2,500.0 6,010.0 10,620.0 76.1 Social/behavioral sciences800.0 ! 1,600.0 3,000.0 7,000.0 9,236.0 81.8 Life and physical sciences500.0 1,400.0 ! 3,000.0 7,360.0 12,000.0 ! 80.0 Engineering/computer science/math550.0 1,500.0 2,956.0 6,216.0 ! 12,000.0 85.9 Education205.0 400.0 1,000.0 ! 2,844.0 6,162.0 ! 92.6 Business/management425.0 1,059.0 ! 2,500.0 ! 5,000.0 7,500.0 91.1 Health500.0 1,000.0 2,580.0 4,935.0 9,800.0 82.9 Law764.0 1,200.0 2,500.0 5,300.0 8,696.0 75.3 Other275.0 !! 900.0 ! 1,771.0 ! 3,904.0 7,634.0 84.7 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: INGRTAMT and MAJORS4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1992-93 National Postsecondary Student Aid Study (NPSAS:93). cgbcacnde1 Percentage distribution of undergraduates, by institution type and full-time, full-year status: 1989-90. Full-time, full-year / Public 2-year(%) Full-time, full-year / Public 4-year(%) Full-time/full-year / Private not-for- profit 4-year(%) Full-time/full-year / Private for-profit less-than-4-year(%) Total Estimates Total18.3 48.3 24.1 9.3 100% Dependency and marital status Dependent15.5 52.4 28.0 4.1 100% Independent25.1 38.0 14.2 22.7 100% Dependency and marital status Dependent15.5 52.4 28.0 4.1 100% Independent, no dep, unmarried20.2 47.0 16.1 16.8 100% Independent, no dep, married/sep23.9 43.9 16.3 15.8 100% Independent, with dep,unmarried28.2 23.0 10.0 38.8 100% Independent, with dep,married/sep32.1 29.0 12.7 26.2 100% The names of the variables used in this table are: ATTNINST, CTZNSHP and DEPEND5A. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 National Postsecondary Student Aid Study (NPSAS:90). cgbcaa0c2 Grade point average (cumulative) by Highest education level completed by either parent. Lower than 2.0(%) 2.0 to 2.49(%) 2.5 to 2.99(%) 3.0 to 3.49(%) 3.5 or higher(%) Total Estimates Total15.1 18.5 21.0 22.2 23.2 100% Highest education level completed by either parent Less than high school diploma16.6 17.4 17.4 23.1 25.6 100% High school graduate, GED, or equivalent14.4 17.3 21.1 22.7 24.5 100% Some college14.9 21.1 21.9 20.5 21.6 100% Bachelor's degree or higher13.5 19.3 23.8 23.1 20.3 100% The names of the variables used in this table are: GPA and PAREDUC. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 National Postsecondary Student Aid Study (NPSAS:90). cgbcaaef3 Pell grant amount 1989-90 with (percent >0.5), SEOG amount 1989-90 with (percent >0.5), State grants 1989-90 with (percent >0.5), Institutional grants total amount 1989-90 with (percent >0.5), Other source grants (private, employer) 1989-90 with (percent >0.5) by Dependency and marital status (v1) 1989-90, Dependency and marital status (v1) 1989-90, Dependent parent income percentile 1988 and Independent student income percentile 1988, for Citizenship 1989-90 (US citizen,Eligible noncitizen) and Full-time student's status and institution type 1989-90 (Public 4-year, full-time). Pell grant amount 1989-90(%>0.5) SEOG amount 1989-90(%>0.5) State grants 1989-90(%>0.5) Institutional grants total amount 1989-90(%>0.5) Other source grants (private, employer) 1989-90(%>0.5) Estimates Total26.9 7.5 16.5 14.9 6.8 Dependency and marital status (v1) 1989-90 Dependent20.5 5.6 14.2 15.1 6.7 Independent49.8 14.1 25.0 14.5 6.8 Dependency and marital status (v1) 1989-90 Dependent20.5 5.6 14.2 15.1 6.7 Independent, no dep, unmarried53.1 12.0 24.7 12.6 5.7 Independent, no dep, married/separated22.6 8.1 17.6 16.7 7.7 Independent, with dep, unmarried78.9 34.5 46.7 20.1 11.0 Independent, with dep, married/separated47.3 14.3 20.6 15.3 7.1 Dependent parent income percentile 1988 Lowest quartile65.1 17.5 34.9 20.7 11.1 Lower middle quartile22.0 6.1 17.6 16.8 9.1 Upper middle quartile4.2 1.4 6.3 15.7 5.0 Highest quartile0.5 ‡ 2.4 8.1 2.7 Independent student income percentile 1988 Lowest quartile62.7 16.2 27.3 15.0 5.8 Lower middle quartile47.0 14.0 27.3 14.8 5.6 Upper middle quartile34.4 11.8 21.4 14.7 12.1 Highest quartile10.3 4.3 ! 10.5 10.4 7.8 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: DEPEND5A, PCTINDEP, STGRT2, SEOGAMT, PELLAMT, OTHGRT2, INSTGRT2, CTZNSHP, PCTDEP and ATTNINST. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 National Postsecondary Student Aid Study (NPSAS:90).. cgbcaad34 Average hours worked/week when enrolled 1989-90 with (percent >0.5), Average hours worked/week when enrolled 1989-90 1 by Marital status 1989-90 and Number of dependents, for Dependency status (Independent). Average hours worked/week when enrolled 1989-90(%>0.5) Average hours worked/week when enrolled 1989-90(Avg>0) Estimates Total75.8 36.2 Marital status 1989-90 Not married78.6 35.0 Married76.9 37.1 Separated67.5 36.7 Number of dependents None84.3 35.8 One77.3 36.8 Two73.2 36.5 Three72.2 37.5 Four or more69.9 36.2 The names of the variables used in this table are: MARITAL, EMWKHR3, NUMDEPND and DEPEND. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 National Postsecondary Student Aid Study (NPSAS:90). cgbcaac785 Percentile include zeros for Tuition and fees 1989-90 by Dependency status and Institution control 1989-90. Percentile [i] 10th25th50th75th90th Dependency status = TotalsEstimates Total102.0 261.0 980.0 2,525.0 6,100.0 Institution control 1989-90 Public100.0 182.0 600.0 1,420.0 2,238.0 Private, not-for-profit924.0 2,325.0 6,010.0 9,250.0 12,772.0 Private, for-profit1,698.0 3,350.0 4,400.0 5,708.0 7,695.0 Dependency status = DependentEstimates Total154.0 671.0 1,542.0 3,600.0 8,375.0 Institution control 1989-90 Public118.0 450.0 1,106.0 1,877.0 2,826.0 Private, not-for-profit2,095.0 4,850.0 7,850.0 10,641.0 13,750.0 Private, for-profit2,063.0 3,300.0 4,460.0 6,024.0 7,950.0 Dependency status = IndependentEstimates Total96.0 169.0 527.0 1,608.0 4,425.0 Institution control 1989-90 Public80.0 128.0 310.0 822.0 1,516.0 Private, not-for-profit536.0 1,105.0 2,533.0 5,300.0 8,000.0 Private, for-profit1,600.0 3,350.0 4,398.0 5,630.0 7,645.0 The names of the variables used in this table are: DEPEND, TUITION2 and CONTROL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 National Postsecondary Student Aid Study (NPSAS:90). cgbcaa5b1 Average hours worked/week when enrolled 1989-90 by Attendance, intensity-fall 1989 and Degree program AY89-90. 10 hours or less(%) 11 to 20 hours(%) 21 to 30 hours(%) 31 to 40 hours(%) More than 40 hours(%) Total Estimates Total6.0 14.9 10.3 36.9 32.0 100% Attendance, intensity-fall 1989 Full-time12.1 28.1 15.8 26.4 17.6 100% More than half-time, not full-time3.3 11.6 9.9 41.3 33.9 100% Less than half-time2.1 5.4 5.6 43.7 43.2 100% Degree program AY89-90 Master's degree4.5 12.6 9.3 39.1 34.4 100% Doctoral degree7.6 27.7 11.9 25.9 27.0 100% First-professional degree15.9 24.5 15.2 28.0 16.4 100% Other graduate program4.7 10.3 10.2 40.2 34.7 100% The names of the variables used in this table are: PROGTYP, EMWKHR3 and ATTEND. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 National Postsecondary Student Aid Study (NPSAS:90). cgbcac502 Total loan amount (except PLUS) by Income and dependency level (categorical). $1 - $4,499(%) $4,500 - $6,499(%) $6,500 - $9,499(%) $9,500 or more(%) Total Estimates Total24.5 12.8 29.8 32.9 100% Income and dependency level (categorical) Dep: Less than $20,00019.6 ! 15.2 ! 29.1 36.2 100% Dep: $20,000-49,99932.9 9.9 21.9 35.3 100% Dep: $50,000 or more22.0 6.9 26.6 44.6 100% Ind: Less than $5,00017.6 9.9 31.3 41.2 100% Ind: $5,000-9,99926.8 15.1 27.2 30.9 100% Ind: $10,000-19,99926.6 13.4 30.3 29.7 100% Ind: $20,000-29,99927.1 15.7 29.8 27.3 100% Ind: $30,000-49,99927.6 11.8 32.4 28.2 100% Ind: $50,000 or more32.0 14.2 31.9 22.0 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: TOTLOAN and INCOME. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 National Postsecondary Student Aid Study (NPSAS:90). cgbcadm3a3 Institution need-based grants and scholarships with (percent >0.5), Institutional non-need-based grants with (percent >0.5) by Degree program AY89-90 and Attendance, intensity-fall 1989. Institution need-based grants and scholarships(%>0.5) Institutional non-need-based grants(%>0.5) Estimates Total3.3 3.2 Degree program AY89-90 Master's degree1.9 2.5 Doctoral degree3.1 6.0 First-professional degree10.2 5.6 Other graduate program2.8 ! 2.0 Attendance, intensity-fall 1989 Full-time6.8 5.5 More than half-time, not full-time1.2 2.2 Less than half-time0.3 1.4 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: INSTNOND, PROGTYP, ATTEND and INSTNEED. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 National Postsecondary Student Aid Study (NPSAS:90). cgbcad8d4 Campus-based (Perkins, SEOG, CWS) 1, Campus-based (Perkins, SEOG, CWS) 1 by Grade point average (cumulative). Campus-based (Perkins, SEOG, CWS)(Avg>0) Campus-based (Perkins, SEOG, CWS)(Median>0) Estimates Total2,253.9 2,000.0 Grade point average (cumulative) Less than 2.02,228.2 2,000.0 2.0 to 2.502,489.2 2,268.0 2.50 to 2.992,161.3 2,000.0 3.0 to 3.492,187.2 2,000.0 3.50 to 4.02,201.4 2,000.0 The names of the variables used in this table are: CAMPAMT and GPA. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 National Postsecondary Student Aid Study (NPSAS:90). cgbcad2e5 Percentile exclude zeros for Total employer aid amount by Average hours worked/week when enrolled 1989-90. Percentile [i] 10th25th50th75th90th Zero Estimates Total224.0 470.0 1,062.0 2,250.0 4,050.0 89.8 Average hours worked/week when enrolled 1989-90 None300.0 600.0 1,465.0 3,200.0 6,800.0 96.7 1 to 19330.0 ! 600.0 1,400.0 ! 3,000.0 ! 7,000.0 97.1 20 to 29150.0 !! 540.0 !! 1,260.0 3,000.0 6,000.0 95.3 30 to 39240.0 405.0 735.0 2,400.0 ! 5,000.0 ! 90.8 40 or more205.0 450.0 1,055.0 2,160.0 3,760.0 82.3 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: EMPLYAMT and EMWKHR3. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 National Postsecondary Student Aid Study (NPSAS:90). cgbcadh371 Attendance, intensity-fall 1986 by Dependency status and Marital status, for Citizenship (U.S. citizen,Resident alien). Full-time(%) Part-time(%) Total Estimates Total62.1 37.9 100% Dependency status Dependent75.0 25.0 100% Independent40.2 59.8 100% Marital status Married33.7 66.3 100% Separated/divorced/widowed48.1 51.9 100% Not married71.7 28.3 100% The names of the variables used in this table are: SMARITAL, CTZNSHP2, DEPEND and ATTEND. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1986-87 National Postsecondary Student Aid Study (NPSAS:87). chgcah0e2 Grade point average by Highest education level completed by either parent. 2.0 or lower(%) 2.1 to 3.0(%) 3.1 to 4.0(%) Total Estimates Total16.1 51.6 32.2 100% Highest education level completed by either parent Less than high school17.7 47.2 35.2 100% GED16.1 47.2 36.7 100% High school diploma15.9 49.6 34.5 100% Voc/trade/business less than 1 year16.3 50.2 33.4 100% Voc/trade/business 1 to 2 years16.0 51.3 32.7 100% Voc/trade/business 2 years or more17.5 50.1 32.4 100% Less than 2 years of college18.6 52.6 28.8 100% 2+ years of college or Associates19.0 52.9 28.1 100% Bachelors degree16.0 55.0 29.0 100% Masters degree13.5 54.8 31.7 100% Advanced degree12.0 52.9 35.2 100% The names of the variables used in this table are: GPA and PAREDUC. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1986-87 National Postsecondary Student Aid Study (NPSAS:87). chgcah3c3 Pell grant with (percent >0.5), SEOG with (percent >0.5), State grant with (percent >0.5), Institution grant with (percent >0.5), Other grant (not fed./state/institutional) with (percent >0.5) by Dependency status, Parents' total income, percentile (dependent students) and Independent student total income, percentile rank. Pell grant(%>0.5) SEOG(%>0.5) State grant(%>0.5) Institution grant(%>0.5) Other grant (not fed./state/institutional)(%>0.5) Estimates Total17.5 5.0 14.1 16.1 6.4 Dependency status Dependent13.9 5.1 14.8 19.0 5.8 Independent23.6 4.8 13.0 11.2 7.4 Parents' total income, percentile (dependent students) Lowest quarter43.8 10.8 27.4 21.4 6.7 Lowest middle quarter10.8 7.0 18.6 21.1 7.1 Upper middle quarter0.7 2.0 9.1 19.5 5.8 Highest quarter0.4 0.4 3.8 13.7 3.8 Independent student total income, percentile rank Lowest quarter51.5 8.7 23.6 14.3 5.0 Lower middle quarter38.6 8.2 20.4 13.8 6.0 Upper middle quarter4.0 2.2 6.1 9.5 9.2 Highest quarter0.4 ! 0.2 !! 1.8 7.3 9.4 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: INGRTAMT, SEOGAMT, PCTINDEP, OTHGTAMT, DEPEND, PELLAMT, PCTDEP and STGTAMT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1986-87 National Postsecondary Student Aid Study (NPSAS:87). chgcahdb24 Average hours worked/week AY 86-87 with (percent >0.5), Average hours worked/week AY 86-87 1 by Marital status and Number of dependents, for Dependency status (Independent). Average hours worked/week AY 86-87(%>0.5) Average hours worked/week AY 86-87(Avg>0) Estimates Total65.5 35.0 Marital status Married66.5 35.9 Separated/divorced/widowed48.6 34.7 Not married65.6 33.6 Number of dependents Zero70.2 33.8 163.7 36.0 259.5 36.4 358.6 36.1 457.3 38.0 560.2 38.3 633.2 ! ‡ 7‡ ‡ 8‡ ‡ ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met.The names of the variables used in this table are: SMARITAL, EMWKHR2, DEPEND and RDEPENDS. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1986-87 National Postsecondary Student Aid Study (NPSAS:87). chgcahe35 Percentile exclude zeros for Tuition and fees minus grants by Control, for Dependency status (Dependent). Percentile [i] 10th25th50th75th90th Zero Estimates Total100.0 330.0 976.0 2,110.0 4,974.0 16.4 Control Public100.0 214.0 671.0 1,311.0 1,923.0 17.7 Private, not-for-profit720.0 1,867.0 3,920.0 6,230.0 8,289.0 13.3 Private, for-profit700.0 1,475.0 2,555.0 4,080.0 5,700.0 9.8 The names of the variables used in this table are: CONTROL, NETCST7 and DEPEND. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1986-87 National Postsecondary Student Aid Study (NPSAS:87). chgcahcf11 Graduate degree program by Attendance, intensity-fall 1986 and Institutional type. Masters degree(%) Doctoral degree(%) First professional degree(%) Other(%) Total Estimates Total60.5 13.9 23.0 2.6 100% Attendance, intensity-fall 1986 Full-time42.5 15.7 39.5 2.3 100% Part-time81.6 11.8 3.6 3.0 100% Institutional type Public, non-doctorate-granting94.5 ‡ ‡ 5.5 100% Public, doctorate-granting58.4 20.7 18.3 2.6 100% Private, not-for-profit, non-PhD-grantin97.3 ‡ ‡ 2.7 ! 100% Private, not-for-profit, PhD-granting43.0 13.9 41.7 1.5 100% Other‡ ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met.The names of the variables used in this table are: SECTOR_B, ATTEND and PROGRAM. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1986-87 National Postsecondary Student Aid Study (NPSAS:87). chgcah2b2 Highest level of education ever expect to complete by Major fields of study. Masters degree(%) Doctorate or first professional degree(%) Other degree(%) Total Estimates Total42.4 55.5 2.2 100% Major fields of study Humanities43.7 54.0 2.3 ! 100% Social/behavioral sciences37.7 60.7 1.6 ! 100% Life & physical education25.5 74.5 ‡ 100% Math/engineering/computer science48.0 50.4 1.6 ! 100% Education52.8 44.6 2.6 100% Business/management76.2 22.0 1.8 100% Health17.3 ! 81.0 1.7 100% Law5.6 93.5 1.0 100% Other59.5 37.9 2.6 !! 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not metThe names of the variables used in this table are: ANYHILVL and MAJORS4.The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1986-87 National Postsecondary Student Aid Study (NPSAS:87). chgcah6f3 Total borrowed for education (undergrad and grad) 1, Amount student still owes 1 by Gender of student and Race/ethnicity. Total borrowed for education (undergrad and grad)(Avg>0) Amount student still owes(Avg>0) Estimates Total13,305.7 13,316.3 Gender of student Male14,546.8 14,592.6 Female11,913.7 11,844.9 Race/ethnicity White, not of Hispanic origin13,151.8 13,319.9 Black, not of Hispanic origin12,183.0 11,225.4 American Indian or Alaska Native12,614.5 12,466.6 Asian or Pacific Islander14,684.1 14,069.3 Hispanic16,224.3 15,162.5 The names of the variables used in this table are: BORAMT3, GENDER, OWEAMT and RACE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1986-87 National Postsecondary Student Aid Study (NPSAS:87). chgcaha34 Institutional grant total with (percent >0.5), Federal grant with (percent >0.5), State grant with (percent >0.5) by Control. Institutional grant total(%>0.5) Federal grant(%>0.5) State grant(%>0.5) Estimates Total27.2 3.0 2.2 Control Public26.6 3.1 2.9 Private, not-for-profit28.1 2.8 1.4 Private, for-profit‡ ‡ ‡ ‡ Reporting standards not met.The names of the variables used in this table are: STGTAMT, TFEDGRT, INGRTAMT and CONTROL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1986-87 National Postsecondary Student Aid Study (NPSAS:87). chgcah8f5 Percentile exclude zeros for Adjusted total cost minus grants and 0.5*loans by Major fields of study. Percentile [i] 10th25th50th75th90th Zero Estimates Total2,455.0 3,788.0 5,254.0 7,700.0 12,622.0 0.9 Major fields of study Humanities2,407.0 3,836.0 5,400.0 7,625.0 9,900.0 1.5 ! Social/behavioral sciences2,333.0 3,500.0 4,924.0 6,375.0 8,495.0 1.4 ! Life & physical education3,137.5 4,369.0 5,768.0 7,487.0 9,320.0 3.0 ! Math/engineering/computer science2,442.0 3,563.0 5,338.0 7,080.0 9,609.0 1.0 !! Education2,231.0 3,023.0 4,465.0 5,691.4 7,253.0 # Business/management2,346.5 3,482.0 4,722.0 6,286.0 9,102.0 1.1 ! Health3,448.0 5,111.0 8,329.0 14,980.0 ! 20,110.0 1.2 Law3,355.0 4,841.0 7,450.0 11,200.0 14,585.0 0.4 !! Other2,341.0 3,255.0 4,755.0 7,052.0 9,775.0 # # Rounds to zero! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: ATTNINST, DEPEND5A and CTZNSHP2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1986-87 National Postsecondary Student Aid Study (NPSAS:87). chgcaheb1 Post-BA degree: Highest, collapsed by Undergrad major field of study 2. No post-baccalaureate enrollment(%) Certificate(%) Associates degree(%) Masters degree(%) Doctoral/professional degree(%) Total Estimates Total74.4 3.5 0.4 16.5 5.3 100% Undergrad major field of study 2 Business and management82.7 3.2 0.0 12.5 1.6 100% Education77.9 2.4 ! 0.5 !! 19.2 0.0 !! 100% Engineering76.0 2.0 ! 0.1 !! 20.0 1.9 100% Health professions72.1 3.4 0.6 ! 19.6 4.3 100% Public affairs/social services73.5 4.2 ! 0.3 !! 18.4 3.6 ! 100% Biological sciences50.6 2.7 ! 0.5 !! 15.3 31.0 100% Mathematics and physical science71.5 3.9 0.0 17.6 7.0 100% Social sciences66.4 4.4 0.1 !! 15.9 13.1 100% History65.6 5.0 ! 0.1 !! 20.7 8.5 100% Humanities73.7 4.1 0.7 !! 17.5 4.0 100% Psychology64.7 1.7 ! 0.5 ! 25.8 7.3 100% Other78.9 4.7 0.7 ! 12.7 3.0 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent. The names of the variables used in this table are: HIDEGC and MAJORS4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, B&B: 00/01 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 2/8/2011. gbbb932 Post-BA: Highest degree completed by Age received BA from NPSAS institution. Certificate(%) Associatesdegree(%) Mastersdegree(%) Doctoral/professionaldegree(%) Total Estimates Total41.7 1.7 !! 44.5 2.3 ! 100% Age received BA from NPSAS institution 22 or younger36.9 0.3 !! 55.8 1.5 !! 100% 23-2442.3 4.1 !! 44.9 1.2 !! 100% 25-2938.6 0.0 40.2 8.0 !! 100% 30 or older53.0 2.0 !! 25.3 0.7 !! 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent. The names of the variables used in this table are: AGENBA and PBATT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, B&B: 00/01 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 2/9/2011. bhabhd293 Average>0 Job income, annual amount, calculated by Highest degree plans. Job income, annual amount, calculated(Avg>0) Estimates Total33,129.6 Highest degree plans No plans beyond bachelors34,078.4 Post-baccalaureate certificate31,428.6 Masters degree33,179.3 Doctoral/professional degree29,082.7 The names of the variables used in this table are: CEANNERN and EDEXP. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, B&B: 00/01 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 2/14/2011. bebbbf64 Total amount borrowed for undergraduate education and median Amount owed on all undergraduate loans by current occupation Total amount borrowed forundergraduate education(%>1000) Amount owed all under-graduate loans 2000(Median) Estimates Total57.9 7,777.0 Current occupation code, collapsed Educators64.9 10,322.0 Business and managment61.0 5,383.0 Engineering/software enginr/architecture58.7 3,991.0 Computer science61.3 5,981.0 Medical professionals65.9 9,800.0 Editors/writers/performers57.1 10,000.0 Human/protective service professionals71.4 11,894.0 Research, scientists, technical61.1 7,500.0 Administrative/clerical/legal63.7 8,595.0 Mechanics, laborers55.1 1,200.0 ! Service industries58.6 8,500.0 Other, uncodeable52.9 5,256.0 ! Job related to undergraduate major, closely Not closely related60.9 6,709.0 Closely related62.9 8,793.0 ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent. The names of the variables used in this table are: OWEAMT1, JBRELMJR, TOTDEBT and OCCD. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, B&B: 00/01 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 2/14/2011. bebbbec5 Current, teaching position type by Attendance intensity 1999-2000. Elementary orsecondary teacher(%) Substituteteacher(%) Teacher'saide(%) Itinerantteacher(%) Supportteacher(%) Total Estimates Total67.9 23.2 6.5 0.9 1.4 100% Attendance intensity 1999-2000 Exclusively full-time68.2 22.8 6.3 0.8 ! 1.8 100% Half-time67.7 21.9 7.7 ! 2.4 !! 0.3 !! 100% Less than half-time70.5 20.6 ! 7.9 !! 0.6 !! 0.5 !! 100% Mixed66.3 25.9 6.6 1.0 !! 0.3 !! 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent. The names of the variables used in this table are: CGCURPOS and ATTNPTRN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, B&B: 00/01 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 2/14/2011. bebbb4d1 Percentage distribution of undergraduates, by institution type and full-time, full-year status: 1992-93. Full-time, full-year / Public 2-year(%) Full-time, full-year / Public 4-year(%) Full-time/full-year / Private not-for- profit 4-year(%) Full-time/full-year / Private for-profit less-than-4-year(%) Total Estimates Total19.7 47.5 23.2 9.7 100% Dependency and marital status Dependent16.8 52.2 26.5 4.5 100% Independent26.7 36.0 14.9 22.4 100% Dependency and marital status Dependent16.8 52.2 26.5 4.5 100% Independent, no dep, unmarried22.3 44.9 16.8 16.1 100% Independent, no dep, married/sep23.0 40.5 17.8 18.6 100% Independent, with dep,unmarried30.3 20.7 11.0 38.0 100% Independent, with dep,married/sep34.6 26.5 12.5 26.4 100% The names of the variables used in this table are: ATTNINST, DEPEND5A and CTZNSHP2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1992-93 National Postsecondary Student Aid Study (NPSAS:93). cgbcap072 Grade point average (cumulative) by Degree program, Any remedial courses taken?. Lower than 2.0(%) 2.0 to 2.49(%) 2.5 to 2.99(%) 3.0 to 3.49(%) 3.5 or higher(%) Total Degree program = TotalsEstimates Total14.9 18.1 21.8 23.4 21.7 100% Any remedial courses taken? No13.7 16.7 20.9 24.5 24.2 100% Yes20.9 22.3 23.9 18.9 14.0 100% Degree program = AssociateEstimates Total18.9 17.4 19.7 22.7 21.3 100% Any remedial courses taken? No18.0 15.9 18.5 23.6 24.0 100% Yes21.5 22.1 22.5 19.8 14.1 100% Degree program = Bachelor'sEstimates Total10.4 20.0 26.2 25.2 18.2 100% Any remedial courses taken? No9.2 18.4 25.6 26.6 20.1 100% Yes17.9 26.2 29.5 16.6 9.8 100% Degree program = Certificate/formal awardEstimates Total18.1 15.0 15.0 19.8 32.1 100% Any remedial courses taken? No16.5 14.0 14.5 19.7 35.3 100% Yes25.1 16.3 15.9 21.1 21.6 100% Degree program = Other undergraduateEstimates Total15.8 12.8 13.2 21.2 36.9 100% Any remedial courses taken? No14.5 12.1 11.0 21.7 40.8 100% Yes22.9 12.0 22.6 18.7 23.8 100% The names of the variables used in this table are: GPA, ANYREM and PROGRAM. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1992-93 National Postsecondary Student Aid Study (NPSAS:93). cgbcaabe773 Pell grant amount 1989-90 with (percent >0.5), SEOG amount 1989-90 with (percent >0.5), State grants 1989-90 with (percent >0.5), Institutional grants total amount 1989-90 with (percent >0.5), Other source grants (private, employer) 1989-90 with (percent >0.5) by Dependency and marital status (v1) 1989-90, Dependency and marital status (v1) 1989-90, Dependent parent income percentile 1988 and Independent student income percentile 1988, for Citizenship 1989-90 (US citizen,Eligible noncitizen) and Full-time student's status and institution type 1989-90 (Public 4-year, full-time). Pell grant amount 1989-90(%>0.5) SEOG amount 1989-90(%>0.5) State grants 1989-90(%>0.5) Institutional grants total amount 1989-90(%>0.5) Other source grants (private, employer) 1989-90(%>0.5) Estimates Total26.4 7.1 15.2 16.3 7.1 Dependency and marital status (v1) 1989-90 Dependent19.1 4.9 12.7 16.5 7.1 Independent53.2 15.0 24.5 15.7 7.1 Dependency and marital status (v1) 1989-90 Dependent19.1 4.9 12.7 16.5 7.1 Independent, no dep, unmarried52.9 12.4 24.7 15.5 5.8 Independent, no dep, married/separated33.2 8.7 17.0 15.8 8.5 Independent, with dep, unmarried81.4 36.8 39.2 22.6 7.2 Independent, with dep, married/separated54.9 17.3 22.2 12.8 10.2 Dependent parent income percentile 1988 Lowest quartile66.9 16.6 32.2 23.1 8.5 Lower middle quartile23.2 6.4 17.9 20.8 9.1 Upper middle quartile3.2 1.0 ! 6.9 15.6 7.7 Highest quartile0.7 0.2 ! 3.2 11.3 5.5 Independent student income percentile 1988 Lowest quartile71.6 20.2 31.0 20.0 6.4 Lower middle quartile38.6 10.7 20.8 11.7 5.8 Upper middle quartile32.3 9.2 14.6 9.9 9.8 Highest quartile8.2 3.3 ! 8.2 9.2 13.0 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: DEPEND5A, OTHGTAR, STGTAMT, SEOGAMT, CTZNSHP2, PCTINDEP, INGRTAMT, PELLAMT, PCTDEP and ATTNINST. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1992-93 National Postsecondary Student Aid Study (NPSAS:93). cgbcaakf5e4 Average hours work/week while enrolled 1992-1993 revised with (percent >0.5), Average hours work/week while enrolled 1992-1993 revised 1 by Marital status and Number of dependents (student only), for Dependency status (Independent). Average hours work/week while enrolled 1992-1993 revised(%>0.5) Average hours work/week while enrolled 1992-1993 revised(Avg>0) Estimates Total74.2 36.1 Marital status Not married74.8 34.7 Married74.5 37.5 Separated60.1 32.3 Number of dependents (student only) Zero77.5 35.6 172.4 36.5 271.3 36.9 368.5 36.8 460.4 38.1 564.6 39.8 645.1 ‡ 7‡ ‡ 8‡ ‡ 9‡ ‡ 10‡ ‡ 11‡ ‡ ‡ Reporting standards not met. The names of the variables used in this table are: SMARITAL, DEPEND, EMWKHR4 and RDEPENDS. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1992-93 National Postsecondary Student Aid Study (NPSAS:93). cgbcaaca05 Percentile include zeros for Tuition and fees (NPSAS institution only) by Dependency status and Working and borrowing status 1992-93. Percentile [i] 10th25th50th75th90th Dependency status = TotalsEstimates Total106.0 299.0 1,075.0 2,713.0 6,979.0 Working and borrowing status 1992-93 Borrowed and working unknown521.0 1,426.0 2,786.0 5,804.0 10,850.0 Borrowed and did not work864.0 1,699.0 3,222.0 6,275.0 11,690.0 Borrwed and worked694.0 1,574.0 2,929.0 7,046.0 12,000.0 Did not borrow and working unknown120.0 351.0 1,091.0 2,542.0 5,600.0 Did not borrow and did not work100.0 330.0 1,260.0 3,271.0 8,040.0 Did not borrow and worked81.0 198.0 588.0 1,590.0 3,328.0 Dependency status = DependentEstimates Total194.0 629.0 1,747.0 4,009.0 10,626.0 Working and borrowing status 1992-93 Borrowed and working unknown640.0 1,820.0 3,480.0 8,160.0 14,340.0 Borrowed and did not work1,200.0 2,016.0 3,840.0 9,000.0 14,555.0 Borrwed and worked836.0 1,875.0 3,810.0 9,500.0 14,360.0 Did not borrow and working unknown192.0 640.0 1,656.0 3,495.0 9,616.0 Did not borrow and did not work248.0 850.0 2,118.0 5,486.0 12,350.0 Did not borrow and worked142.0 362.0 1,075.0 2,255.0 5,125.0 Dependency status = IndependentEstimates Total70.0 192.0 620.0 1,826.0 4,400.0 Working and borrowing status 1992-93 Borrowed and working unknown453.0 1,166.0 2,394.0 4,872.0 7,250.0 Borrowed and did not work756.0 1,428.0 2,569.0 4,996.0 7,600.0 Borrwed and worked564.0 1,265.0 2,325.0 4,868.0 7,950.0 Did not borrow and working unknown74.0 252.0 720.0 1,840.0 4,332.0 Did not borrow and did not work50.0 168.0 600.0 1,734.0 4,400.0 Did not borrow and worked55.0 147.0 366.0 990.0 2,200.0 The names of the variables used in this table are: DEPEND, TUITION2 and WHRSBORR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1992-93 National Postsecondary Student Aid Study (NPSAS:93). cgbcaa9e1 Full- or part-time employment at this institution by Institutional classification, matches NSOPF93. Full-time(%) Part-time(%) Total Estimates Total66.9 33.1 100% Institutional classification, matches NSOPF93 Public research85.6 14.4 100% Private research78.3 21.7 100% Public PhD/including medical schools83.2 16.8 100% Private PhD/including medical schools63.0 37.0 100% Public comprehensive74.5 25.5 100% Private comprehensive60.9 39.1 100% Private Liberal arts69.4 30.6 100% Public 2-year47.9 52.1 100% All other50.0 50.0 100% The names of the variables used in this table are: STRAT93 and F04. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1988 National Study of Postsecondary Faculty (NSOPF:88). ccgcafp912 Tenure by Institution type, for Full- or part-time employment at this institution (Full-time). Tenured(%) On tenure track(%) Not on tenure track(%) No tenure system for faculty status(%) No tenure system at institution(%) Total Estimates Total58.4 21.0 7.9 3.6 9.0 100% Institution type Public research66.9 19.2 8.5 4.6 ‡ 100% Private research52.4 29.7 13.1 3.2 1.7 !! 100% Public doctoral58.1 27.1 11.6 3.0 ! ‡ 100% Private doctoral43.7 28.0 2.1 !! 6.6 ! 19.6 ! 100% Public comprehensive65.2 22.1 8.7 2.9 1.1 !! 100% Private comprehensive54.9 29.4 8.7 3.8 ! 3.2 !! 100% Liberal arts49.7 24.8 7.8 5.0 12.7 ! 100% Public 2-year59.6 9.1 4.0 2.3 25.0 100% Private 2-year22.2 !! 3.1 !! ‡ 4.3 !! 70.3 100% Other35.3 17.6 5.9 ! 3.5 ! 37.6 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.The names of the variables used in this table are: F09, TYPE2 and F04. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1988 National Study of Postsecondary Faculty (NSOPF:88). ccgcaffd3 Income, total earnings 1, Basic salary 1 by Institutional classification, matches NSOPF93, for Full- or part-time employment at this institution (Full-time). Income, total earnings(Avg>0) Basic salary(Avg>0) Estimates Total48,499.5 39,518.7 Institutional classification, matches NSOPF93 Public research57,390.3 47,332.3 Private research72,473.3 51,959.9 Public PhD/including medical schools55,610.5 44,139.1 Private PhD/including medical schools56,797.6 48,146.0 Public comprehensive43,343.8 36,853.4 Private comprehensive41,213.1 32,452.5 Private Liberal arts32,778.7 28,823.0 Public 2-year38,682.3 32,507.3 All other39,807.0 31,130.0 The names of the variables used in this table are: F40A, STRAT93 and TOTSAL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1988 National Study of Postsecondary Faculty (NSOPF:88). ccgcagge674 Age 1, Gender with (percent =1), Gender with (percent =2) by Institutional classification, matches NSOPF93, for Full- or part-time employment at this institution (Part-time). Age(Avg>0) Gender(%=1) Gender(%=2) Estimates Total44.3 57.6 42.4 Institutional classification, matches NSOPF93 Public research46.2 66.3 33.7 Private research45.3 56.9 43.1 Public PhD/including medical schools41.6 39.2 60.8 Private PhD/including medical schools44.4 70.2 29.8 ! Public comprehensive43.4 50.0 50.0 Private comprehensive44.8 60.6 39.4 Private Liberal arts46.7 37.0 63.0 Public 2-year44.0 60.6 39.4 All other43.8 61.9 38.1 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: AGE, GENDER, F04 and STRAT93. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1988 National Study of Postsecondary Faculty (NSOPF:88). ccgcagm8b5 Percentile exclude zeros for Basic salary by Primary teaching discipline. Percentile [i] 10th25th50th75th90th Zero Estimates Total2,500.0 8,000.0 28,600.0 40,000.0 55,000.0 2.0 Primary teaching discipline Agriculture & Home Economics10,000.0 ! 24,557.0 36,000.0 45,000.0 58,000.0 1.0 !! Business1,500.0 3,000.0 22,137.0 38,000.0 48,000.0 1.3 ! Education2,798.0 12,000.0 ! 27,500.0 36,800.0 43,500.0 0.8 !! Engineering2,250.0 10,000.0 !! 36,000.0 47,500.0 55,000.0 1.1 !! Fine Arts2,035.0 5,000.0 20,100.0 30,500.0 40,000.0 0.9 !! Health Sciences4,800.0 21,000.0 37,000.0 62,000.0 85,000.0 4.1 ! Humanities2,500.0 8,000.0 26,500.0 37,000.0 46,800.0 0.6 !! Natural Sciences3,000.0 8,000.0 ! 30,000.0 41,000.0 53,000.0 2.0 ! Social Sciences3,000.0 ! 22,000.0 31,880.0 41,000.0 53,000.0 1.3 ! All Other Fields1,680.0 3,800.0 23,500.0 35,000.0 46,000.0 4.0 ! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: F40A and PGMAREA. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1988 National Study of Postsecondary Faculty (NSOPF:88). ccgcagnaf1 Aid: Applied for federal aid by Income: Dependent student household income. No(%) Yes(%) Total Estimates Total 41.7 58.3 100% Income: Dependent student household income Less than $32,00021.3 78.7 100% $32,000-59,99933.4 66.6 100% $60,000-91,99943.1 56.9 100% $92,000 or more52.9 47.1 100% The names of the variables used in this table are: DEPINC and FEDAPP. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES PowerStats on 10/1/2010. bmabhd142 Cumulative Grade Point Average (GPA) as of 2003-2004 by College study: Major field. Less than 2.75(%) 2.75 to 3.74(%) More than 3.75(%) Total Estimates Total34.4 49.0 16.7 100% College study: Major field Humanities35.9 50.4 13.6 100% Social/behavioral sciences35.0 52.1 12.8 100% Life sciences34.9 52.7 12.4 100% Physical sciences31.5 54.3 14.2 100% Math29.1 55.3 15.6 100% Computer/information science34.0 48.1 17.9 100% Engineering37.4 48.1 14.5 100% Education31.9 52.6 15.5 100% Business/management35.6 49.3 15.1 100% Health32.2 50.7 17.0 100% Vocational/technical33.3 47.1 19.6 100% Other technical/professional36.7 49.9 13.4 100% Undeclared or not in a degree program33.2 44.1 22.8 100% The names of the variables used in this table are: MAJORS12 and GPA. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES PowerStats on 10/1/2010. bmabhd3a3 Median Net price after all aid by NPSAS institution type. Net price after all aid(Avg) Estimates Total6,656.0 Institution sector (with multiple) Public less-than-2-year5,616.5 Public 2-year4,716.3 Public 4-year nondoctorate6,253.5 Public 4-year doctorate7,564.1 Private not-for-profit less than 4-year7,382.3 Private not-for-profit 4-yr nondoctorate9,208.7 Private not-for-profit 4-year doctorate14,812.2 Private for-profit less-than-2-year7,842.9 Private for-profit 2 years or more6,737.6 Attended more than one institution‡ ‡ Reporting standards not met.The names of the variables used in this table are: NETCST1 and AIDSECT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES PowerStats on 10/1/2010. bmabhd324 Parent's highest education by Institution sector (with multiple). High school or less(%) Some college(%) Bachelor's or higher(%) Total Estimates Total36.3 21.1 42.6 100% Institution sector (with multiple) Public less-than-2-year53.9 17.3 28.8 100% Public 2-year42.7 23.5 33.8 100% Public 4-year nondoctorate34.6 21.7 43.6 100% Public 4-year doctorate24.2 18.9 56.9 100% Private not-for-profit less than 4-year46.1 18.5 35.3 100% Private not-for-profit 4-yr nondoctorate34.1 19.1 46.8 100% Private not-for-profit 4-year doctorate19.2 14.6 66.2 100% Private for-profit less-than-2-year54.8 17.1 28.1 100% Private for-profit 2 years or more53.0 20.0 27.0 100% Attended more than one institution31.1 21.8 47.1 100% The names of the variables used in this table are: PAREDUC and AIDSECT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES PowerStats on 10/1/2010. bmabhd935 Average>0 Grants: Pell Grants by Income: Categories by dependency status. Grants: Pell Grants(Avg>0) Estimates Total2,449.7 Income: Categories by dependency status Dependent: Less than $10,0003,242.2 Dependent: $10,000-$19,9993,176.1 Dependent: $20,000-$29,9992,715.0 Dependent: $30,000-$39,9991,958.3 Dependent: $40,000-$49,9991,508.6 Dependent: $50,000-$59,9991,309.0 Dependent: $60,000-$69,9991,241.7 Dependent: $70,000-$79,9991,404.4 Dependent: $80,000-$99,999‡ Dependent: $100,000 or more‡ Independent: Less than $5,0002,860.3 Independent: $5,000-$9,9992,642.9 Independent: $10,000-$19,9992,291.7 Independent: $20,000-$29,9992,328.3 Independent: $30,000-$49,9991,561.9 Independent: $50,000 or more1,124.3 ‡ Reporting standards not met. The names of the variables used in this table are: INCOME and PELLAMT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04). NOTICE OF REVISIONS: The NPSAS:04 weights were revised in June 2009. The revised weights will produce 2003-04 estimates that differ somewhat from those in any tables and publications produced before June 2009. See the description for the total Stafford loan variable (STAFFAMT) for details. Computation by NCES PowerStats on 10/1/2010. bmabhe3a1 Employment status by 1987 Carnegie, matches NSOPF93, for Any instructional duties for credit (Yes). Full-time(%) Part-time(%) Total Estimates Total46.3 53.7 100% 1987 Carnegie, matches NSOPF93 Public research25.4 74.6 100% Private research45.6 54.4 100% Public PhD/including medical schools32.7 67.3 100% Private PhD/including medical schools49.7 50.3 100% Public comprehensive36.7 63.3 100% Private comprehensive51.3 48.7 100% Private liberal arts38.4 61.6 100% Public two-year56.3 43.7 100% All other51.1 48.9 100% The names of the variables used in this table are: X02, X01Z1 and B18C. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1993 National Study of Postsecondary Faculty (NSOPF:93). mgcagc52 Citizenship collapsed by 1987 Carnegie, matches NSOPF88, for Employment status (Full-time) and Any instructional duties (Yes). Citizen(%) Non-citizen(%) Total Estimates Total97.3 2.7 100% 1987 Carnegie, matches NSOPF88 Public research95.5 4.5 !! 100% Private research94.9 5.1 ! 100% Public PhD/including medical schools99.7 ‡ 100% Private PhD/including medical schools95.1 4.9 ! 100% Public comprehensive96.9 3.1 !! 100% Private comprehensive97.2 2.8 ! 100% Liberal arts99.3 ‡ 100% Public two-year97.9 2.1 100% Other, excluding private two-year95.8 4.2 ! 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.The names of the variables used in this table are: X03F57, X01, B18C and Z1. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1993 National Study of Postsecondary Faculty (NSOPF:93). mgcag8f3 Average total hours per week worked 1, Total hours/week teaching credit classes 1 by 1987 Carnegie, NSOPF88 collapsed, for Employment status (Full-time) and Any instructional duties (Yes). Average total hours per week worked(Avg>0) Total hours/week teaching credit classes(Avg>0) Estimates Total40.0 6.5 1987 Carnegie, NSOPF88 collapsed Four-year public doctoral40.9 5.4 Four-year private doctoral42.7 5.0 Four-year public non-doctoral41.5 5.6 Four-year private non-doctoral41.3 6.4 Two-year public38.5 7.1 Two-year private40.4 6.8 The names of the variables used in this table are: X08, X01C23, X01C36, B18C and Z1. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1993 National Study of Postsecondary Faculty (NSOPF:93). mgcagg294 Average size of undergraduate class taught for credit 1, Average size of undergraduate class taught for with (percent >50) by Employment status and Faculty status. Average size of undergraduate class taught for credit(Median>0) Average size of undergraduate class taught for(%>50) Employment status = TotalsEstimates Total22.8 7.1 Faculty status Yes23.4 8.2 No, I do not have faculty status20.0 2.8 No one at inst. has faculty status23.8 ‡ Employment status = Full-timeEstimates Total20.5 3.5 Faculty status Yes21.0 4.0 No, I do not have faculty status20.0 2.3 ! No one at inst. has faculty status‡ ‡ Employment status = Part-timeEstimates Total24.0 10.2 Faculty status Yes25.0 11.1 No, I do not have faculty status22.2 3.7 ! No one at inst. has faculty status‡ ‡ ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met.The names of the variables used in this table are: Z3, B18C and X22C23. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1993 National Study of Postsecondary Faculty (NSOPF:93).mgcaged5 Percentile exclude zeros for Total income from the institution by Age, for Employment status (Full-time) and Any instructional duties (Yes). Percentile [i] 10th25th50th75th90th Zero Estimates Total1,000.0 2,000.0 4,000.0 8,600.0 27,000.0 5.8 Age Under 351,000.0 1,620.0 3,400.0 7,000.0 14,000.0 4.2 ! 35 - 441,000.0 1,939.0 3,628.0 8,000.0 25,000.0 4.8 45 - 541,050.0 2,000.0 4,500.0 9,000.0 30,004.0 5.5 55 - 641,200.0 2,500.0 5,000.0 10,000.0 31,683.0 10.1 ! 65 - 701,900.0 3,600.0 6,000.0 11,500.0 40,000.0 ! 13.5 !! 71 or Older‡ ‡ ‡ ‡ ‡ ‡ ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.The names of the variables used in this table are: X03F52, X04E47, B18C and Z1. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1993 National Study of Postsecondary Faculty (NSOPF:93). mgcagbe1 Full- or part-time employment at this institution by 2000 Carnegie code (5 category) by control, for Any instructional duties for credit (Yes). Full-time(%) Part-time(%) Total Estimates Total57.4 42.6 100% 2000 Carnegie code (5 category) by control Public Doctoral76.2 23.8 100% Private Doctoral63.5 36.5 100% Public Master's62.2 37.8 100% Private Master's53.9 46.1 100% Private Baccalaureate58.2 41.8 100% Public Associate's37.2 62.8 100% Other50.0 50.0 100% The names of the variables used in this table are: X100Z0, X01Z1 and Q5. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1999 National Study of Postsecondary Faculty (NSOPF:99). cffcadka32 Citizenship collapsed by 2000 Carnegie code (5 category) by control, for Any instructional duties for credit (Yes) and Full- or part-time employment at this institution (Full-time). Yes(%) No(%) Total Estimates Total93.4 6.6 100% 2000 Carnegie code (5 category) by control Public Doctoral90.5 9.5 100% Private Doctoral88.0 12.0 100% Public Master's94.6 5.4 100% Private Master's96.6 3.4 100% Private Baccalaureate95.3 4.7 100% Public Associate's98.6 1.4 100% Other95.3 4.7 ! 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: X100Z0, X01Z1, Q5 and X02Z90. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1999 National Study of Postsecondary Faculty (NSOPF:99). cffcade03 Average total hours per week worked 0 by Full- or part-time employment at this institution and 2000 Carnegie code (5 category) by control, for Any instructional duties for credit (Yes). Average total hours per week worked(Avg) Full- or part-time employment at this institution = TotalsEstimates Total46.3 2000 Carnegie code (5 category) by control Public Doctoral51.8 Private Doctoral49.2 Public Master's47.5 Private Master's43.9 Private Baccalaureate47.1 Public Associate's40.4 Other44.8 Full- or part-time employment at this institution = Full-timeEstimates Total53.3 2000 Carnegie code (5 category) by control Public Doctoral55.8 Private Doctoral54.8 Public Master's52.4 Private Master's51.7 Private Baccalaureate54.3 Public Associate's48.9 Other52.3 Full- or part-time employment at this institution = Part-timeEstimates Total36.9 2000 Carnegie code (5 category) by control Public Doctoral39.0 Private Doctoral39.4 Public Master's39.5 Private Master's34.8 Private Baccalaureate37.1 Public Associate's35.4 Other37.2 The names of the variables used in this table are: X100Z0, X01Z30, X01Z1 and Q5. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1999 National Study of Postsecondary Faculty (NSOPF:99). cffcaee9a4 Average total hours per week worked 0, Average total hours per week worked with (percent >40) by Principal field of teaching, matches NSOPF88, for Any instructional duties for credit (Yes). Average total hours per week worked(Median) Average total hours per week worked(%>40) Estimates Total50.0 66.9 Principal field of teaching, matches NSOPF88 Agriculture and home economics52.0 76.4 Business49.0 66.5 Education50.0 67.2 Engineering52.0 76.1 Fine arts46.0 60.6 Health sciences51.0 72.4 Humanities45.0 58.4 Natural sciences50.0 70.0 Social sciences50.0 69.2 All other programs50.0 68.2 The names of the variables used in this table are: X02Z14, X01Z30 and X01Z1. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1999 National Study of Postsecondary Faculty (NSOPF:99). cffcaeh0f5 Percentile exclude zeros for Total monetary income of respondent from all sources by Gender, for Full- or part-time employment at this institution (Full-time) and Any instructional duties for credit (Yes). Percentile [i] 10th25th50th75th90th Zero Estimates Total35,000.0 44,000.0 59,000.0 80,623.0 115,000.0 0.3 Gender Male39,000.0 49,500.0 65,000.0 90,250.0 125,750.0 0.3 ! Female31,738.0 39,000.0 49,500.0 65,000.0 88,000.0 0.2 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: X16Z76, X01Z1, Q5 and Q81. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1999 National Study of Postsecondary Faculty (NSOPF:99). cffcaeec1 Employed full or part time at this institution by 2000 Carnegie code by control. Full time(%) Part time(%) Total Estimates Total56.3 43.7 100% 2000 Carnegie code by control Public doctoral77.8 22.2 100% Private not-for-profit doctoral68.7 31.3 100% Public master's63.3 36.7 100% Private not-for-profit master's45.0 55.0 100% Private not-for-profit baccalaureate63.2 36.8 100% Public associates33.3 66.7 100% Other49.2 50.8 100% The names of the variables used in this table are: Q5 and X121Q0. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES PowerStats on 10/1/2010. bkabhbfa2 Race/ethnicity recoded by 2000 Carnegie code by control, for Employed full or part time at this institution (Full time). White non-Hispanic(%) Black/African American non-Hispanic(%) Asian/Pacific Islander(%) Hispanic White or Hispanic Black(%) Other(%) Total Estimates Total80.3 5.8 9.2 3.4 1.2 100% 2000 Carnegie code by control Public doctoral78.9 4.2 12.9 3.0 1.1 100% Private not-for-profit doctoral78.2 5.1 12.8 3.2 0.7 ! 100% Public master's78.1 9.1 7.6 3.6 1.6 ! 100% Private not-for-profit master's85.6 5.1 5.7 2.4 1.3 100% Private not-for-profit baccalaureate85.7 6.9 4.1 2.2 1.2 100% Public associates80.7 7.4 4.4 5.7 1.7 100% Other86.7 5.0 5.6 1.8 0.9 ! 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent. The names of the variables used in this table are: X03Q74, Q5 and X121Q0. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES PowerStats on 10/1/2010. bkabhb683 Tenure status by 2000 Carnegie code by control, for Employed full or part time at this institution (Full time). Tenured(%) On tenure track but not tenured(%) Not on tenure track(%) Not tenured-no tenure system(%) Total Estimates Total47.5 20.6 23.7 8.3 100% 2000 Carnegie code by control Public doctoral49.3 19.4 30.3 0.9 100% Private not-for-profit doctoral43.4 19.3 32.7 4.7 100% Public master's53.9 27.7 17.6 0.9 !! 100% Private not-for-profit master's42.0 27.4 22.2 8.4 100% Private not-for-profit baccalaureate42.7 24.5 22.7 10.1 100% Public associates48.5 15.5 10.1 25.9 100% Other39.8 16.8 19.4 24.1 100% !! Interpret data with caution. Relative standard error (RSE) > 50 percent. The names of the variables used in this table are: Q12, Q5 and X121Q0. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES PowerStats on 10/1/2010. bkabhb0a4 Rank by 2000 Carnegie code by control, for Employed full or part time at this institution (Part time). Professor(%) Associate professor(%) Assistant professor(%) Instructor or Lecturer(%) Other ranks/Not applicable(%) Total Estimates Total4.4 2.8 3.7 43.1 46.0 100% 2000 Carnegie code by control Public doctoral6.7 4.5 9.4 42.1 37.3 100% Private not-for-profit doctoral5.9 4.7 11.7 32.1 45.5 100% Public master's6.2 2.2 ! 2.2 40.4 49.0 100% Private not-for-profit master's2.6 3.3 2.6 30.3 61.1 100% Private not-for-profit baccalaureate4.5 4.5 5.9 ! 31.7 53.5 100% Public associates3.1 1.4 0.9 51.5 43.2 100% Other6.7 4.8 4.9 35.2 48.4 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent. The names of the variables used in this table are: Q10, Q5 and X121Q0. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES PowerStats on 10/1/2010. bkabhb1b5 Average>0 Average total hours per week worked by Tenure status. Average total hours per week worked(Avg>0) Estimates Total47.4 Tenure status Tenured53.3 On tenure track but not tenured53.7 Not on tenure track43.0 Not tenured-no tenure system45.4 The names of the variables used in this table are: Q12 and X01Q31. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES PowerStats on 10/1/2010. bkabhb3a1 Any faculty represented by a union by 2000 Carnegie code by control. Not represented by a union(%) Represented by a union(%) Total Estimates Total68.1 31.9 100% 2000 Carnegie code by control Public doctoral69.1 30.9 100% Private not-for-profit doctoral94.4 5.6 100% Public master's58.1 41.9 100% Private not-for-profit master's87.6 12.4 ! 100% Private not-for-profit baccalaureate86.7 13.3 !! 100% Public associates42.4 57.6 100% Other78.3 21.7 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent. The names of the variables used in this table are: X01I12 and X121Q0. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES PowerStats on 10/1/2010. bkabhb792 Median Undergraduate instruction: Percent full-time faculty by 2000 Carnegie code by control. Undergraduate instruction: Percent full-time faculty(Avg) Estimates Total67.1 2000 Carnegie code by control Public doctoral68.6 Private not-for-profit doctoral70.7 Public master’s75.6 Private not-for-profit master’s68.3 Private not-for-profit baccalaureate74.5 Public associates59.9 Other67.0 The names of the variables used in this table are: I19A and X121Q0. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES PowerStats on 10/1/2010. bkabhb523 Full time tenure: Downsized tenured faculty by 2000 Carnegie code by control. No(%) Yes(%) Total Estimates Total85.7 14.3 100% 2000 Carnegie code by control Public doctoral83.4 16.6 100% Private not-for-profit doctoral93.9 6.1 100% Public master's90.7 9.3 ! 100% Private not-for-profit master's99.6 0.4 100% Private not-for-profit baccalaureate88.1 11.9 !! 100% Public associates87.7 12.3 ! 100% Other68.0 32.0 !! 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent. The names of the variables used in this table are: I7C and X121Q0. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES PowerStats on 10/1/2010. bkabhb854 Full time tenure: Maximum years on tenure track by 2000 Carnegie code by control. No maximum(%) Less than 5 years(%) 5 years(%) 6 years(%) 7 years(%) More than 7 years(%) Total Estimates Total17.5 ! 17.4 8.5 27.0 26.0 3.6 100% 2000 Carnegie code by control Public doctoral7.5 0.0 1.1 37.3 45.9 8.2 100% Private not-for-profit doctoral11.4 0.0 2.8 32.0 34.4 19.4 100% Public master's1.5 0.0 22.0 ! 37.1 38.9 0.6 100% Private not-for-profit master's16.8 !! 0.0 7.1 !! 40.5 ! 27.4 ! 8.2 !! 100% Private not-for-profit baccalaureate9.9 ! 0.7 0.0 53.5 32.2 3.7 !! 100% Public associates15.6 !! 44.6 16.9 ! 8.2 ! 13.7 !! 1.1 ! 100% Other41.9 ! 27.1 ! 1.9 !! 10.3 !! 18.5 ! 0.2 !! 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent. The names of the variables used in this table are: I6 and X121Q0. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES PowerStats on 10/1/2010. bkabhb735 Undergraduate instruction: Percent part time faculty with (percent > 50) by 2000 Carnegie code by control. Undergraduate instruction: Percent part time faculty(%>50) Estimates Total17.9 2000 Carnegie code by control Public doctoral0.6 Private not-for-profit doctoral9.9 Public master's1.6 Private not-for-profit master's15.6 !! Private not-for-profit baccalaureate11.1 ! Public associates23.9 Other26.0 ! ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent. The names of the variables used in this table are: I19B and X121Q0. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2004 National Study of Postsecondary Faculty (NSOPF:04). Computation by NCES PowerStats on 10/1/2010. bkabhb2c1 Time between college entry and bachelor’s degree by Undergraduate major. 4 years or less(%) 4 to 5 years(%) 5 to 6 years(%) 6 to 10 years(%) More than 10 years(%) Total Estimates Total 35.5 27.4 11.4 11.7 14.0 100% Undergraduate major Business and management 32.6 26.9 8.7 13.3 18.6 100% Education 32.9 30.4 10.7 11.0 15.0 100% Engineering 25.3 37.4 15.9 11.4 10.0 100% Health professions 22.0 27.3 13.5 14.2 23.1 100% Public affairs/social services 28.3 29.7 11.9 ! 13.2 17.0 100% Biological sciences 53.5 21.7 10.9 8.4 5.5 100% Mathematics & science 38.9 24.9 11.7 11.2 13.3 100% Social science 47.5 25.3 11.4 10.2 5.6 100% History 40.1 26.3 20.0 ! 5.3 ! 8.3 100% Humanities 39.8 21.4 12.8 12.1 13.8 100% Psychology 39.8 26.1 7.3 12.0 14.8 100% Other 35.4 28.7 12.4 11.3 12.2 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.The names of the variables used in this table are: BAMAJOR and B2BATIM2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, B&B: 93/03 Baccalaureate and Beyond Longitudinal Study. Computation by NCES PowerStats on 10/1/2010. bmabhea282 Highest degree completed as of 2003 by Age when received bachelor’s degree, for Student has a bachelor’s degree (Yes). Bachelor's degree(%) Master's degree(%) First-professional degree(%) Doctoral degree(%) Total Estimates Total 73.8 20.2 4.0 2.0 100% Age when received bachelor’s degree 22 or younger 65.5 24.6 6.7 3.1 100% 23-24 80.9 15.4 2.4 1.3 100% 25-29 84.9 13.7 0.7 ! 0.7 100% 30 or older 78.8 19.1 1.3 ! 0.8 ! 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.The names of the variables used in this table are: BACC, AGEATBA and B3HDG03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, B&B: 93/03 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 10/1/2010. bmabhee33 Median Job 2003: annual salary by Highest degree attained by 2003. Job 2003: annual salary (Avg>0) Estimates Total 52,423.0 Highest degree attained by 2003 Bachelor's degree 50,430.9 Master's degree 52,943.2 First-professional degree 82,217.0 Doctoral degree 60,705.7 The names of the variables used in this table are: B3CURINC and B3HDG03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, B&B: 93/03 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 10/1/2010. bmabhef8e4 Undergraduate loans: total owed as of 2003 with (percent > 1) by Occupational category 2003 (collapsed). Undergraduate loans: total owed as of 2003(%>1) Estimates Total 51.4 Occupational category 2003 (collapsed) Educators 54.3 Business and management 49.4 Engineering/architecture 54.8 Computer science 56.2 Medical professionals 52.9 Editors/writers/performers 44.5 Human/protective service/legal profess 53.4 Research, scientists, technical 50.5 Administrative/clerical/legal support 53.2 Mechanics, laborers 50.6 Service industries 48.7 Other, military 51.1 The names of the variables used in this table are: B3UGLN and B3OCCAT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, B&B: 93/03 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 10/1/2010. bmabhe4c5 Teaching status as of 2003 interview by Highest degree attained by 2003. Currently teaching(%) Left teaching(%) Never taught(%) Total Estimates Total 10.5 9.1 80.4 100% Highest degree attained by 2003 Bachelor's degree 8.1 8.0 83.9 100% Master's degree 20.2 13.3 66.5 100% First-professional degree 0.7 !! 5.0 94.3 100% Doctoral degree 0.9 !! 10.2 88.9 100% !! Interpret data with caution. Relative standard error (RSE) > 50 percent.The names of the variables used in this table are: B3TCHST and B3HDG03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, B&B: 93/03 Baccalaureate and Beyond Longitudinal Study. Computation by NCES PowerStats on 10/1/2010. bmabhe631 Degree program AY89-90 by Marital status of student, AY89-90. Associates's degree(%) Bachelor's degree(%) Undergraduate certificate(%) Other undergraduate(%) Estimates Total 33.7 33.0 17.0 16.2 Marital status of student, AY89-90 Not married 32.9 37.4 15.3 14.4 Married 39.9 7.8 26.5 25.8 Separated 26.4 4.4 33.7 35.5 The names of the variables used in this table are: PROGTYP and MARITAL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTD000.Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:90/94).Computation by NCES PowerStats on 11/11/2011. bbnbbb92 Average>0 Total loan (except PLUS) AY89-90 by Control of principal institution AY89-90. Total loan (except PLUS) AY89-90(Avg>0) Estimates Total2,514.5 Control of principal institution AY89-90 Public2,001.6 Private not-for-profit2,753.4 Private for-profit2,939.2 The names of the variables used in this table are: CTRL8990 and TOTLOAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTD000.Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:90/94). Computation by NCES PowerStats on 11/11/2011. bbnbb743 Delayed enrollment by Single parent AY89-90. No, did not delay(%) Delayed(%) Total Estimates Total67.6 32.4 100% Single parent AY89-90 No71.4 28.6 100% Yes7.9 92.1 100% The names of the variables used in this table are: SINGLPAR and DELAYENR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTD000.Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:90/94).Computation by NCES PowerStats on 11/11/2011. bbnbbf64 Centile exclude zeros for Independent students income 1988 AGI by Race-ethnicity. Centile [i] 10th25th50th75th90th Zero Estimates Total3,175.0 7,000.0 13,365.0 24,578.0 34,110.0 6.0 Race-ethnicity White, non-Hispanic3,365.0 7,963.0 14,600.0 26,225.0 34,484.0 4.3 Black, non-Hispanic2,544.0 4,831.0 9,149.0 18,494.0 23,717.0 13.6 Hispanic1,388.0 4,467.0 11,388.0 19,706.0 29,734.0 10.5 Asian/Pacific islander3,000.0 6,516.0 12,567.0 41,628.0 44,989.0 17.6 American Indian/Alaskan native‡ ‡ ‡ ‡ ‡ ‡ ‡ Reporting standards not met. The names of the variables used in this table are: BPSRACE and INDEPINC. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:90/94).Computation by NCES PowerStats on 11/11/2011. cbpba0a5 Attainment/enrollment status 1994 by First transfer, level and control of destination, for Level and control, 1989-90 referent NPSAS institution. Attained BA(%) Enrolled 4-yr (no BA)(%) Enrolled less-than 4-yr (no BA)(%) Not enrolled (no BA)(%) Total Estimates Total 53.3 14.5 2.7 29.5 100% First transfer, level and control of destination Public, 4-year 46.2 25.9 1.9 26.1 100% Private nfp, 4-year 40.5 28.7 1.4 29.4 100% Public, 2-year 12.3 11.9 18.7 57.1 100% Private not-for-profit, 2-year ‡ ‡ ‡ ‡ 100% Public, less-than-2-year ‡ ‡ ‡ ‡ 100% Private nfp, less-than-2-year ‡ ‡ ‡ ‡ 100% Private for-profit 7.6 8.6 22.9 60.9 100% First transfer, level and control of destination Transfer to 4-year (exc. fp) 44.9 26.5 1.8 26.8 100% Transfer to 2-year (exc. fp) 12.5 11.5 19.3 56.7 100% Transfer to less-than-2-year (exc. fp) ‡ ‡ ‡ ‡ 100% Transfer to private, fp 7.6 8.6 22.9 60.9 100% First transfer, level and control of destination Did not transfer or horizontal transfer 59.4 15.1 0.3 25.3 100% Vertical transfer (down) 11.8 10.6 19.2 58.4 100% ‡ Reporting standards not met. The names of the variables used in this table are: TRANTO, OFCO8990 and ATTENRST. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1989-90 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:90/94).Computation by NCES PowerStats on 11/11/2011. bfpbae51 Student budget 1999-2000, Tuition and fees 1999-2000 by Institution type, and Dependency and marital status 1999-2000, for Attendance pattern 1999-2000. Student budget 1999-2000(Avg>0) Tuition and fees 1999-2000(Avg>0) Estimates Total 14,643.1 6,443.9 Institution type (with multiple) 1999-2000 Public 2-year 9,034.8 1,538.6 Public 4-year 12,509.7 4,227.6 Private 4-year 23,585.6 15,031.9 For Profit 18,084.0 8,853.5 Dependency and marital status (separated=unmarried) 1999-2000 Dependent 15,086.5 7,118.9 Indep, no dep, unmarried/separated 13,719.3 4,957.9 Indep, no dep, married 13,303.6 4,095.5 Indep, with dep, unmarried/sepratd 13,086.1 4,396.0 Indep, with dep, married 13,109.0 3,945.1 The names of the variables used in this table are: DEPEND5B, ATTNSTAT, AIDSECT, TUITION2 and BUDGETA2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:00).Computation by NCES PowerStats on 12/14/2010. bepbad42 Hours worked per week (incl work-study) 1999-2000 by Sampled institution type 1999-2000. Student budget 1999-2000(Avg>0) Estimates Total 31.5 Sampled institution type 1999-2000 Public 2-year 35.7 Public 4-year 27.2 Private 4-year 26.2 For Profit 34.2 Other 31.3 The names of the variables used in this table are: SECTOR9 and WKHRS2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:00). Computation by NCES PowerStats on 12/14/2010. bepbac23 Undergraduate field of study 1999-2000 by Sampled institution type 1999-2000. Humanities/social behavioral sciences(%) STEM(%) Education(%) Business/management(%) Health(%) Vocational/technical and other technical/professional(%) Estimates Total 26.8 21.4 8.1 18.5 10.1 15.1 Sampled institution type 1999-2000 Public 2-year 27.4 20.2 6.8 16.9 11.2 17.4 Public 4-year 27.0 22.7 10.3 19.0 9.3 11.6 Private nfp 4-year 31.9 19.7 9.6 22.7 7.3 8.7 Private for-profit 10.5 26.7 0.5 ! 16.6 12.6 33.1 Other 13.4 21.8 3.6 ! 19.6 14.4 27.1 ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.The names of the variables used in this table are: SECTOR9 and MAJORS3. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:04/09).Computation by NCES PowerStats on 12/1/2010. bepba904 Pell Grant total 1999-2000 by Income percentile 1999-2000 and Sector 1999-2000. Pell Grant total 1999-2000(Avg>0) Estimates Total 1,910.2 Income percentile 1999-2000 0 <= X <= 25 2,091.4 26 <= X <= 50 1,691.4 51 <= X <= 75 1,217.9 X >= 76 1,140.9 Sector (4 categories plus multiple) 1999-2000 Public 4-year 2,035.8 Private not-for-profit 4-year 1,996.1 Public 2-year 1,698.2 Private for profit 1,999.7 The names of the variables used in this table are: PCTALL2, PELLAMT and SECTOR4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:00). Computation by NCES PowerStats on 12/14/2010. bepba4e5 Stafford total subsidized unsubsidized 1999-2000 by Dependency status 1999-2000 by Income percentile dependent students 1999-2000 and Income percentile independent students 1999-2000. Pell Grant total 1999-2000(Avg>0) Estimates Total 4,036.0 Dependency status 1999-2000 Dependent 3,539.7 Independent 4,833.8 Income percentile dependent students 1999-2000 Bottom quartile 3,467.5 Second quartile 3,487.8 Third quartile 3,560.4 Top quartile 3,679.4 Income percentile independent students 1999-2000 Bottom quartile 4,695.3 Second quartile 4,738.4 Third quartile 5,051.0 Top quartile 5,373.6 The names of the variables used in this table are: STAFFAMT, PCTDEP, DEPEND and PCTINDEP. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:00). Computation by NCES PowerStats on 12/14/2010. bepba681 Grade point average 1995-96 by Attendance pattern (full year=9 months) 1995-96 and Total aid 1995-96, for Gender (Female). Mostly A's (3.75+)(%) A's & B's (3.25-3.74)(%) Mostly B's (2.75-3.24)(%) B's & C's (2.25-2.74)(%) Mostly C's (1.75-2.24)(%) C's & D's (1.25-1.74)(%) Mostly D's or below ( below 1.24)(%) Estimates Total15.2 21.3 24.2 16.8 9.8 3.6 8.8 Attendance pattern (full year=9 months) 1995-96 Full-time, full-year, 1 institution10.8 25.1 29.2 20.6 9.2 3.3 1.5 Full-time/full year, more than 1 inst.18.8 18.8 28.1 14.6 9.6 2.7 7.3 Full-time/part year13.7 18.1 21.2 13.7 10.7 5.6 16.2 Part-time/full year, 1 institution14.5 22.4 25.1 19.0 11.5 3.2 4.2 Part-time/full year, more than 1 inst.26.2 16.7 21.5 14.4 5.9 2.5 ! 11.9 Part-time/part year20.9 17.7 18.9 12.1 8.8 3.5 17.6 Total aid 1995-96 $016.2 19.3 23.5 15.2 9.9 3.7 11.7 $1-1,99917.2 21.5 21.4 14.4 10.0 4.2 11.0 $2,000-3,99912.8 22.8 23.9 20.5 10.7 3.8 5.1 $4,000-5,99914.2 21.5 25.7 20.1 10.6 3.3 4.1 $6,000-9,99912.1 24.5 29.7 20.1 8.0 3.0 2.5 $10,000-24,99911.0 28.6 29.1 20.1 7.8 2.2 1.0 $25,000 or more10.4 ! 27.6 35.5 15.6 9.5 ! 0.5 !! 0.9 !! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: TOTAID, GENDER, GPA and ATTNST3. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96).Computation by NCES PowerStats on 4/18/2012. bgdbcea2 Average Total federal grant 1995-96, average Total state aid 1995-96, median>0 Institutional total aid 1995-96 by Dependency status 1995-96 and Degree program during first term 1995-96. Total federal grant 1995-96(Avg) Total state aid 1995-96(Avg) Institutional total aid 1995-96(Median>0) Estimates Total366.3 192.3 1,499.0 Dependency status 1995-96 Dependent337.3 244.2 2,266.0 Independent, no dep, unmarried285.1 158.5 560.0 Independent, no dep, married121.6 62.5 666.0 ! Independent with deps569.5 157.9 494.0 Degree program during first term 1995-96 Certificate or award378.9 143.1 443.0 Associate's degree296.6 86.3 399.0 Bachelor's degree458.1 329.9 2,499.0 Undergraduate, non degree program76.6 15.0 ! 471.0 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: TFEDGRT, DEPEND4, INSTAMT, STATEAMT and DEGFIRST. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96).Computation by NCES PowerStats on 4/18/2012. bgdbc7e3 Centile exclude zeros for SAT combined score by Gender and Grade point average 1995-96. Centile [i] 10th25th50th75th90th Zero Gender = MaleEstimates Total670.0 800.0 950.0 1,100.0 1,230.0 0.0 Grade point average 1995-96 Mostly A's (3.75+)870.0 1,040.0 1,150.0 1,270.0 1,370.0 0.0 A's & B's (3.25-3.74)810.0 940.0 1,070.0 1,220.0 1,320.0 0.0 Mostly B's (2.75-3.24)720.0 830.0 990.0 1,130.0 1,240.0 0.0 B's & C's (2.25-2.74)650.0 770.0 920.0 1,040.0 1,180.0 0.0 Mostly C's (1.75-2.24)640.0 760.0 880.0 1,010.0 1,110.0 0.0 C's & D's (1.25-1.74)600.0 750.0 840.0 950.0 1,090.0 0.0 Mostly D's or below ( below 1.24)540.0 620.0 800.0 930.0 1,100.0 0.0 Gender = FemaleEstimates Total640.0 760.0 900.0 1,060.0 1,200.0 0.0 Grade point average 1995-96 Mostly A's (3.75+)790.0 920.0 1,080.0 1,190.0 1,320.0 0.0 A's & B's (3.25-3.74)730.0 870.0 1,010.0 1,140.0 1,260.0 0.0 Mostly B's (2.75-3.24)680.0 780.0 910.0 1,050.0 1,160.0 0.0 B's & C's (2.25-2.74)630.0 720.0 840.0 970.0 1,090.0 0.0 Mostly C's (1.75-2.24)590.0 670.0 800.0 920.0 1,050.0 0.0 C's & D's (1.25-1.74)570.0 660.0 760.0 890.0 1,010.0 0.0 Mostly D's or below ( below 1.24)500.0 630.0 760.0 910.0 1,060.0 0.0 The names of the variables used in this table are: GENDER, GPA and TESATCRE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96).Computation by NCES PowerStats on 4/18/2012. bgdbc824 Attendance intensity 1995-96 by Race/ethnicity of student and Parents highest education level (3 values) 1995-96. Exclusively full-time(%) Mixed full-time/part-time(%) Exclusively half-time(%) Exclusively less-than-half-time(%) Mixed half-time/less-than-half-time(%) Other(%) Estimates Total45.1 17.8 12.9 18.2 5.4 0.6 Race/ethnicity of student White, non-Hispanic44.9 17.6 12.0 19.5 5.5 0.5 ! Black, non-Hispanic44.2 17.9 16.1 16.0 5.0 0.8 ! Hispanic44.5 17.4 16.4 15.8 5.2 0.6 ! Asian/Pacific Islander48.9 19.7 11.4 13.5 5.5 0.9 ! American Indian/Alaskan Native44.3 16.4 15.6 17.5 ! 4.7 ! 1.5 !! Other63.4 22.5 8.0 ! 3.5 ! 1.2 !! 1.4 !! Non-resident alien52.1 26.7 6.0 7.2 ! 7.7 ! 0.2 !! Parents highest education level (3 values) 1995-96 Less than high school37.0 14.7 19.3 21.2 7.2 0.5 ! High school graduate47.0 18.7 12.5 16.0 5.3 0.5 ! College or beyond54.7 19.8 9.9 11.3 3.6 0.6 ! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: PARED, ATTNPTRN and RACE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96).Computation by NCES PowerStats on 4/16/2012. bgdbc025 Percentage of students who met with an advisor about plans, percent who talked with faculty outside class time, and percent who attended lectures/conventions/field trips, by Gender and Dependency status 1995-96. Meet with advisor about plans 1995-96(%>0.5) Talk with faculty outside class 1995-96(%>0.5) Attend lectures/conventions/field trips 1995-96(%>0.5) Estimates Total70.0 66.8 43.0 Gender Male69.5 66.8 44.0 Female70.4 66.9 42.1 Dependency status 1995-96 Dependent76.3 72.8 47.4 Independent53.2 51.0 31.3 The names of the variables used in this table are: SITALK, GENDER, SILECTUR, DEPEND4 and SIMEET. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTB000.Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96).Computation by NCES PowerStats on 4/18/2012. bgdbcb81 Distance education - entire program by Institution control 1999-2000. Did not take distance education(%) Entire program not distance education(%) Entire program distance education(%) Total Estimates Total89.1 6.8 4.1 100% Institution control 1999-2000 Public87.9 7.8 4.3 100% Private not-for-profit90.8 5.3 3.9 100% Private for-profit89.7 8.4 ! 1.9 !! 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent. The names of the variables used in this table are: CONTROL and NEENTPGM. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:00).Computation by NCES PowerStats on 12/21/2010. capbad82 Graduate fellowship 1999-2000 with (percent > 0.5), average>0 Graduate fellowship 1999-2000 by Graduate degree type 1999-2000. Graduate fellowship 1999-2000(%>0.5) Graduate fellowship 1999-2000(Avg>0) Estimates Total100.0 7,784.2 Graduate degree type 1999-2000 Masters100.0 6,224.7 Doctorate100.0 10,559.2 First-professional100.0 7,730.5 Post-BA certificate‡ ‡ Other100.0 2,843.3 ‡ Reporting standards not met. The names of the variables used in this table are: GRADDEG and FELLAMT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:00).Computation by NCES PowerStats on 12/20/2010. capbaa23 Graduate field of study 1999-2000 by Race-ethnicity (with multiple) 1999-2000. STEM(%) Business/management(%) Education(%) Other(%) Total Estimates Total14.7 18.3 24.1 42.9 100% Race-ethnicity (with multiple) 1999-2000 White, non-Hispanic12.3 17.0 26.4 44.2 100% Black, non-Hispanic8.0 27.0 27.9 37.2 100% Hispanic or Latino13.2 18.8 25.7 42.2 100% Asian or Pacific Islander34.3 20.1 6.5 39.0 100% Other or more than one race22.6 15.3 18.2 43.9 100% The names of the variables used in this table are: MAJORS4 and RACE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:00).Computation by NCES PowerStats on 12/20/2010. capbad74 Centile exclude zeros for Total income (continuous) 1999-2000 by Graduate and first-professional programs 1999-2000. Centile [i] 10th25th50th75th90th Zero Estimates Total5,000.0 13,817.0 32,292.0 61,808.0 90,000.0 1.2 Graduate and first-professional programs 1999-2000 Business administration (MBA)10,000.0 25,000.0 48,000.0 79,000.0 107,000.0 0.3 !! Education (any master's)10,383.0 25,000.0 40,570.0 69,000.0 90,000.0 0.1 !! MA/MS/Other Masters'5,000.0 13,200.0 31,418.0 59,635.0 89,000.0 0.6 PhD except in education6,720.0 12,000.0 20,000.0 44,970.0 75,000.0 0.2 !! Education (any doctorate)16,919.0 30,000.0 59,000.0 90,000.0 120,000.0 0.1 !! Medicine (MD)1,308.0 ! 3,947.0 10,000.0 22,077.0 46,860.0 11.1 Other health science degree1,716.0 4,035.0 8,800.0 23,236.0 52,500.0 7.9 Law (LLB or JD)2,200.0 4,990.0 13,129.0 29,080.0 52,560.0 3.0 Theology (MDiv, MHL, BD)7,713.0 !! 22,000.0 40,300.0 61,000.0 90,000.0 0.0 ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent. The names of the variables used in this table are: GRADPGM2 and CINCOME. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:00).Computation by NCES PowerStats on 12/20/2010. capbabd5 Primary role-student or employee 1999-2000 by Weeks worked while enrolled 1999-2000. Student who works(%) Employee who studies(%) Total Estimates Total36.6 63.4 100% Weeks worked while enrolled 1999-2000 All27.5 72.5 100% Most52.5 47.5 100% Half75.9 24.1 100% Less than half69.0 31.0 100% The names of the variables used in this table are: NDWKSWK and SEROLE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:00).Computation by NCES PowerStats on 12/20/2010. capba031 Attendance pattern 1995-96 by Graduate field of study 1995-96 (12 cat) and Number of years of graduate study. Full-time/full year, 1 institution(%) Full-time/part year(%) Part-time/full year, 1 institution(%) Part-time/full year, more than 1 inst.(%) Part-time/part year(%) Estimates Total30.6 9.5 33.0 0.7 25.7 Graduate field of study 1995-96 (12 cat) Undeclared9.1 11.9 33.9 1.6 ! 41.2 Humanities39.3 7.2 34.8 0.6 ! 17.5 Social/behavioral sciences40.4 7.8 37.5 0.3 !! 12.9 Life and physical sciences37.9 14.0 ! 33.0 0.1 !! 14.4 Engineering/computer science/math26.7 10.4 35.4 0.3 !! 26.4 Education16.0 10.9 39.5 1.1 32.3 Business/management23.2 8.8 43.1 0.8 ! 23.6 Health60.9 6.5 20.6 0.4 ! 11.1 Law72.7 6.1 16.5 0.4 !! 2.8 Other37.4 15.4 28.8 0.4 !! 18.1 Number of years of graduate study Zero8.8 25.9 4.6 ! 2.7 ! 55.1 One46.4 5.9 35.4 1.2 9.9 Two39.9 6.6 38.0 1.0 13.7 Three to four31.2 9.3 ! 40.2 1.6 ! 17.3 Five to seven19.7 9.6 42.6 0.5 !! 27.4 Eight or more22.5 9.8 ! 37.4 0.8 !! 29.5 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: ATTNSTAT, MAJORS4 and GRADYRS. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96).Computation by NCES PowerStats on 4/17/2012. bhdbc0a2 Average Total aid 1995-96, average Total grant 1995-96 by Grade point average 1995-96 and Attendance intensity 1995-96. Total aid 1995-96(Avg) Total grant 1995-96(Avg) Estimates Total5,050.5 1,179.0 Grade point average 1995-96 Mostly A's (3.75+)3,845.1 1,125.5 A's & B's (3.25-3.74)4,696.2 1,086.2 Mostly B's (2.75-3.24)5,619.1 821.0 B's & C's (2.25-2.74)7,304.1 643.0 Mostly C's (1.75-2.24)6,607.7 871.2 C's & D's (1.25-1.74)4,880.7 ! 340.0 !! Mostly D's or below ( below 1.24)2,589.8 304.3 ! No grades or pass/fail‡ ‡ Attendance intensity 1995-96 Exclusively full-time9,116.4 2,069.4 Mixed full-time/part-time5,294.4 1,037.8 Exclusively half-time2,804.7 765.9 Exclusively less-than-half-time815.1 334.4 Mixed half-time/less-than-half-time2,233.3 672.2 Other5,819.0 1,505.4 ! ‡ Reporting standards not met. ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: TOTAID, TOTGRT, GPA and ATTNPTRN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96).Computation by NCES PowerStats on 4/18/2012.bgdbc153 Centile exclude zeros for Total loan (including PLUS) 1995-96 by Both parents have a Bachelors 1995-96, Both parents have a high school diploma 1995-96, Both parents have a less than HS education 1995-96 and Both parents have a Masters degree or higher 1995-96, for Referent parent 1995-96 (Both parents or both guardians). Centile [i] 10th25th50th75th90th Zero Estimates Total3,328.0 6,000.0 9,250.0 15,003.0 18,500.0 73.2 Both parents have a Bachelors 1995-96 No3,198.0 5,944.0 9,092.0 14,985.0 18,500.0 73.0 Yes3,656.0 ! 7,650.0 9,754.0 15,458.0 18,500.0 76.7 Both parents have a high school diploma 1995-96 No3,400.0 6,444.0 9,400.0 15,153.0 18,500.0 74.3 Yes2,625.0 5,156.0 8,500.0 14,500.0 18,500.0 69.4 Both parents have a less than HS education 1995-96 No3,333.0 6,014.0 9,250.0 15,070.0 18,500.0 72.2 Yes1,750.0 !! 3,612.0 ! 8,200.0 11,330.0 17,083.0 89.4 Both parents have a Masters degree or higher 1995-96 No3,168.0 5,992.0 9,020.0 14,304.0 18,500.0 74.5 Yes3,570.0 6,500.0 9,712.0 18,499.0 18,500.0 60.5 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: BHSG, BLTHS, BMAPLUS, BBA, RPAR and TOTLOAN2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.< The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96).Computation by NCES PowerStats on 4/17/2012. bhdbccd4 Income and dependency level (categorical) 1994 by Employed by school 1995-96 and Number of jobs held 1995-96. Less than $5,000(%) $5,000-$9,999(%) $10,000-$19,999(%) $20,000-$29,999(%) $30,000-$49,999(%) $50,000 or more(%) Total Estimates Total14.5 11.2 17.7 14.5 20.0 22.2 100% Employed by school 1995-96 School15.3 14.8 23.6 16.6 13.0 16.6 100% Someone else8.6 7.8 14.1 13.5 23.7 32.2 100% Number of jobs held 1995-96 025.3 12.2 21.3 11.3 15.0 15.0 100% 18.7 7.0 14.6 14.8 22.5 32.3 100% 213.1 17.1 19.8 10.8 19.8 19.4 100% 322.2 16.0 25.3 14.8 11.0 ! 10.7 100% 4+16.4 ! 13.5 28.4 ! 22.7 7.1 ! 11.9 ! 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: SESCHEMP, INCOME and SCNUMJBS. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96).Computation by NCES PowerStats on 4/19/2012. bhdbc0f5 Percentage of graduate students who worked 30 hours or more, by degree program during first term 1995-96. Average hours worked per week while enrolled 1995-96(%>29.5) Estimates Total55.3 Degree program during first term 1995-96 Post baccalaureate certificate62.9 Masters degree62.6 Doctoral or professional degree32.0 Other graduate program incl. non degree76.0 The names of the variables used in this table are: HRSWORK and DEGFIRST. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTB000.Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96).Computation by NCES PowerStats on 4/18/2012. bgdbcd61 Table Title Column Cat 1(%) Column Cat 2(%) Column Cat 3(%) Total Sub Table Cat 1 Total % % % 100% Row var label Category % % % 100% Category % % % 100% Sub Table Cat 1 Total % % % 100% Row var label Category % % % 100% Category % % % 100% Source: U.S. Department of Education, National Center for Education Statistics, 19XX-XX Beginning Postsecondary Students... (BPS:96/01). Computation by PowerStats on 9/10/2009 2 Table Title Column Cat 1(%) Column Cat 2(%) Column Cat 3(%) Total Sub Table Cat 1 Total % % % 100% Row var label Category % % % 100% Category % % % 100% Sub Table Cat 1 Total % % % 100% Row var label Category % % % 100% Category % % % 100% Source: U.S. Department of Education, National Center for Education Statistics, 19XX-XX Beginning Postsecondary Students... (BPS:96/01). Computation by PowerStats on 9/10/2009 3 Table Title Column Cat 1(%) Column Cat 2(%) Column Cat 3(%) Total Sub Table Cat 1 Total % % % 100% Row var label Category % % % 100% Category % % % 100% Sub Table Cat 1 Total % % % 100% Row var label Category % % % 100% Category % % % 100% Source: U.S. Department of Education, National Center for Education Statistics, 19XX-XX Beginning Postsecondary Students... (BPS:96/01). Computation by PowerStats on 9/10/2009 4 Table Title Column Cat 1(%) Column Cat 2(%) Column Cat 3(%) Total Sub Table Cat 1 Total % % % 100% Row var label Category % % % 100% Category % % % 100% Sub Table Cat 1 Total % % % 100% Row var label Category % % % 100% Category % % % 100% Source: U.S. Department of Education, National Center for Education Statistics, 19XX-XX Beginning Postsecondary Students... (BPS:96/01). Computation by PowerStats on 9/10/2009 5 Table Title Column Cat 1(%) Column Cat 2(%) Column Cat 3(%) Total Sub Table Cat 1 Total % % % 100% Row var label Category % % % 100% Category % % % 100% Sub Table Cat 1 Total % % % 100% Row var label Category % % % 100% Category % % % 100% Source: U.S. Department of Education, National Center for Education Statistics, 19XX-XX Beginning Postsecondary Students... (BPS:96/01). Computation by PowerStats on 9/10/2009 1 Primary disability as reported by child's teacher, by Child's race and Child's gender. Autism(%) Developmental Delay(%) Mild Mental Retardation(%) Moderate/Severe Mental Retardation(%) Multiple Disabilities(%) Other(%) Total Child's race = HispanicEstimates Total12.5 11.3 0.5 ! 9.5 1.1 !! 65.1 100% Child's gender Male12.4 11.4 0.2 !! 10.1 1.4 !! 64.5 100% Female12.9 ! 10.8 ! 1.4 !! 7.7 ! 0.0 67.1 100% Child's race = Black Or African American/Non-HispanicEstimates Total12.9 ! 19.9 5.9 !! 3.7 ! 2.7 !! 55.0 100% Child's gender Male15.0 ! 17.3 7.7 !! 1.8 ! 1.7 !! 56.5 100% Female6.0 !! 28.2 ! 0.0 10.0 !! 5.9 !! 49.9 100% Child's race = White/Non-HispanicEstimates Total12.9 12.1 3.3 3.4 2.4 ! 65.8 100% Child's gender Male15.2 11.7 2.4 3.4 2.7 ! 64.7 100% Female7.7 ! 13.2 5.5 ! 3.6 ! 1.8 !! 68.3 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: GENDER, DPRACET and DT3PDIS. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTX000. Source: U.S. Department of Education, National Center Special Education Research, Pre-Elementary Education Longitudinal Study (PEELS), Waves 1-5.Computation by NCES PowerStats on 8/22/2011. cdhbbcf2 Average Amount of school time per week (in minutes)in special education setting and regular education classroom, by parent's satisfaction with child's school. Amount of school time per week in special education setting(Avg>0) Amount of school time per week in regular education classroom(Avg>0) Estimates Total842.0 1,505.8 Parent satisfaction with child's school Very Satisfied830.3 1,557.3 Satisfied813.3 1,505.8 Dissatisfied970.4 1,540.8 Very Dissatisfied‡ ‡ ‡ Reporting standards not met. The names of the variables used in this table are: P3SESCH, DT3TMA and DT3TMB. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTT000. Source: U.S. Department of Education, National Center Special Education Research, Pre-Elementary Education Longitudinal Study (PEELS), Waves 1-5.Computation by NCES PowerStats on 8/22/2011. cchbb8a3 Centile (include zeros) for Years teacher working with children with disabilities, Wave 1 by Whether child can recognize alphabet. Centile [i] 10th25th50th75th90th Estimates Total2.0 4.0 9.0 17.0 24.0 Whether child can recognize alphabet, Wave 1 All The Letters1.0 !! 4.0 ! 9.0 17.0 25.0 Most Of Them2.0 5.0 9.0 18.0 25.0 Some Of Them2.0 4.0 8.0 16.0 24.0 None Of Them2.0 5.0 10.0 17.0 24.0 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: P1CBALP and T1EC1B. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTG000. Source: U.S. Department of Education, National Center Special Education Research, Pre-Elementary Education Longitudinal Study (PEELS), Waves 1-5.Computation by NCES PowerStats on 8/22/2011. cchbb934 Average number of children with disabilities in primary class (preschool) and average number of other children in child's primary class (preschool)by number of adults instructing/supervising child's primary class/group and number of special education aides usually in the classroom. Number of children with disabilities in primary class (preschool), Wave 2(Median>0) Number of other children in child's primary class (preschool), Wave 2(Median>0) Estimates Total2.0 11.0 Num. of adults instructing/supervising child's primary class/group, Wave 2 Only 13.0 8.0 2 to 32.0 11.0 4 or more1.0 !! 11.0 Number of special education aides usually in the classroom, Wave 2 Only 12.0 11.0 2 to 31.0 9.0 4 or more‡ ‡ ‡ Reporting standards not met. !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: P2ASSTN, P2NUMSP, DT2STFF and P2CHILD. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTH000. Source: U.S. Department of Education, National Center Special Education Research, Pre-Elementary Education Longitudinal Study (PEELS), Waves 1-5.Computation by NCES PowerStats on 8/22/2011. cchbb5e5 Receives tutoring through the school per parent by Household income. Yes(%) No(%) Total Estimates Total29.7 70.3 100% Household income, Wave 4 $20,000 Or Less37.5 62.5 100% $20,001 - 40,00027.3 72.7 100% > $40,00028.1 71.9 100% The names of the variables used in this table are: DP4INCO and P4ETUTO. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WT4000. Source: U.S. Department of Education, National Center Special Education Research, Pre-Elementary Education Longitudinal Study (PEELS), Waves 1-5.Computation by NCES PowerStats on 8/22/2011. cchbb9d1 Distance education courses by Job 2003-04: Student or employee role (includes work study). Yes(%) No(%) Total Estimates Total 9.3 90.7 100% Job 2003-04: Student or employee role (includes work study) A student working to meet expenses 9.7 90.3 100% An employee enrolled in school 11.4 88.6 100% No job 7.3 92.7 100% The names of the variables used in this table are: DISTEDUC and JOBROLE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:04/06). Computation by NCES PowerStats on 10/1/2010. cehak172 Persistence at any institution through 2006 by Gender. Attained, still enrolled(%) Attained, not enrolled(%) No degree, still enrolled(%) No degree, not enrolled(%) Total Estimates Total 7.0 8.9 50.7 33.5 100% Gender Male 6.5 7.5 50.4 35.6 100% Female 7.3 9.9 50.9 31.9 100% The names of the variables used in this table are: GENDER and PRAT3Y. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:04/06).Computation by NCES PowerStats on 10/1/2010. cgeak593 Total aid 2003-04 with (percent > 0) by Persistence at any institution through 2006. Total aid 2003-04(%>0) Estimates Total 70.6 Persistence at any institution through 2006 Attained a degree or certificate 50,430.9 No degree, still enrolled 52,943.2 No degree, not enrolled 82,217.0 The names of the variables used in this table are: TOTAID and PRAT3Y. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:04/06).Computation by NCES PowerStats on 10/1/2010. cgeak944 Persistence at any institution through 2006 by Grade Point Average 2006. Attained, still enrolled(%) Attained, not enrolled(%) No degree, still enrolled(%) No degree, not enrolled(%) Total Estimates Total 7.0 8.9 50.7 33.5 100% Grade Point Average 2006 Below 2.0 7.5 !! 0.0 13.8 ! 78.8 100% 2.1 to 2.5 ‡ ‡ ‡ ‡ 100% 2.51 to 2.99 ‡ ‡ ‡ ‡ 100% 3.0 and above 8.2 8.1 62.0 21.7 100% ‡ Reporting standards not met.! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent.The names of the variables used in this table are: PRAT3Y and GPA06. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:04/06).Computation by NCES PowerStats on 10/1/2010. cgeak4f5 Persistence anywhere through 2006 by Highest degree ever expected, 2003-04. Attained, still enrolled(%) Attained, not enrolled(%) No degree, still enrolled(%) No degree, not enrolled(%) Total Estimates Total 7.0 8.9 50.7 33.5 100% Highest degree ever expected, 2003-04 No degree or certificate 3.8 !! 17.1 16.3 62.8 100% Certificate 6.9 41.5 10.3 41.3 100% Associate's degree 8.7 17.3 25.3 48.8 100% Bachelor's degree 6.9 7.9 45.2 40.0 100% Post-BA or post-master certificate 5.1 !! 13.4 ! 42.9 38.6 100% Master's degree 7.1 4.8 60.6 27.4 100% Doctoral degree 7.1 4.2 67.9 20.8 100% First-professional degree 3.5 6.7 67.6 22.2 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.!! Interpret data with caution. Relative standard error (RSE) > 50 percent.The names of the variables used in this table are: HIGHLVEX and PRAT3Y. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 Beginning Postsecondary Students Longitudinal Study, First Follow-up (BPS:04/06). Computation by NCES PowerStats on 10/1/2010. cgeak021 Total aid amount by Attendance intensity (all schools) and NPSAS institution sector (with multiple). Total aid amount(Avg>0) Estimates Total9,021.1 Attendance intensity (all schools) Exclusively full-time11,322.8 Exclusively part-time3,897.1 Mixed full-time and part-time9,158.5 NPSAS institution sector (with multiple) Public less-than-2-year4,619.8 Public 2-year3,367.4 Public 4-year non-doctorate-granting8,051.1 Public 4-year doctorate-granting10,170.7 Private nonprofit less-than-4-year7,792.5 Private nonprofit 4-year non-doctorate-granting16,007.1 Private nonprofit 4-year doctorate-granting19,007.9 Private for profit less-than-2-year8,538.1 Private for profit 2-year10,635.7 Private for profit 4-year9,016.3 Attended more than one institution‡ The names of the variables used in this table are: TOTAID, ATTNPTRN and AIDSECT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08).Computation by NCES PowerStats on 5/24/2013. bhabheh6d2 Dependent students: Parent's income by NPSAS institution sector (4 with multiple). Less than $29,000(%) $29,000-63,999(%) $64,000-103,999(%) $104,000 or more(%) Estimates Total19.6 29.3 26.4 24.7 NPSAS institution sector (4 with multiple) Public 4-year16.0 26.1 28.6 29.3 Private nonprofit 4-year13.5 23.7 26.5 36.3 Public 2-year23.9 35.9 25.0 15.1 Private for profit38.7 30.7 19.2 11.4 Others or attended more than one school18.7 28.0 26.2 27.0 The names of the variables used in this table are: DEPINC and SECTOR4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08).Computation by NCES PowerStats on 5/24/2013. bhabhea5f3 Average Total aid amount by Undergraduate degree program. Total aid amount(Avg) Estimates Total5,913.8 Undergraduate degree program Certificate4,909.4 Associate's degree2,889.7 Bachelor's degree9,403.9 Not in a degree program or others1,345.8 The names of the variables used in this table are: TOTAID and UGDEG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08).Computation by NCES PowerStats on 5/24/2013. bhabhedf4 Attendance intensity at all schools by NPSAS institution sector. Exclusively full-time(%) Exclusively part-time(%) Mixed full-time and part-time(%) Estimates Total47.6 36.5 15.9 NPSAS institution sector - 10 categories Public less-than-2-year62.3 33.6 4.1 ! Public 2-year26.7 58.4 14.9 Public 4-year non-doctorate-granting52.6 29.0 18.4 Public 4-year doctorate-granting63.0 16.3 20.7 Private nonprofit lt 4-year54.7 29.3 16.0 Private nonprofit 4-year nondoctorate67.4 18.8 13.9 Private nonprofit 4-year doctorate-granting72.2 14.4 13.4 Private for profit less-than-2-year75.4 15.5 9.1 Private for profit 2-year67.7 19.5 12.8 Private for profit 4-year‡ ‡ ‡ ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: SECTOR9 and ATTNPTRN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08).Computation by NCES PowerStats on 5/24/2013. bhabhecb5 Federal Pell grant with (percent > 0) by Total income by dependency. Federal Pell grant(%>0) Estimates Total27.8 Total income by dependency (categorical) Dependent: Less than $10,00063.8 Dependent: $10,000-$19,99972.9 Dependent: $20,000-$29,99965.1 Dependent: $30,000-$39,99953.4 Dependent: $40,000-$49,99932.6 Dependent: $50,000-$59,99915.7 Dependent: $60,000-$69,9992.3 Dependent: $70,000-$79,9990.0 Dependent: $80,000-$99,9990.0 Dependent: $100,000 or more0.0 Independent: Less than $5,00053.2 Independent: $5,000-$9,99965.1 Independent: $10,000-$19,99952.0 Independent: $20,000-$29,99934.1 Independent: $30,000-$49,99927.9 Independent: $50,000 or more0.2! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: INCOME and PELLAMT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08).Computation by NCES PowerStats on 5/24/2013. bhabhegc31 Total loans with (percent > 0.5) by Total income (continuous). Total loans(%>0.5) Estimates Total42.4 Total income (continuous) Less than $15,00054.9 $15,000-34,29950.2 $34,270-68,05939.7 $68,060 or more27.6 The names of the variables used in this table are: CINCOME and TOTLOAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08).Computation by NCES PowerStats on 5/24/2013. bhabhebe2 Direct Subsidized and Unsubsidized Loans with (percent > 0.5) by Graduate degree program. Direct Subsidized and Unsubsidized Loans(%>0.5) Estimates Total38.6 Graduate degree program Master's degree38.5 Post-baccalaureate or post-master's certificate33.0 Doctor's degree - research/scholarship76.8 Doctor's degree - professional practice25.0 Doctor's degree - other13.4 Not in a degree program‡ The names of the variables used in this table are: GRADDEG and STAFFAMT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08).Computation by NCES PowerStats on 5/24/2013. bhabhecd3 Total assistantships amount with (percent > 0.5) by Attendance intensity (all schools). Total assistantships amount(%>0.5) Estimates Total14.8 Attendance intensity (all schools) Exclusively full-time22.8 Exclusively part-time6.4 Mixed full-time and part-time19.0 The names of the variables used in this table are: GRASTAMT and ATTNPTRN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08).Computation by NCES PowerStats on 5/24/2013. bhabhee254 Average Total loans by NPSAS institution type: Graduate (with multiple). Total loans(Avg) Estimates Total7,810.1 NPSAS institution type: Graduate (with multiple) Public 4-year non-doctorate-granting3,559.3 Public 4-year doctorate-granting5,824.4 Private nonprofit 4-year nondoctorate6,386.4 Private nonprofit 4-year doctorate-granting10,510.1 Private for profit 4-year12,555.7 Attended more than one institution8,495.8 The names of the variables used in this table are: AIDSECTG and TOTLOAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08).Computation by NCES PowerStats on 5/24/2013. bhabhep955 Institutional tuition & fee waivers with (percent > 0.5) by Graduate degree program. Institutional tuition & fee waivers(%>0.5) Estimates Total8.6 Graduate degree program Master's degree7.0 Post-baccalaureate or post-master's certificate21.0 Doctor's degree - research/scholarship2.6 Doctor's degree - professional practice2.8 Doctor's degree - other6.0 Not in a degree program‡ ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: GRADDEG and INSWAIV. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08).Computation by NCES PowerStats on 5/24/2013. bhabhe801 Highest degree attained anywhere through 2009 by Single parent status in 2003-04. No degree(%) Certificate(%) Associate's degree(%) Bachelor's degree(%) Total Estimates Total 50.5 9.4 9.3 30.7 100% Single parent status in 2003-04 Not a single parent 47.9 8.4 9.7 34.0 100% Single parent 72.5 17.9 6.3 3.3 100% The names of the variables used in this table are: ATHTY6Y and SINGLPAR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:04/09).Computation by NCES PowerStats on 12/1/2010. bhabhdc72 Cumulative federal student loan amount owed as of 2009 with (percent > 0.5), average>0 Cumulative federal student loan amount owed as of 2009 by Major (12 categories) when last enrolled 2009. Cumulative federal student loan amount owed as of 2009(%>0.5) Cumulative federal student loan amount owed as of 2009(Avg>0) Estimates Total 47.0 12,655.4 Major (12 categories) when last enrolled 2009 STEM 53.3 15,309.5 Social/behavioral sciences 52.6 15,782.4 Humanities 54.4 16,158.6 Business/management 53.3 15,900.5 Education 55.6 15,448.3 Health 55.7 12,257.8 Vocational, technical or other professional 52.5 14,171.1 Undeclared or not in a degree program 37.5 9,516.0 The names of the variables used in this table are: MAJ09B and T4XOWE09. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:04/09).Computation by NCES PowerStats on 12/1/2010. bhabhd753 Attainment or level at last institution enrolled through 2009 by Attendance intensity pattern through 2009. Attained a degree or certificate(%) No degree, enrolled at 4-year(%) No degree, enrolled at less-than-4-year(%) No degree, not enrolled(%) Total Estimates Total 49.5 7.1 7.9 35.5 100% Attendance intensity pattern through 2009 Always full-time 62.6 4.8 2.8 29.7 100% Always part-time 15.7 1.8 11.3 71.3 100% Mixed 41.9 11.2 13.5 33.4 100% The names of the variables used in this table are: PRLVL6Y and ENINPT6Y. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:04/09).Computation by NCES PowerStats on 12/1/2010. bhabhdfc4 Number of transfers as of June 2009 by Job 2004: Hours worked per week (incl work study), for First institution sector (level and control) 2003-04 (Public 2-year). Did not transfer(%) Transferred(%) Total Estimates Total 60.1 39.9 100% Job 2004: Hours worked per week (incl work study) Did not work 63.3 36.7 100% Part-time 52.4 47.6 100% Full-time 68.7 31.3 100% The names of the variables used in this table are: JOBHOUR2, FSECTOR and TFNUM6Y. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:04/09).Computation by NCES PowerStats on 12/1/2010. bhabhdff5 Cumulative retention and attainment at first inst 6-yr total 2009 by Highest level of high school mathematics. Bachelor’s degree(%) Associate’s degree(%) Certificate(%) No degree, still enrolled(%) No degree, transferred(%) No degree, left without return(%) Total Estimates Total 22.3 8.8 7.7 6.1 26.8 28.2 100% Highest level of high school mathematics None of these 5.2 10.8 9.3 7.6 27.8 39.3 100% Algebra 2 12.8 11.1 8.2 7.5 32.7 27.7 100% Trigonometry/Algebra II 28.6 10.1 4.6 5.1 31.4 20.3 100% Pre-calculus 38.1 7.2 1.9 5.3 31.8 15.7 100% Calculus 60.7 3.5 1.0 ! 3.3 21.5 10.0 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent.The names of the variables used in this table are: HCMATH and PROUTFI6. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 Beginning Postsecondary Students Longitudinal Study, Second Follow-up (BPS:04/09).Computation by NCES PowerStats on 12/1/2010. bhabhebc1 DEMOGRAPHIC AND UNDERGRADUATE COLLEGE CHARACTERISTICS: Percentage distribution of 2007-08 bachelor's degree recipients, by demographic and enrollment characteristics: 2012 STEM(%) Non-STEM(%) Total Estimates Total16.2 83.8 100% Labor force participation in 2012 One full-time job16.8 83.2 100% One part-time job12.6 87.4 100% Multiple jobs10.6 89.4 100% Unemployed12.9 87.1 100% Out of the labor force20.2 79.8 100% Labor force participation in 2012 Employed15.9 84.1 100% The names of the variables used in this table are: B2LFP12 and MAJORS4Y. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTE000. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12)Computation by NCES PowerStats on 3/27/2014. hfbbb62 EMPLOYMENT AND ENROLLMENT: Percentage distribution of 2007-08 bachelor's degree recipients' employment and additional postsecondary enrollment status, by demographic and enrollment characteristics: 2012 Employed(%) Both Employed and enrolled(%) Enrolled only(%) Unemployed(%) Out of the labor force(%) Total Estimates Total69.0 10.7 5.7 6.7 7.9 100% Gender Male71.2 9.7 5.7 6.9 6.5 100% Female67.4 11.4 5.7 6.6 8.9 100% Race/ethnicity (with multiple) White72.1 10.5 4.8 5.5 7.2 100% Black57.1 14.2 8.8 11.8 8.1 100% Hispanic65.8 10.1 6.2 8.5 9.4 100% Asian58.7 8.5 9.7 11.9 11.3 100% Other60.8 11.5 8.1 7.9 11.7 100% Age at bachelor's degree 23 or younger70.1 10.6 6.5 6.0 6.8 100% 24-2969.0 11.0 4.6 6.9 8.6 100% 30 or older64.6 10.7 3.7 9.6 11.3 100% Field of study: undergraduate (10 categories) STEM68.2 10.3 9.6 5.0 6.9 100% Non-STEM69.2 10.7 5.0 7.1 8.0 100% Field of study: undergraduate (10 categories) Computer and information sciences76.8 6.8 2.0 !! 4.9 ! 9.5 100% Engineering and engineering technology76.5 10.1 4.6 ! 4.3 4.5 100% Biological and physical sciences, science technology, mathematics, and agriculture sciences58.0 11.8 16.7 5.6 7.9 100% General studies and other68.6 9.9 3.5 ! 9.4 8.6 100% Social Sciences61.7 12.2 8.9 9.6 7.6 100% Humanities61.4 13.2 8.5 9.0 7.9 100% Health care fields72.6 13.8 2.8 2.2 8.5 100% Business74.6 8.8 2.8 6.6 7.3 100% Education67.8 14.8 3.3 5.0 9.1 100% Other Applied73.7 6.6 4.0 6.9 8.9 100% NPSAS institution type Public 4-year70.0 11.3 5.0 6.2 7.5 100% Private nonprofit 4-year67.7 9.7 7.7 7.1 7.8 100% For profit65.2 8.3 1.9 ! 11.9 12.8 100% Public 2-year and Private NFP <4y‡ ‡ ‡ ‡ ‡ 100% Family status in 2012 Unmarried, no dependent children67.6 11.0 7.4 7.7 6.3 100% Unmarried with dependent children67.1 12.9 5.3 6.5 8.3 100% Married, no dependent children74.9 9.3 3.8 5.5 6.5 100% Married with dependent children67.2 10.5 3.3 5.6 13.4 100% ‡ Reporting standards not met.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: GENDER, B2LFP12, RACE, MAJORS4Y, SECTOR9, B2MARCH and AGEATBA. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTE000. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12)Computation by NCES PowerStats on 3/27/2014. hfbb033 EMPLOYMENT STATUS AND INTENSITY: Percentage of 2007-08 bachelor's degree recipients who were not enrolled in 2012 who were employed and, of those, percentage distribution of employment intensity, by demographic and enrollment characteristics: 2012 One full-time job, not enrolled(%) One part-time job, not enrolled(%) Multiple jobs, not enrolled(%) Total Estimates Total84.5 7.8 7.7 100% Gender Male88.7 5.4 5.9 100% Female81.2 9.7 9.1 100% Race/ethnicity (with multiple) White84.5 7.8 7.8 100% Black83.9 5.2 10.9 100% Hispanic82.9 11.1 6.0 100% Asian92.0 5.9 2.1 !! 100% Other79.6 8.0 ! 12.4 100% Age at bachelor's degree 23 or younger85.5 7.5 7.0 100% 24-2982.7 8.5 8.8 100% 30 or older82.5 8.1 9.4 100% Field of study: undergraduate (10 categories) STEM90.7 4.3 5.0 100% Non-STEM83.3 8.5 8.2 100% Field of study: undergraduate (10 categories) Computer and information sciences91.6 3.6 ! 4.7 ! 100% Engineering and engineering technology92.9 2.6 ! 4.5 ! 100% Biological and physical sciences, science technology, mathematics, and agriculture sciences87.9 6.5 5.6 100% General studies and other82.2 11.3 6.5 ! 100% Social Sciences83.6 8.0 8.5 100% Humanities74.1 14.0 11.9 100% Health care fields76.0 13.5 10.4 100% Business89.9 4.8 5.3 100% Education81.8 7.4 10.8 100% Other Applied83.4 8.6 8.0 100% NPSAS institution type Public 4-year85.3 7.6 7.1 100% Private nonprofit 4-year82.9 8.3 8.8 100% For profit85.5 6.5 8.0 ! 100% Public 2-year and Private NFP <4y‡ ‡ ‡ 100% Family status in 2012 Unmarried, no dependent children84.9 7.3 7.8 100% Unmarried with dependent children79.6 10.0 10.4 100% Married, no dependent children86.4 6.5 7.0 100% Married with dependent children82.4 10.0 7.6 100% ‡ Reporting standards not met.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: GENDER, B2LFP12, RACE, MAJORS4Y, SECTOR9, B2MARCH and AGEATBA. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTE000. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12)Computation by NCES PowerStats on 3/27/2014. hfbb694 NUMBER OF JOBS AFTER BACHELOR'S: Among 2007-08 bachelor's degree recipients who worked after receiving the 2007-08 degree and had not enrolled, average number of jobs held since receipt of degree and percentage distribution by number of jobs held and demographic and enrollment characteristics: 2012 Number of jobs since 2007-08 bachelor's degree(Avg>0) Estimates Total2.1 Gender Male2.0 Female2.1 Race/ethnicity (with multiple) White2.1 Black1.9 Hispanic1.9 Asian1.9 Other2.3 Age at bachelor's degree 23 or younger2.2 24-292.0 30 or older1.7 Field of study: undergraduate (10 categories) STEM1.9 Non-STEM2.1 Field of study: undergraduate (10 categories) Computer and information sciences1.8 Engineering and engineering technology1.7 Biological and physical sciences, science technology, mathematics, and agriculture sciences2.2 General studies and other2.1 Social Sciences2.1 Humanities2.6 Health care fields1.9 Business1.9 Education2.1 Other Applied2.3 NPSAS institution type Public 4-year2.1 Private nonprofit 4-year2.1 For profit2.0 Public 2-year and Private NFP <4y‡ Family status in 2012 Unmarried, no dependent children2.2 Unmarried with dependent children1.9 Married, no dependent children2.0 Married with dependent children1.8 ‡ Reporting standards not met.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: MAJORS4Y, B2CPSTGRD, RACE, B2TOTJOB, AGEATBA, SECTOR9, B2MARCH and GENDER. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTE000. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12)Computation by NCES PowerStats on 3/27/2014. hfbb005 PERCENTAGE OF TIME EMPLOYED, UNEMPLOYED, AND OUT OF THE LABOR FORCE: Average percentage of time, in months, since graduation that 2007-08 bachelor's degree recipients who had not enrolled spent employed, unemployed, and out of the labor force, by demographic and enrollment characteristics: 2012 Percent of time employed from bachelor's to 2nd followup(Avg) Percent of time unemployed from bachelor's to 2nd followup(Avg) Percent of time out of the labor force from bachelor's to 2nd followup(Avg) Estimates Total84.0 5.8 10.2 Gender Male84.9 6.2 9.0 Female83.2 5.5 11.3 Race/ethnicity (with multiple) White86.0 5.1 8.9 Black78.9 7.2 13.9 Hispanic78.1 8.4 13.6 Asian74.2 8.2 17.5 Other81.3 7.2 11.5 Age at bachelor's degree 23 or younger85.4 5.6 9.1 24-2982.6 6.5 10.9 30 or older80.9 5.8 13.3 Field of study: undergraduate (10 categories) STEM87.5 4.6 8.0 Non-STEM83.4 6.0 10.6 Field of study: undergraduate (10 categories) Computer and information sciences86.4 3.7 9.9 Engineering and engineering technology92.1 3.9 4.0 Biological and physical sciences, science technology, mathematics, and agriculture sciences82.0 6.3 11.7 General studies and other83.3 5.1 11.7 Social Sciences80.1 7.0 12.9 Humanities81.6 7.5 10.9 Health care fields88.8 2.5 8.8 Business84.6 5.7 9.7 Education82.1 6.6 11.3 Other Applied82.6 6.5 10.8 NPSAS institution type Public 4-year84.1 5.6 10.4 Private nonprofit 4-year84.5 6.0 9.5 For profit80.0 7.4 12.7 Public 2-year and Private NFP <4y‡ ‡ ‡ Family status in 2012 Unmarried, no dependent children83.9 7.2 8.9 Unmarried with dependent children85.5 5.7 8.9 Married, no dependent children85.7 4.5 9.8 Married with dependent children82.0 4.0 14.0 ‡ Reporting standards not met.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: B2PCEMP, B2CPSTGRD, RACE, SECTOR9, AGEATBA, B2PCUNEM, B2MARCH, B2PCOLF, MAJORS4Y and GENDER. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTE000. SOURCE: U.S. Department of Education, National Center for Education Statistics, 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12)Computation by NCES PowerStats on 3/27/2014. hfbbb71 Highest degree program enrollment after bachelor's degree, as of 2009 by Bachelor's degree major (collapsed), 2007-08. Had not enrolled in degree program as of 2009(%) Undergraduate certificate(%) Associate's degree(%) Additional bachelor's degree & Post-bachelor's certificate & Master's degree(%) Post-master's certificate & First-professional degree & Doctoral degree(%) Total Estimates Total68.9 1.3 0.8 22.6 6.4 100% Bachelor's degree major (collapsed), 2007-08 Computer and information sciences84.9 0.2 !! 0.0 !! 12.6 2.3 ! 100% Engineering and engineering technology72.0 1.5 !! 0.6 !! 18.5 7.5 100% Bio/physical science/science tech/math/agriculture49.4 1.7 0.7 ! 24.6 23.7 100% General studies and other64.8 2.6 ! 0.4 !! 27.0 5.2 100% Social sciences59.7 1.4 1.1 27.2 10.5 100% Humanities60.7 1.9 1.0 ! 28.3 8.1 100% Health care fields69.5 0.7 ! 1.0 ! 23.5 5.3 100% Business78.9 1.2 0.6 ! 16.7 2.6 100% Education69.0 0.9 ! 0.2 !! 29.0 0.8 ! 100% Other applied74.6 1.1 1.1 20.6 2.6 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: B1HIENR and MAJORS4Y. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, B&B: 09 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 6/27/2011. hfbbb62 Highest degree program enrollment after bachelor's degree, by age at 2007-08 bachelor's degree completion. Had not enrolled in degree program as of 2009(%) Undergraduate certificate(%) Associate's degree(%) Additional bachelor's degree & Post-bachelor's certificate & Master's degree(%) Post-master's certificate & First-professional degree & Doctoral degree(%) Total Estimates Total68.9 1.3 0.8 22.6 6.4 100% Age at 2007-08 bachelor's degree completion 22 or younger64.0 1.0 0.8 24.8 9.4 100% 23-2473.3 1.8 0.9 19.2 4.7 100% 25-2975.4 1.1 ! 1.0 ! 19.0 3.4 100% 30 or older72.4 1.5 0.3 ! 24.1 1.6 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: B1HIENR and MAJORS4Y. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, B&B: 09 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 6/27/2011. hfbb033 K-12 teaching experience as of 2009, by dependency and marital status in 2007-08. Taught before bachelor's degree(%) Taught since completing bachelor's(%) Certified or prepared to teach(%) Considered teaching(%) Not considered(%) Total Estimates Total2.5 8.0 4.8 9.4 75.2 100% Dependency and marital status (separated is unmarried) in 2007-08 Unmarried with no dependents2.9 5.8 4.5 12.3 74.5 100% Unmarried with dependents4.8 7.0 2.8 14.3 71.1 100% Married with no dependents4.8 8.7 5.9 7.9 72.8 100% Married with dependents3.4 7.9 5.1 10.9 72.7 100% The names of the variables used in this table are: DEPEND5B and B1TSTAT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, B&B: 09 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 6/27/2011. hfbb694 Average>0 Earned income in 2009, median>0 Earned income in 2009 by Highest level of education ever expected as of 2007-08. Earned income in 2009(Avg>0) Earned income in 2009(Median>0) Estimates Total34,795.2 32,000.0 Highest level of education ever expected as of 2007-08 Bachelor's degree35,926.9 32,000.0 Post-BA or post-master certificate34,542.3 34,450.0 Master's degree35,912.4 33,785.4 Doctoral degree29,071.9 26,811.2 First-professional degree30,704.7 27,500.0 The names of the variables used in this table are: HIGHLVEX and B1ERNINC. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, B&B: 09 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 6/27/2011. hfbb005 Cumulative loan amount borrowed for undergraduate through 2007-08 with (percent > .5), median Cumulative amount owed for undergraduate as of 2008-09 by Looking for a job in 2009 and Employment and enrollment status in 2009. Cumulative loan amount borrowed for undergraduate through 2007-08(%>0.5) Cumulative amount owed for undergraduate as of 2008-09(Median) Estimates Total65.6 19,857.0 Looking for a job in 2009 Not looking for a job63.5 18,108.0 Looking for a job69.1 22,000.0 Employment and enrollment status in 2009 One full-time job66.5 19,439.0 One part-time job62.3 19,239.0 Multiple jobs70.3 21,555.0 Unemployed65.8 22,463.0 Out of the labor force55.8 16,875.0 The names of the variables used in this table are: B1LFP09, B1SEARCH, B1OWAMT1 and B1BORAT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, B&B: 09 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 6/27/2011. hfbbb71 Highest degree attained by 2003 by Highest level of education expected in 1992-93. Bachelor's degree(%) Post-baccalaureate certificate(%) Master's degree(%) Post-master's certificate(%) First-professional degree(%) Doctoral degree(%) Estimates Total35.0 0.9 48.8 0.8 9.7 4.9 Highest level of education expected in 1992-93 Bachelor's degree or less42.1 1.3 !! 48.8 0.1 !! 5.8 ! 2.0 ! Master's degree or equivalent36.5 1.1 ! 58.4 0.7 ! 2.6 0.8 ! Doctorate degree (PhD, EdD)34.1 0.8 !! 43.8 0.9 9.6 10.9 First professional degree23.5 0.7 !! 20.3 0.9 ! 45.4 9.1 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: ANYHILVL and B3HDG03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTC000.Source: U.S. Department of Education, National Center for Education Statistics, B&B: 93/03 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 9/10/2012. chabha4f2 Undergraduate loans: Total amount 2003 with (percent > 1), average Undergraduate loans: Total amount 2003 by Enrolled in degree program after bachelor's degree as of 2003. Undergraduate loans: Total amount 2003(%>1) Undergraduate loans: Total amount 2003(Avg) Estimates Total51.3 5,338.0 Enrolled in degree program after bachelor's degree as of 2003 Graduate only50.9 5,301.2 Both graduate and undergraduate55.9 5,703.9 The names of the variables used in this table are: B3UGLN and B3ENRPG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTC000.Source: U.S. Department of Education, National Center for Education Statistics, B&B: 93/03 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 9/10/2012. bmabhfd523 Highest degree attained by 2003 by Gender of student. Bachelor's degree(%) Post-baccalaureate certificate(%) Master's degree(%) Post-master's certificate(%) First-professional degree(%) Doctoral degree(%) Estimates Total35.0 0.9 48.8 0.8 9.7 4.9 Gender of student Male33.2 1.1 ! 45.9 0.6 ! 12.3 7.0 Female36.3 0.8 ! 51.1 0.9 7.6 3.2 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: GENDER and B3HDG03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTC000.Source: U.S. Department of Education, National Center for Education Statistics, B&B: 93/03 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 9/10/2012. bmabhf014 Average>0 Current or most recent salary 2003 by Highest degree attained by 2003. Current or most recent salary 2003(Avg>0) Estimates Total57,656.9 Highest degree attained by 2003 Bachelor's degree51,254.2 Post-baccalaureate certificate‡ Master's degree56,660.6 Post-master's certificate53,783.9 First-professional degree84,122.4 Doctoral degree64,058.0 ‡ Reporting standards not met.The names of the variables used in this table are: B3HDG03 and B3CRSAL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTC000.Source: U.S. Department of Education, National Center for Education Statistics, B&B: 93/03 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 9/10/2012. bmabhf645 Average Post baccalaureate loans: Total amount 2003 by Highest degree attained by 2003. Post baccalaureate loans: Total amount 2003(Avg) Estimates Total16,471.5 Highest degree attained by 2003 Bachelor's degree8,450.8 Post-baccalaureate certificate‡ Master's degree10,763.4 Post-master's certificate8,846.4 ! First-professional degree64,035.1 Doctoral degree26,122.6 ‡ Reporting standards not met.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: B3GRLN and B3HDG03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTC000.Source: U.S. Department of Education, National Center for Education Statistics, B&B: 93/03 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 9/10/2012. bmabhf741 Average Number of books - total, average Number of audio/video - total, average Number of books - acquired, average Number of audio/video - acquired, average Expenditures - books, average Expenditures - audio/video, average Expenditures - total by Charter school identifier, Collapsed urban-centric school locale code, and Total K-12 and ungraded enrollment in school. Number of books - total(Avg) Number of audio/video - total(Avg) Number of books - acquired(Avg) Number of audio/video - acquired(Avg) Expenditures - books(Avg) Expenditures - audio/video(Avg) Expenditures - total(Avg) Estimates Total12,776.8 473.8 522.1 24.9 6,006.2 488.1 9,344.5 Charter school identifier School is a public charter school10,361.9 363.3 824.8 90.8 !! 6,264.5 614.4 9,030.4 School is not a public charter school12,834.9 476.5 514.8 23.3 6,000.0 485.1 9,352.0 Collapsed urban-centric school locale code City12,794.8 429.7 587.1 30.6 6,478.6 555.8 10,235.5 Suburb14,139.9 534.8 562.3 23.7 6,477.4 512.4 9,607.4 Town12,702.3 477.2 452.6 18.5 5,597.0 427.9 9,815.8 Rural11,684.8 454.6 470.9 24.3 5,448.9 444.1 8,297.6 Total K-12 and ungraded enrollment in school Less than 100 Students7,370.1 267.6 330.0 11.1 ! 2,754.8 119.5 4,153.2 100 to 199 Students10,339.5 400.4 380.4 13.1 3,641.3 281.2 ! 5,510.9 200 to 499 Students11,204.9 357.6 443.1 17.5 4,647.1 307.0 7,530.0 500 to 749 Students13,831.0 483.8 574.3 20.6 6,181.8 583.1 9,868.7 750 to 999 Students14,965.1 631.0 621.8 32.4 7,787.5 565.1 10,885.5 1000 or more Students17,461.3 853.3 751.4 66.7 11,497.0 1,127.2 17,498.8 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: M0109, ENRK12UG, CHARFLAG, M0106, M0112, M0107, M0110, M0111, URBANS12 and M0108. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES PowerStats on 1/28/2014. cgabe2b2 Average Number of computer workstations, average Number of computers with internet by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level and Total K-12 and ungraded enrollment in the district. Number of computer workstations(Avg) Number of computers with internet(Avg) Estimates Total18.1 17.9 Charter school identifier School is a public charter school15.3 16.3 School is not a public charter school18.2 17.9 Collapsed urban-centric school locale code City16.6 16.3 Suburb21.1 20.1 Town18.1 18.3 Rural16.8 17.0 Four-category school level Primary12.3 12.6 Middle22.7 22.2 High33.2 31.0 Combined14.6 15.4 Total K-12 and ungraded enrollment in school Less than 100 Students10.1 11.9 100 to 199 Students13.1 13.8 200 to 499 Students14.3 14.5 500 to 749 Students16.4 16.2 750 to 999 Students22.3 21.8 1000 or more Students39.0 34.6 The names of the variables used in this table are: M0075, SCHLEVE2, URBANS12, CHARFLAG, ENRK12UG and M0076. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES PowerStats on 1/28/2014. cgabec93 Access to online, licensed databases - classroom by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level, Total K-12, and ungraded enrollment in school and Percentage of enrolled students approved for the NSLP at school. Yes(%) No(%) Total Estimates Total94.8 5.2 100% Charter school identifier School is a public charter school85.3 14.7 100% School is not a public charter school95.0 5.0 100% Collapsed urban-centric school locale code City95.7 4.3 100% Suburb95.5 4.5 100% Town94.0 6.0 100% Rural93.7 6.3 100% Four-category school level Primary95.9 4.1 100% Middle94.3 5.7 100% High93.0 7.0 100% Combined91.0 9.0 100% Total K-12 and ungraded enrollment in school Less than 100 Students90.6 9.4 ! 100% 100 to 199 Students93.9 6.1 ! 100% 200 to 499 Students94.4 5.6 100% 500 to 749 Students95.5 4.5 100% 750 to 999 Students95.6 4.4 100% 1000 or more Students95.6 4.4 100% Percentage of enrolled students approved for the NSLP at school Less than 35%94.5 5.5 100% 35% to 49%94.3 5.7 100% 50% to 74%96.1 3.9 100% 75% or more94.0 6.0 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: NSLAPP_S, M0078, SCHLEVE2, URBANS12, CHARFLAG and ENRK12UG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES PowerStats on 1/28/2014. cgabe3f4 Flexibility of scheduling for classes/activities by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level, Total K-12, and ungraded enrollment in school and Percentage of enrolled students approved for the NSLP at school. Only flexible scheduling (available as needed)(%) Only regular scheduling (previously specified times)(%) Both flexible and regular scheduling(%) Total Estimates Total19.4 19.2 61.5 100% Charter school identifier School is a public charter school25.7 13.9 60.4 100% School is not a public charter school19.2 19.3 61.5 100% Collapsed urban-centric school locale code City17.7 20.4 61.9 100% Suburb20.4 23.4 56.2 100% Town18.5 18.0 63.5 100% Rural20.1 15.3 64.6 100% Four-category school level Primary5.0 30.2 64.8 100% Middle29.8 3.3 66.9 100% High53.6 2.6 43.8 100% Combined22.7 8.0 69.3 100% Total K-12 and ungraded enrollment in school Less than 100 Students23.4 14.8 61.7 100% 100 to 199 Students17.1 13.1 69.8 100% 200 to 499 Students11.0 23.0 65.9 100% 500 to 749 Students15.9 22.4 61.7 100% 750 to 999 Students27.0 16.2 56.8 100% 1000 or more Students52.1 4.6 43.3 100% Percentage of enrolled students approved for the NSLP at school Less than 35%22.6 16.9 60.5 100% 35% to 49%20.7 17.3 62.0 100% 50% to 74%16.7 18.8 64.5 100% 75% or more16.0 24.6 59.4 100% The names of the variables used in this table are: NSLAPP_S, M0033, SCHLEVE2, URBANS12, CHARFLAG and ENRK12UG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES PowerStats on 1/28/2014. cgabedb5 Community use, during week, outside school hours by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level, Total K-12, and ungraded enrollment in school and Percentage of enrolled students approved for the NSLP at school. Yes(%) No(%) Total Estimates Total48.9 51.1 100% Charter school identifier School is a public charter school52.5 47.5 100% School is not a public charter school48.8 51.2 100% Collapsed urban-centric school locale code City55.9 44.1 100% Suburb47.0 53.0 100% Town52.9 47.1 100% Rural44.8 55.2 100% Four-category school level Primary46.4 53.6 100% Middle52.1 47.9 100% High51.9 48.1 100% Combined48.6 51.4 100% Total K-12 and ungraded enrollment in school Less than 100 Students38.7 61.3 100% 100 to 199 Students41.1 58.9 100% 200 to 499 Students49.2 50.8 100% 500 to 749 Students47.5 52.5 100% 750 to 999 Students54.2 45.8 100% 1000 or more Students56.0 44.0 100% Percentage of enrolled students approved for the NSLP at school Less than 35%54.7 45.3 100% 35% to 49%47.6 52.4 100% 50% to 74%41.8 58.2 100% 75% or more50.7 49.3 100% The names of the variables used in this table are: NSLAPP_S, M0028, SCHLEVE2, URBANS12, CHARFLAG and ENRK12UG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES PowerStats on 1/28/2014. cgabe661 Average and Median full-time and part-time teacher salary by Collapsed urban-centric district locale code. Teacher salary - lowest paid full-time(Avg>0) Teacher salary - lowest paid full-time(Median>0) Teacher salary - highest paid full-time(Avg>0) Teacher salary - highest paid full-time(Median>0) Estimates Total35,800.1 34,274.0 62,691.3 60,447.0 Collapsed urban-centric district locale code City36,679.4 35,000.0 58,978.2 55,000.0 Suburb40,451.1 39,881.0 78,636.3 78,256.0 Town35,430.1 34,009.0 62,266.8 62,565.0 Rural33,802.9 32,605.0 57,614.6 56,894.0 The names of the variables used in this table are: URBAND12, D0512 and D0511. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2011-12 Computation by NCES PowerStats on 1/10/2014. baabeee2 Teacher Benefits - Tuition Reimbursement by Number of schools in district, Collapsed urban-centric district locale code, and by Total K-12 student enrollment. No(%) Yes(%) Total Estimates Total55.0 45.0 100% Number of schools in district, post-collapsing 1 or less57.8 42.2 100% 2 or 352.5 47.5 100% 4 or 553.2 46.8 100% 6 to 950.5 49.5 100% 10 to 1958.9 41.1 100% 20 or more61.6 38.4 100% Collapsed urban-centric district locale code City58.5 41.5 100% Suburb42.4 57.6 100% Town54.6 45.4 100% Rural59.1 40.9 100% Total student enrollment- K-12 grade levels Less than 250 students59.9 40.1 100% 250 to 999 students55.8 44.2 100% 1,000 to 1,999 students48.4 51.6 100% 2,000 to 4,999 students49.9 50.1 100% 5,000 to 9,999 students58.6 41.4 100% 10,000 or more students63.9 36.1 100% The names of the variables used in this table are: URBAND12, D0418, AG_NOSC2 and D0519. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2011-12 Computation by NCES PowerStats on 1/10/2014. baabeaa3 Average Count of dismissed teachers by Number of schools in district, Collapsed urban-centric district locale code, and Percentage of students in district approved for the NSLP. Count of dismissed teachers - Total(Avg) Estimates Total3.5 Number of schools in district, post-collapsing Less than 10 schools1.5 10 to 50 schools11.2 51 to 100 schools62.8 More than 100 schools‡ Collapsed urban-centric district locale code City7.1 Suburb6.6 Town2.3 Rural1.5 Percentage of students in district approved for the NSLP Less than 35%2.9 35% to 49%3.1 50% to 74%4.6 75% or more3.8 ‡ Reporting standards not met. The names of the variables used in this table are: URBAND12, AG_NOSC2, NSLAPP_D and D0480. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2011-12 Computation by NCES PowerStats on 1/10/2014. baabe934 Teachers association or union agreement by Total student enrollment- K-12 grade levels and Number of schools in district. Yes, meet-and-confer(%) Yes, collective bargaining(%) Yes, other type of agreement(%) No agreement(%) Total Estimates Total8.4 50.2 1.4 40.0 100% Total student enrollment- K-12 grade levels Less than 1,0007.3 40.5 1.6 50.5 100% 1,001 to 4,999 students9.0 62.8 1.1 27.1 100% 5,000 to 9,999 students11.3 60.7 1.4 26.6 100% 10,000 to 49,999 students11.5 56.4 1.4 30.8 100% 50,000 to 199,999 students12.2 48.2 2.8 36.8 100% More than 200,000 students‡ ‡ ‡ ‡ 100% Number of schools in district, post-collapsing Less than 20 schools8.2 50.0 1.4 40.4 100% 20 to 100 schools11.8 53.2 2.0 33.1 100% 101 to 200 schools‡ ‡ ‡ ‡ 100% More than 200 schools‡ ‡ ‡ ‡ 100% ‡ Reporting standards not met. The names of the variables used in this table are: D0418, AG_NOSC2 and D0452. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2011-12 Computation by NCES PowerStats on 1/10/2014. baabed65 Salary Incentive to retain teachers in a less desirable location by Percentage of students in district approved for the NSLP and Collapsed urban-centric district locale code. Yes(%) No(%) Total Estimates Total5.6 94.4 100% Percentage of students in district approved for the NSLP Less than 35%2.7 97.3 100% 35 to 49 %3.2 96.8 100% 50% to 74%5.9 94.1 100% More than 75%12.4 87.6 100% Collapsed urban-centric district locale code City11.3 88.7 100% Suburb4.2 95.8 100% Town3.8 96.2 100% Rural5.1 94.9 100% The names of the variables used in this table are: URBAND12, D0526 and NSLAPP_D. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2011-12 Computation by NCES PowerStats on 1/10/2014. baabed21 Percentage of enrolled students with an IEP with (percent >0.5) by Charter school identifier. Percentage of enrolled students with an IEP(%>0.5) Estimates Total97.7 Charter school identifier School is a public charter school97.3 School is not a public charter school97.7 The names of the variables used in this table are: IEP and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2011-12. Computation by NCES PowerStats on 11/16/2017. bhnbhmk222 Q1 School offers 12th grade with (percent =1) by Three-category level of school based on grade levels offered. Q1 School offers 12th grade(%=1) Estimates Total26.9 Three-category level of school based on grade levels offered Elementary‡ Secondary84.8 Combined89.1 ‡ Reporting standards not met. The names of the variables used in this table are: S0037 and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2011-12. Computation by NCES PowerStats on 11/16/2017. bhnbhmmea3 Q17d Programs offered: Advanced Placement (AP) courses for college credit by Categorical measure of total K-12 enrollment. No(%) Yes(%) Total Estimates Total83.216.8100% Categorical measure of total K-12 enrollment 1-4994.25.8 !100% 50-9991.88.2100% 100-14990.010.0100% 150-19985.015.0100% 200-34989.110.9100% 350-49991.88.2100% 500-74987.812.2100% 750-99980.819.2100% 1,000-1,19958.741.3100% 1,200-1,49941.558.5100% 1,500-1,99913.386.7100% 2,000 or more7.892.2100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: S0095 and SCHSIZE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2011-12. Computation by NCES PowerStats on 11/16/2017. bhnbhmnda4 Percentage of enrolled students who are LEP with (percent <2, excluding zero) by Urban-centric school locale code. Percentage of enrolled students who are LEP(%<2) Estimates Total25.3 Urban-centric school locale code City, Large12.1 City, Midsize15.5 City, Small18.1 Suburb, Large29.5 Suburb, Midsize26.0 Suburb, Small28.2 Town, Fringe31.2 Town, Distant30.9 Town, Remote25.9 Rural, Fringe34.9 Rural, Distant28.4 Rural, Remote15.4 The names of the variables used in this table are: SLOCP12 and LEP. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2011-12. Computation by NCES PowerStats on 11/16/2017. bhnbhmp645 Q44 School had students enrolled who received Title I services by Q16a School has a magnet program. No(%) Yes(%) Total Estimates Total42.657.4100% Q16a School has a magnet program No42.357.7100% Yes47.652.4100% The names of the variables used in this table are: S0090 and S0275. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2011-12. Computation by NCES PowerStats on 11/16/2017. bhnbhmpb21 Percentage of enrolled students with an IEP with (percent >0.5) by Q13 School type. Percentage of enrolled students with an IEP(%>0.5) Estimates Total63.4 Q13 School type Regular school60.9 Montessori school52.4 Special Program Emphasis School‡ Special Education school100.0 Alternative/Other school74.1 Early Childhood Program or Day Care Center‡ ‡ Reporting standards not met. The names of the variables used in this table are: IEP and S0055. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2011-12. Computation by NCES PowerStats on 11/16/2017. bhnbhmba2 Four-category level of school based on grade levels offered by Estimated number of full-time equivalent teachers in the school. Primary(%) Middle(%) High(%) Combined(%) Total Estimates Total56.21.2 !10.632.0100% Estimated number of full-time equivalent teachers in the school 0 to 2061.31.4 !7.829.5100% 21 to 4052.3‡15.931.2100% 41 to 6020.3‡38.441.2100% 61 to 8015.3 !‡27.856.9100% More than 80‡‡11.5 !86.9100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: NUMTCH and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2011-12. Computation by NCES PowerStats on 11/16/2017. bhnbhmm013 Highest tuition charged by private school 0 by Categorical measure of total K-12 enrollment. Highest tuition charged by private school(Avg) Estimates Total8,253.9 Categorical measure of total K-12 enrollment 1-497,556.4 50-997,211.7 100-1498,068.8 150-1996,591.9 200-3499,312.8 350-49910,670.1 500-74910,761.1 750-99911,738.7 1,000-1,199‡ 1,200-1,499‡ 1,500-1,999‡ 2,000 or more‡ ‡ Reporting standards not met. The names of the variables used in this table are: TUITIN and SCHSIZE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2011-12. Computation by NCES PowerStats on 11/16/2017. bhnbhmaac4 Q34 12th grades enrolled 2010-11 with (percent =1) by Three-level private school typology. Q34 12th grades enrolled 2010-11(%=1) Estimates Total33.9 Three-level private school typology Catholic19.7 Other religious37.4 Nonsectarian41.6 The names of the variables used in this table are: RELIG and S0133. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2011-12. Computation by NCES PowerStats on 11/16/2017. bhnbhmnd85 Estimated number of full-time equivalent teachers in the school by Percentage of enrolled students with an IEP. 0 to 20(%) 21 to 40(%) 41 to 60(%) 61 to 80(%) More than 80(%) Total Estimates Total78.314.43.81.62.0100% Percentage of enrolled students with an IEP 0% to 5%79.113.83.31.82.0100% >5% to 10%74.416.55.51.5 !2.0 !100% >10% to 15%69.620.44.6 !3.2 !‡100% >15% to 20%71.718.3 !!7.2 !!‡‡100% >20%85.210.62.7 !!‡1.4 !100% ! Interpret data with caution. Estimate is unstable because the stanard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: NUMTCH and IEP. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2011-12. Computation by NCES PowerStats on 11/16/2017. bhnbhm421 School has students with an Individual Education Plan (IEP), by Charter school identifier, Collapsed urban-centric school locale code, Four-category level of school based on grade levels offered , Total number of students enrolled in grades K-12, Percentage of enrolled students approved for the NSLP at school, and whether or not the school participates in the National School Lunch Program. School has students with and Individual Education Plan (IEP)(%>0.5) Estimates Total98.0 Charter school identifier School is a public charter school98.0 School is not a public charter school98.0 Collapsed urban-centric school locale code City98.9 Suburb97.6 Town98.3 Rural97.5 Four-category level of school based on grade levels offered Primary98.5 Middle98.8 High96.8 Combined96.4 Total number of students enrolled in grades K-12 0 to 99 Students87.8 100 to 199 Students96.5 200 to 499 Students99.1 500 to 749 Students98.8 750 to 999 Students98.9 1,000 to 9,999 Students99.5 Percentage of enrolled students approved for the NSLP at school Less than 35%97.9 35% to 49%98.0 50% to 74%98.9 More than 74%98.4 School participates in the National School Lunch Program Yes98.3 No89.6 The names of the variables used in this table are: NSLAPP_S, S0052, S0272, SCHLEVE2, URBANS12, CHARFLAG and S0250. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2011-12 Computation by NCES PowerStats on 1/28/2014. cgabe932 12th graders enrolled 2010-11 with average Percentage of students who went to a 4-yr college by Three-level private school typology, Collapsed urban-centric school locale code, and Three-category level of school based on grade levels offered. 12th graders enrolled 2010-11(%>0.5) Percentage of students who went to a 4-yr college(Avg) Estimates Total33.9 64.3 Three-level private school typology Catholic19.7 81.4 Other religious37.4 60.8 Nonsectarian41.6 62.1 Collapsed urban-centric school locale code City34.4 72.9 Suburb30.0 62.3 Town35.4 49.2 Rural37.2 62.0 Three-category level of school based on grade levels offered Secondary93.1 67.8 Combined73.4 63.5 The names of the variables used in this table are: S0136, SCHLEVEL, URBANS12, RELIG and S0133. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2011-12 Computation by NCES PowerStats on 1/28/2014. bkbbeb63 Percentage of schools offering Advanced Placement (AP) courses for college credit and International Baccalaureate (IB), by Three-level private school typology, Collapsed urban-centric school locale code, Three-category level of school based on grade levels offered and Total number of students enrolled in grades K-12, for Students enrolled in ninth grade, Students enrolled in twelfth grade, Students enrolled in tenth grade, and Students enrolled in eleventh grade. Programs offered: Advanced Placement (AP) courses for college credit(%>0.5) Programs offered: International Baccalaureate (IB)(%>0.5) Estimates Total52.9 1.7 ! Three-level private school typology Catholic86.1 3.2 ! Other religious46.3 ‡ Nonsectarian47.5 1.5 ! Collapsed urban-centric school locale code City60.5 3.7 ! Suburb54.8 ‡ Town41.5 ‡ Rural46.6 ‡ Three-category level of school based on grade levels offered Elementary‡ ‡ Secondary68.6 ‡ Combined46.5 ‡ Total number of students enrolled in grades K-12 0 to 99 Students29.9 ‡ 100 to 199 Students44.0 ‡ 200 to 499 Students69.2 ‡ 500 to 749 Students92.4 ‡ 750 to 9,999 Students85.2 ‡ ‡ Reporting standards not met. ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: S0730, RELIG, S0095, S0096, S0052, S0728, URBANS12, S0726, S0732 and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2011-12 Computation by NCES PowerStats on 1/28/2014. cgabe394 School has English Language Learners (ELL)/LEP students by Charter school identifier, Collapsed urban-centric school locale code, Four-category level of school based on grade levels offered, and Percentage of students who are of a racial/ethnic minority. School has English Language Learners (ELL)/LEP students(%>0.25) Estimates Total73.9 Charter school identifier School is a public charter school65.6 School is not a public charter school74.3 Collapsed urban-centric school locale code City83.2 Suburb84.1 Town71.6 Rural59.2 Four-category level of school based on grade levels offered Primary79.5 Middle78.5 High66.6 Combined45.0 Percentage of students who are of a racial/ethnic minority Less than 35%63.6 35% to 49%85.6 50% to 74%85.4 75% or more82.2 The names of the variables used in this table are: SCHLEVE2, S0260, URBANS12, CHARFLAG and MINENR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2011-12 Computation by NCES PowerStats on 1/28/2014. cgabed45 Percentage of schools that had students enrolled who received Title I services by School has a magnet program, School has special requirements when admitting students, Percentage of students who went to a 4-yr college , Charter school identifier, Four-category level of school based on grade levels offered, and Collapsed urban-centric school locale code. School had students enrolled who received Title I services(%>0.25) Estimates Total57.4 Q16a School has a magnet program No57.7 Yes52.4 Q14a School has special requirements when admitting students No58.2 Yes53.5 Q26b Percentage of students who went to a 4-yr college Less than 35%42.4 35% to 49%35.7 50% to 74%37.4 75% or more39.2 Charter school identifier School is a public charter school66.3 School is not a public charter school56.9 Four-category level of school based on grade levels offered Primary70.4 Middle44.1 High31.0 Combined59.4 Collapsed urban-centric school locale code City62.0 Suburb49.2 Town60.8 Rural59.0 The names of the variables used in this table are: S0080, S0090, URBANS12, SCHLEVE2, CHARFLAG, S0136 and S0275. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2011-12 Computation by NCES PowerStats on 1/28/2014. cgabe1d1 Q78.Gender by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Male(%) Female(%) Total Estimates Total23.7 76.3 100% Charter school identifier School is a public charter school25.1 74.9 100% School is not a public charter school23.6 76.4 100% Collapsed urban-centric school locale code City23.4 76.6 100% Suburb23.0 77.0 100% Town24.2 75.8 100% Rural24.5 75.5 100% Four-category school level Primary10.7 89.3 100% Middle27.4 72.6 100% High41.7 58.3 100% Combined31.2 68.8 100% Collapsed total K-12 and ungraded enrollment in school 1-4940.5 59.5 100% 50-9929.0 71.0 100% 100-14926.0 74.0 100% 150-19926.3 73.7 100% 200-34919.3 80.7 100% 350-49916.5 83.5 100% 500-74918.1 81.9 100% 750-99922.7 77.3 100% 1,000-1,19930.3 69.7 100% 1,200-1,49933.7 66.3 100% 1,500-1,99939.5 60.5 100% 2,000 or more42.9 57.1 100% The names of the variables used in this table are: SCHSIZE, URBANS12, T0525, SCHLEVE2 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhna142 Q1. Teacher's main position at the school by Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Administrator(%) Other(%) Total Estimates Total92.7 2.4 0.2 !! 4.6 100% Four-category school level Primary90.9 2.4 0.1 !! 6.6 100% Middle94.4 2.0 0.1 ! 3.5 100% High95.0 2.4 0.4 2.1 100% Combined92.1 3.8 0.7 !! 3.4 100% Collapsed total K-12 and ungraded enrollment in school 1-4985.3 9.9 ! 1.2 !! 3.6 !! 100% 50-9984.8 8.4 ! 0.7 !! 6.1 100% 100-14985.9 5.9 ! 0.4 ! 7.8 100% 150-19990.3 4.4 0.5 !! 4.8 100% 200-34989.7 2.9 0.3 !! 7.1 100% 350-49991.1 2.8 0.1 !! 6.0 100% 500-74992.1 2.0 0.2 ! 5.6 100% 750-99994.6 2.0 0.1 !! 3.2 100% 1,000-1,19995.8 1.5 0.2 ! 2.4 100% 1,200-1,49995.3 2.4 0.2 !! 2.1 100% 1,500-1,99996.3 1.7 0.3 !! 1.7 100% 2,000 or more96.3 1.9 0.5 !! 1.3 ! 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: SCHSIZE, SCHLEVE2 and T0025. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhnf823 Estimated number of full-time equivalent teachers in the school 1, Q70.Base salary for the entire 2011-12 school year 1 by Collapsed total K-12 and ungraded enrollment in school and Collapsed urban-centric school locale code. Estimated number of full-time equivalent teachers in the school(Avg>0) Q70.Base salary for the entire 2011-12 school year(Avg>0) Estimates Total53.8 52,515.3 Collapsed total K-12 and ungraded enrollment in school 1-496.0 43,967.9 50-9910.3 43,498.0 100-14914.1 44,129.3 150-19917.6 45,248.0 200-34924.0 49,992.3 350-49931.5 52,090.3 500-74941.9 52,415.8 750-99956.2 52,478.0 1,000-1,19970.5 53,759.0 1,200-1,49984.8 55,931.6 1,500-1,999109.8 55,108.6 2,000 or more145.5 58,129.8 Collapsed urban-centric school locale code City57.1 53,972.1 Suburb62.5 58,073.8 Town42.5 47,508.0 Rural44.9 46,577.9 The names of the variables used in this table are: NUMTCH, URBANS12, SCHSIZE and T0508. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhnh4c4 Q46a.Prof dev: reading instruction by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. No(%) Yes(%) Total Estimates Total43.3 56.7 100% Charter school identifier School is a public charter school44.5 55.5 100% School is not a public charter school43.3 56.7 100% Collapsed urban-centric school locale code City38.2 61.8 100% Suburb43.9 56.1 100% Town43.7 56.3 100% Rural47.6 52.4 100% Four-category school level Primary29.3 70.7 100% Middle52.1 47.9 100% High59.6 40.4 100% Combined52.6 47.4 100% Collapsed total K-12 and ungraded enrollment in school 1-4949.4 50.6 100% 50-9950.9 49.1 100% 100-14947.3 52.7 100% 150-19951.5 48.5 100% 200-34938.4 61.6 100% 350-49934.5 65.5 100% 500-74939.1 60.9 100% 750-99944.1 55.9 100% 1,000-1,19949.0 51.0 100% 1,200-1,49955.3 44.7 100% 1,500-1,99958.5 41.5 100% 2,000 or more56.6 43.4 100% The names of the variables used in this table are: T0344, SCHSIZE, URBANS12, SCHLEVE2 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhnm8d5 Highest degree earned by Four-category school level and Program type of school. Associate's degree or no college degree(%) Bachelor's degree(%) Master's degree(%) Education specialist or Certificate of Advanced Graduate Studies(%) Doctorate or Professional degree(%) Total Estimates Total3.8 39.9 47.7 7.6 1.1 100% Four-category school level Primary3.2 41.2 47.3 8.0 0.4 100% Middle3.5 38.8 48.5 7.9 1.3 100% High4.8 36.8 49.6 6.8 2.1 100% Combined5.1 46.9 39.4 7.1 1.4 ! 100% Program type of school Regular3.6 40.0 47.9 7.5 1.0 100% Montessori‡ ‡ ‡ ‡ ‡ 100% Special program emphasis2.2 ! 39.6 46.3 10.0 1.9 ! 100% Special Education3.2 ! 25.5 57.6 12.9 0.8 !! 100% Career/Technical/Vocational Education24.8 32.2 34.9 6.5 1.6 !! 100% Alternative4.6 44.4 40.8 8.1 2.1 ! 100% Early Childhood Program/Daycare Center‡ ‡ ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: PGMTYPE, SCHLEVE2 and HIDEGR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhnpf171 Q78.Gender by Three-level private school typology, Four-category school level, Collapsed urban-centric school locale code and Collapsed total K-12 and ungraded enrollment in school. Male(%) Female(%) Total Estimates Total25.2 74.8 100% Three-level private school typology Catholic21.5 78.5 100% Other religious25.5 74.5 100% Nonsectarian28.7 71.3 100% Four-category school level Primary13.6 86.4 100% Middle44.6 55.4 100% High44.4 55.6 100% Combined29.7 70.3 100% Collapsed urban-centric school locale code City27.1 72.9 100% Suburb23.5 76.5 100% Town18.9 81.1 100% Rural26.5 73.5 100% Collapsed total K-12 and ungraded enrollment in school 1-4922.9 77.1 100% 50-9921.6 78.4 100% 100-14917.1 82.9 100% 150-19923.9 76.1 100% 200-34921.4 78.6 100% 350-49922.3 77.7 100% 500-74931.9 68.1 100% 750-99933.9 66.1 100% 1,000-1,19939.6 60.4 100% 1,200-1,49941.9 58.1 100% 1,500-1,999‡ ‡ 100% 2,000 or more‡ ‡ 100% ‡ Reporting standards not met. The names of the variables used in this table are: SCHSIZE, URBANS12, T0525, SCHLEVE2 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhn6a2 Q1.Teacher's main position at the school by Three-level private school typology, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Administrator(%) Other(%) Total Estimates Total79.2 14.8 2.8 3.1 100% Three-level private school typology Catholic82.5 12.9 1.5 3.1 100% Other religious73.2 20.1 3.4 3.3 ! 100% Nonsectarian83.8 9.7 3.4 ! 3.1 100% Four-category school level Primary78.7 16.5 1.7 3.1 100% Middle82.3 9.7 ! 5.0 ! 3.1 ! 100% High75.5 16.8 ! 4.5 3.2 100% Combined81.0 12.5 3.3 3.2 ! 100% Collapsed total K-12 and ungraded enrollment in school 1-4968.2 21.4 ! 5.8 4.6 ! 100% 50-9972.3 20.7 3.8 3.3 ! 100% 100-14975.5 17.2 5.0 ! 2.2 ! 100% 150-19970.2 25.0 1.0 ! 3.8 100% 200-34980.9 14.3 1.5 ! 3.2 ! 100% 350-49984.8 11.3 1.9 ! 2.0 ! 100% 500-74982.8 11.1 ! 2.1 !! 4.0 ! 100% 750-99991.5 4.9 ! 2.7 !! ‡ 100% 1,000-1,19983.1 7.7 !! ‡ ‡ 100% 1,200-1,49985.4 6.2 !! ‡ 3.9 !! 100% 1,500-1,999‡ ‡ ‡ ‡ 100% 2,000 or more‡ ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: SCHSIZE, SCHLEVE2, T0025 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhn733 Estimated number of full-time equivalent teachers in the school 1, Q70.Base salary for the entire 2011-12 school year 1 by Collapsed total K-12 and ungraded enrollment in school and Collapsed urban-centric school locale code. Estimated number of full-time equivalent teachers in the school(Avg>0) Q70.Base salary for the entire 2011-12 school year(Avg>0) Estimates Total36.9 37,152.0 Collapsed total K-12 and ungraded enrollment in school 1-496.8 25,408.0 50-9910.1 29,132.5 100-14914.9 29,057.3 150-19917.2 30,795.0 200-34926.6 37,122.8 350-49939.5 41,813.7 500-74955.5 42,851.6 750-99977.0 52,848.0 1,000-1,19989.7 45,348.0 1,200-1,499113.9 52,731.6 1,500-1,999‡ ‡ 2,000 or more‡ ‡ Collapsed urban-centric school locale code City47.1 41,135.8 Suburb31.4 36,302.9 Town16.3 28,420.3 Rural29.7 31,730.5 ‡ Reporting standards not met. The names of the variables used in this table are: NUMTCH, URBANS12, SCHSIZE and T0508. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhnd94 Q46a.Prof dev: reading instruction by Three-level private school typology and Four-category school level. No(%) Yes(%) Total Estimates Total71.4 28.6 100% Three-level private school typology Catholic67.0 33.0 100% Other religious73.3 26.7 100% Nonsectarian73.5 26.5 100% Four-category school level Primary63.3 36.7 100% Middle79.1 20.9 100% High87.1 12.9 100% Combined73.9 26.1 100% The names of the variables used in this table are: T0344, SCHLEVE2 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhn5a5 Highest degree earned by Program type of school and Four-category school level. Associate's degree or no college degree(%) Bachelor's degree(%) Master's degree(%) Education specialist or Certificate of Advanced Graduate Studies(%) Doctorate or Professional degree(%) Total Estimates Total8.4 48.5 35.8 5.0 2.3 100% Program type of school Regular8.5 49.4 35.4 4.3 2.3 100% Montessori10.4 42.4 28.6 17.9 ‡ 100% Special program emphasis6.5 !! 36.3 43.4 7.5 ! 6.3 !! 100% Special Education4.4 ! 44.8 42.2 8.5 ‡ 100% Career/Technical/Vocational Education‡ ‡ ‡ ‡ ‡ 100% Alternative13.2 !! 39.8 35.7 6.9 ! 4.3 !! 100% Early Childhood Program/Daycare Center‡ ‡ ‡ ‡ ‡ 100% Four-category school level Primary8.5 52.7 32.6 5.2 1.0 100% Middle5.3 !! 52.1 28.9 6.8 ! 7.0 ! 100% High5.2 40.6 46.0 3.7 4.5 100% Combined9.7 46.7 35.7 5.2 2.7 ! 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: PGMTYPE, SCHLEVE2 and HIDEGR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhn331 Gender by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level, Collapsed total K-12 and ungraded enrollment, and Percentage of enrolled students approved for the NSLP at school. Male(%) Female(%) Total Estimates Total23.7 76.3 100% Charter school identifier School is a public charter school25.1 74.9 100% School is not a public charter school23.6 76.4 100% Collapsed urban-centric school locale code Suburb23.0 77.0 100% Town24.2 75.8 100% Rural24.5 75.5 100% Four-category school level Primary10.7 89.3 100% Middle27.4 72.6 100% High41.7 58.3 100% Combined31.2 68.8 100% Collapsed total K-12 and ungraded enrollment in school less than 10032.1 67.9 100% 100-19926.2 73.8 100% 200-49917.6 82.4 100% 500-74918.1 81.9 100% 750-99922.7 77.3 100% 1000 or more36.8 63.2 100% Percentage of enrolled students approved for the NSLP at school Less than 35%25.1 74.9 100% 35% to 49%26.2 73.8 100% 50% to 74%22.0 78.0 100% More than 75%21.5 78.5 100% The names of the variables used in this table are: SCHSIZE, NSLAPP_S, SCHLEVE2, URBANS12, T0525 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2011-12Computation by NCES PowerStats on 1/16/2014. bgabe732 Teacher's main position at the school by Three-level private school typology, Three-category school level and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Itinerant teacher(%) Long-term substitute(%) Administrator(%) Library media specialist or Librarian(%) Other professional staff(%) Support staff(%) Total Estimates Total79.2 14.8 0.5 0.2 ! 2.8 0.4 ! 1.8 ‡ 100% Three-level private school typology Catholic82.5 12.9 0.8 ! ‡ 1.5 ‡ 1.6 ‡ 100% Other religious73.2 20.1 ‡ ‡ 3.4 ‡ 1.7 ! ‡ 100% Nonsectarian83.8 9.7 ‡ ‡ 3.4 ! 0.5 ! 1.9 ! ‡ 100% Three-category school level Elementary78.6 16.5 0.9 ‡ 1.8 ‡ 1.6 ‡ 100% Secondary76.4 16.3 ! ‡ ‡ 4.3 ‡ 2.2 ! 0.0 100% Combined81.0 12.4 ‡ ‡ 3.4 ‡ 1.7 ! ‡ 100% Collapsed total K-12 and ungraded enrollment in school Less than 10070.5 21.0 ‡ 0.5 ! 4.7 ‡ 1.6 ‡ 100% 100-19972.9 21.0 1.1 ! ‡ 3.1 ‡ 1.3 ! ‡ 100% 200-49982.4 13.2 0.3 ! ‡ 1.7 ‡ 2.0 ! ‡ 100% 500-74982.8 11.1 ! 0.0 0.0 ‡ ‡ ‡ 0.0 100% 750 or more88.4 5.1 ! 0.0 ‡ ‡ ‡ ‡ ‡ 100% ‡ Reporting standards not met. ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: SCHSIZE, T0025, SCHLEVEL and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2011-12 Computation by NCES PowerStats on 1/16/2014. bgabe843 Average Estimated number of full-time equivalent teachers in the school and average Base salary for the entire 2011-12 school year by Collapsed total K-12 and ungraded enrollment in school and Collapsed urban-centric school locale code. Estimated number of full-time equivalent teachers in the school(Avg>0) Base salary for the entire 2011-12 school year(Avg>0) Estimates Total51.7 50,673.5 Collapsed total K-12 and ungraded enrollment in school 1 to 199 students12.8 36,291.2 200 to 499 students29.1 49,452.7 500 to 999 students48.0 52,118.4 1,000 to 1,499 students78.7 54,503.4 1,500 to 1,999 students110.4 55,228.3 2,000 or more students148.2 57,605.0 Collapsed urban-centric school locale code City55.4 51,787.5 Suburb58.5 55,281.7 Town40.9 46,345.3 Rural43.7 45,438.3 The names of the variables used in this table are: SCHSIZE, URBANS12, NUMTCH and T0508. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 2011-12 Computation by NCES PowerStats on 1/16/2014. bgabe8c4 Professional Development-Reading Instruction by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level, Collapsed total K-12 and ungraded enrollment in school, and Percentage of enrolled students approved for the NSLP at school. No(%) Yes(%) Total Estimates Total43.3 56.7 100% Charter school identifier School is a public charter school44.5 55.5 100% School is not a public charter school43.3 56.7 100% Collapsed urban-centric school locale code Suburb43.9 56.1 100% Town43.7 56.3 100% Rural47.6 52.4 100% Four-category school level Primary29.3 70.7 100% Middle52.1 47.9 100% High59.6 40.4 100% Combined52.6 47.4 100% Collapsed total K-12 and ungraded enrollment in school less than 10050.5 49.5 100% 100-19949.8 50.2 100% 200-49936.0 64.0 100% 500-74939.1 60.9 100% 750-99944.1 55.9 100% 1000 more54.9 45.1 100% Percentage of enrolled students approved for the NSLP at school Less than 35%47.7 52.3 100% 35% to 49%45.6 54.4 100% 50% to 74%42.7 57.3 100% More than 75%34.8 65.2 100% The names of the variables used in this table are: SCHSIZE, T0344, SCHLEVE2, URBANS12, CHARFLAG and NSLAPP_S. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2011-12 bgabe655 Highest degree earned by Three-level private school typology and Three-category school level. No bachelor's(%) BA(%) MA(%) Higher than a master's degree(%) Total Estimates Total8.4 48.5 35.8 7.3 100% Three-level private school typology Catholic5.8 51.7 36.5 6.1 100% Other religious12.7 52.0 29.3 6.0 100% Nonsectarian5.5 40.4 43.6 10.5 100% Three-category school level Elementary8.4 52.8 32.7 6.1 100% Secondary5.4 40.9 45.0 8.7 100% Combined9.6 46.7 35.6 8.1 100% The names of the variables used in this table are: HIDEGR, SCHLEVEL and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2011-12 Computation by NCES PowerStats on 1/16/2014. bgabebe1 Principal's age 1, Principal's age 1 by Four-category school level (primary/middle/high/combined) and Percentage of enrolled students approved for the NSLP at school. Principal's age(Avg>0) Principal's age(Median>0) Estimates Total48.0 48.0 Four-category school level (primary/middle/high/combined) Primary48.1 48.0 Middle46.8 46.0 High48.5 48.0 Combined48.1 48.0 Percentage of enrolled students approved for the NSLP at school 0% to 25%47.9 47.0 26% to 50%48.0 48.0 51% to 75%48.1 48.0 More than 75%48.0 48.0 The names of the variables used in this table are: NSLAPP_S, AGE_P and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12.Computation by NCES PowerStats on 5/17/2017.bhebhmd2e2 Q55 Annual salary- before taxes and deductions 1 by Charter school identifier, Collapsed urban-centric school locale code and Three-category school level (elementary/secondary/combined). Q55 Annual salary- before taxes and deductions(Avg>0) Estimates Total90,509.6 Charter school identifier School is a public charter school80,068.2 School is not a public charter school91,054.9 Collapsed urban-centric school locale code City95,872.7 Suburb101,601.6 Town82,888.1 Rural80,218.9 Three-category school level (elementary/secondary/combined) Elementary89,572.3 Secondary96,310.9 Combined82,934.0 The names of the variables used in this table are: URBANS12, A0335, SCHLEVEL and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12.Computation by NCES PowerStats on 5/17/2017.bhebhab33 Q34 Total number of hours spent on all school activities every week 1, Q35 Total number of hours spent on student interaction every week 1 by Charter school identifier, Three-category school level (elementary/secondary/combined) and Collapsed total K-12 and ungraded enrollment in school. Q34 Total number of hours spent on all school activities every week(Avg>0) Q35 Total number of hours spent on student interaction every week(Avg>0) Estimates Total58.1 22.5 Charter school identifier School is a public charter school59.2 23.2 School is not a public charter school58.0 22.4 Three-category school level (elementary/secondary/combined) Elementary57.9 21.5 Secondary59.5 24.7 Combined55.9 24.0 Collapsed total K-12 and ungraded enrollment in school 1-4946.9 20.1 50-9953.3 21.4 100-14953.9 22.6 150-19956.3 24.2 200-34958.1 23.5 350-49958.4 22.1 500-74958.7 22.2 750-99959.8 21.7 1,000-1,19960.8 22.6 1,200-1,49960.5 23.3 1,500-1,99963.8 24.7 2,000 or more62.8 22.2 The names of the variables used in this table are: SCHSIZE, SCHLEVEL, A0241, A0240 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12.Computation by NCES PowerStats on 5/17/2017.bhebhafb4 Q1 Number of years served as a principal 0, Q2 Number of years served as principal at current school 0 by Charter school identifier, Collapsed urban-centric school locale code and Four-category school level (primary/middle/high/combined). Q1 Number of years served as a principal(Avg) Q2 Number of years served as principal at current school(Avg) Estimates Total7.2 4.2 Charter school identifier School is a public charter school5.9 3.3 School is not a public charter school7.2 4.2 Collapsed urban-centric school locale code City6.8 3.9 Suburb7.3 4.2 Town7.3 4.6 Rural7.3 4.3 Four-category school level (primary/middle/high/combined) Primary7.3 4.3 Middle6.5 4.0 High7.2 4.0 Combined7.0 4.2 The names of the variables used in this table are: A0026, A0025, URBANS12, SCHLEVE2 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12.Computation by NCES PowerStats on 5/17/2017.bhebhad905 Q16g Influence: school budget by Three-category school level (elementary/secondary/combined), Collapsed urban-centric school locale code and Collapsed total K-12 and ungraded enrollment in school. No Influence(%) Minor Influence(%) Moderate Influence(%) Major Influence(%) Not Applicable(%) Total Estimates Total1.2 8.3 26.2 63.8 0.5 100% Three-category school level (elementary/secondary/combined) Elementary1.0 7.0 25.0 66.5 0.4 100% Secondary1.3 9.0 27.6 61.0 1.0 100% Combined1.7 15.9 31.8 50.0 0.6 ! 100% Collapsed urban-centric school locale code City1.8 6.5 22.5 69.0 0.3 ! 100% Suburb0.7 ! 8.2 26.3 64.0 0.7 ! 100% Town0.8 ! 7.4 27.2 63.6 0.9 ! 100% Rural1.1 10.1 28.7 59.6 0.5 ! 100% Collapsed total K-12 and ungraded enrollment in school 1-492.2 !! 7.6 ! 22.7 65.3 2.2 ! 100% 50-994.6 ! 13.8 26.0 55.5 ‡ 100% 100-1491.2 !! 14.6 28.4 53.9 ‡ 100% 150-1992.3 ! 9.1 28.1 60.5 ‡ 100% 200-3491.2 9.1 25.0 64.5 ‡ 100% 350-4991.1 7.7 28.5 62.3 0.4 ! 100% 500-7490.6 ! 8.4 26.7 63.8 0.5 ! 100% 750-9991.3 ! 5.4 23.0 69.5 0.9 !! 100% 1,000-1,199‡ 5.4 26.3 67.1 ‡ 100% 1,200-1,499‡ 7.8 24.3 67.5 ‡ 100% 1,500-1,999‡ 4.5 23.4 71.3 0.7 ! 100% 2,000 or more‡ 6.4 27.8 65.3 ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: A0089, SCHSIZE, URBANS12 and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12.Computation by NCES PowerStats on 5/17/2017.bhebhaac1 Principal's age 1, Principal's age 1 by Four-category school level (primary/middle/high/combined) and Percentage of enrolled students approved for the NSLP at school. Principal's age(Avg>0) Principal's age(Median>0) Estimates Total51.7 53.0 Four-category school level (primary/middle/high/combined) Primary51.7 53.0 Middle‡ ‡ High53.7 56.0 Combined51.3 53.0 Percentage of enrolled students approved for the NSLP at school 0% to 25%53.1 55.0 26% to 50%53.3 56.0 51% to 75%50.2 52.0 More than 75%50.6 51.0 ‡ Reporting standards not met. The names of the variables used in this table are: NSLAPP_S, AGE_P and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhmc72 Q52 Annual salary- before taxes and deductions 1 by Three-level private school typology, Collapsed urban-centric school locale code and Three-category school level (elementary/secondary/combined). Q52 Annual salary- before taxes and deductions(Avg>0) Estimates Total65,281.7 Three-level private school typology Catholic63,752.3 Other religious52,725.8 Nonsectarian88,010.6 Collapsed urban-centric school locale code City72,452.9 Suburb68,042.6 Town45,094.7 Rural57,977.6 Three-category school level (elementary/secondary/combined) Elementary60,445.2 Secondary84,872.3 Combined66,914.0 The names of the variables used in this table are: URBANS12, A0335, SCHLEVEL and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhm7b3 Q34 Total number of hours spent on all school activities every week 1, Q35 Total number of hours spent on student interaction every week 1 by Three-level private school typology, Four-category school level (primary/middle/high/combined) and Collapsed total K-12 and ungraded enrollment in school. Q34 Total number of hours spent on all school activities every week(Avg>0) Q35 Total number of hours spent on student interaction every week(Avg>0) Estimates Total53.2 21.2 Three-level private school typology Catholic56.7 19.6 Other religious51.8 22.9 Nonsectarian52.3 19.6 Four-category school level (primary/middle/high/combined) Primary53.4 20.8 Middle‡ ‡ High56.1 22.3 Combined52.0 21.5 Collapsed total K-12 and ungraded enrollment in school 1-4948.4 25.0 50-9951.7 21.8 100-14954.9 20.8 150-19955.0 19.7 200-34958.3 18.3 350-49956.5 17.4 500-74957.7 15.1 750-99957.6 18.7 1,000-1,199‡ ‡ 1,200-1,499‡ ‡ 1,500-1,999‡ ‡ 2,000 or more‡ ‡ ‡ Reporting standards not met. The names of the variables used in this table are: SCHSIZE, SCHLEVE2, A0241, A0240 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhm124 Q1 Number of years served as a principal 0, Q2 Number of years served as principal at current school 0 by Collapsed urban-centric school locale code, Four-category school level (primary/middle/high/combined) and Collapsed total K-12 and ungraded enrollment in school. Q1 Number of years served as a principal(Avg) Q2 Number of years served as principal at current school(Avg) Estimates Total10.8 7.4 Collapsed urban-centric school locale code City11.0 7.6 Suburb11.8 8.2 Town10.2 6.4 Rural9.7 6.5 Four-category school level (primary/middle/high/combined) Primary10.5 6.9 Middle‡ ‡ High11.2 7.8 Combined11.3 8.1 Collapsed total K-12 and ungraded enrollment in school 1-499.3 7.2 50-9910.9 7.8 100-1499.7 5.8 150-19911.3 7.3 200-34911.9 7.3 350-49913.4 8.4 500-74913.7 9.2 750-99912.4 8.5 1,000-1,199‡ ‡ 1,200-1,499‡ ‡ 1,500-1,999‡ ‡ 2,000 or more‡ ‡ ‡ Reporting standards not met. The names of the variables used in this table are: A0026, A0025, URBANS12, SCHLEVE2 and SCHSIZE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhmf35 Q16g Influence: school budget by Three-category school level (elementary/secondary/combined), Collapsed urban-centric school locale code and Collapsed total K-12 and ungraded enrollment in school. No influence(%) Minor influence(%) Moderate influence(%) Major influence(%) Not applicable(%) Total Estimates Total2.9 8.1 24.8 62.1 2.1 100% Three-category school level (elementary/secondary/combined) Elementary2.5 ! 7.9 26.9 60.2 2.5 ! 100% Secondary‡ 7.6 19.9 70.2 ‡ 100% Combined4.3 ! 8.7 22.9 62.6 1.5 ! 100% Collapsed urban-centric school locale code City1.4 ! 7.8 24.8 64.2 1.8 ! 100% Suburb1.9 ! 9.0 22.9 65.1 1.1 ! 100% Town3.2 !! 3.0 !! 26.5 65.6 ‡ 100% Rural6.1 ! 9.5 26.6 53.9 3.8 !! 100% Collapsed total K-12 and ungraded enrollment in school 1-497.4 11.2 24.8 52.7 3.9 ! 100% 50-99‡ 6.6 26.8 63.1 3.0 !! 100% 100-149‡ 11.2 20.4 68.2 ‡ 100% 150-199‡ 7.5 ! 32.2 59.0 ‡ 100% 200-3491.4 !! 5.1 23.6 68.6 ‡ 100% 350-499‡ 3.0 ! 16.2 80.6 ‡ 100% 500-749‡ 5.8 ! 30.5 63.7 ‡ 100% 750-999‡ ‡ 25.5 ! 68.5 ‡ 100% 1,000-1,199‡ ‡ ‡ ‡ ‡ 100% 1,200-1,499‡ ‡ ‡ ‡ ‡ 100% 1,500-1,999‡ ‡ ‡ ‡ ‡ 100% 2,000 or more‡ ‡ ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: A0089, SCHSIZE, URBANS12 and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12.Computation by NCES PowerStats on 5/18/2017.bkebhm911 Average Principal's age, and median Principal's age by Charter school identifier, Four-category school level (primary/middle/high/combined), Collapsed total K-12 and ungraded enrollment in school, and Percentage of enrolled students approved for the NSLP at school. Principal's age(Avg>0) Principal's age(Median>0) Estimates Total48.0 48.0 Charter school identifier School is a public charter school46.3 45.0 School is not a public charter school48.1 48.0 Four-category school level (primary/middle/high/combined) Primary48.1 48.0 Middle46.8 46.0 High48.5 48.0 Combined48.1 48.0 Collapsed total K-12 and ungraded enrollment in school Less than 100 Students49.5 51.0 100 to 199 Students48.1 48.0 200 to 499 Students47.7 47.0 500 to 749 Students48.0 48.0 750 to 999 Students47.7 47.0 1,000 or more Students48.4 48.0 Percentage of enrolled students approved for the NSLP at school Less than 35%47.8 47.0 35% to 49%48.1 48.0 50% to 74%48.0 48.0 75% or more47.9 48.0 The names of the variables used in this table are: SCHSIZE, SCHLEVE2, AGE_P, CHARFLAG and NSLAPP_S. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES PowerStats on 1/22/2014. ccabed62 Average Annual salary- before taxes and deductions by Three-level private school typology, Collapsed urban-centric school locale code and Three-category school level (elementary/secondary/combined). Annual salary- before taxes and deductions(Avg>0) Estimates Total65,281.7 Three-level private school typology Catholic63,752.3 Other religious52,725.8 Nonsectarian88,010.6 Collapsed urban-centric school locale code City72,452.9 Suburb68,042.6 Town45,094.7 Rural57,977.6 Three-category school level (elementary/secondary/combined) Elementary60,445.2 Secondary84,872.3 Combined66,914.0 The names of the variables used in this table are: SCHLEVEL, URBANS12, RELIG and A0335. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12 Computation by NCES PowerStats on 1/22/2014. ccabe3e3 Average Total number of hours spent on all school activities every week and average Total number of hours spent on student interaction every week by Charter school identifier, Four-category school level (primary/middle/high/combined), and Collapsed total K-12 and ungraded enrollment in school. Q34 Total number of hours spent on all school activities every week(Avg>0) Q35 Total number of hours spent on student interaction every week(Avg>0) Estimates Total58.1 22.5 Charter school identifier School is a public charter school59.2 23.2 School is not a public charter school58.0 22.4 Four-category school level (primary/middle/high/combined) Primary57.7 21.1 Middle58.3 23.1 High59.8 25.3 Combined55.7 23.6 Collapsed total K-12 and ungraded enrollment in school Less than 100 Students50.4 20.8 100 to 199 Students55.1 23.4 200 to 499 Students58.2 22.7 500 to 749 Students58.7 22.2 750 to 999 Students59.8 21.7 1,000 or more Students61.8 23.2 The names of the variables used in this table are: SCHSIZE, SCHLEVE2, A0241, A0240 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES PowerStats on 1/22/2014. ccabed74 Average Number of years served as a principal, average Number of years served as principal at current school by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level (primary/middle/high/combined), and Percentage of enrolled students approved for the NSLP at school. Number of years served as a principal(Avg) Number of years served as principal at current school(Avg) Estimates Total7.2 4.2 Charter school identifier School is a public charter school5.9 3.3 School is not a public charter school7.2 4.2 Collapsed urban-centric school locale code City6.8 3.9 Suburb7.3 4.2 Town7.3 4.6 Rural7.3 4.3 Four-category school level (primary/middle/high/combined) Primary7.3 4.3 Middle6.5 4.0 High7.2 4.0 Combined7.0 4.2 Percentage of enrolled students approved for the NSLP at school Less than 35%7.6 4.4 35% to 49%7.3 4.2 50% to 74%7.2 4.3 75% or more6.5 3.7 The names of the variables used in this table are: NSLAPP_S, SCHLEVE2, URBANS12, A0026, A0025 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2011-12 Computation by NCES PowerStats on 1/22/2014. ccabe2f5 Influence: school budget by Three-category school level (elementary/secondary/combined), Three-level private school typology, Collapsed urban-centric school locale code, and Collapsed total K-12 and ungraded enrollment in school. No influence(%) Minor influence(%) Moderate influence(%) Major influence(%) Total Estimates Total3.0 8.3 25.3 63.4 100% Three-category school level (elementary/secondary/combined) Elementary2.5 ! 8.1 27.6 61.8 100% Secondary‡ 7.7 20.2 71.2 100% Combined4.4 ! 8.8 23.3 63.6 100% Three-level private school typology Catholic‡ 5.5 27.2 67.1 100% Other religious4.9 10.6 29.2 55.3 100% Nonsectarian2.2 ! 7.0 15.9 75.0 100% Collapsed urban-centric school locale code City1.5 ! 8.0 25.2 65.4 100% Suburb1.9 ! 9.1 23.2 65.8 100% Town‡ ‡ 27.0 66.8 100% Rural6.3 ! 9.9 27.6 56.1 100% Collapsed total K-12 and ungraded enrollment in school Less than 100 Students5.1 9.9 26.4 58.5 100% 100 to 199 Students‡ 9.5 25.8 64.1 100% 200 to 499 Students‡ 4.5 21.6 72.7 100% 500 to 749 Students0.0 5.8 ! 30.5 63.7 100% 750 to 999 Students0.0 ‡ 25.5 ! 68.5 100% 1,000 or more Students‡ 10.5 ! 28.6 ! 54.4 100% ‡ Reporting standards not met. ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: SCHSIZE, A0089, URBANS12, SCHLEVEL and RELIG. The variable names are unique identifiers. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2011-12 Computation by NCES PowerStats on 1/22/2014. ccabea61 High school completion status: 2006 by Sex and Student's race/ethnicity. Pre-fall 2003 graduate(%) Fall 2003 - Summer 2004 graduate(%) Post-academic year 2003-2004 graduate, GED recipient or high school equivalency recipient(%) Not completed(%) Total Estimates Total2.7 83.5 6.7 7.0 100% Sex Male2.9 80.5 8.1 8.4 100% Female2.6 86.4 5.4 5.7 100% Student's race/ethnicity White1.6 88.9 5.1 4.4 100% Asian or Pacific Islander2.4 88.9 4.7 4.0 100% Black or African American6.1 73.3 9.0 11.6 100% Hispanic3.9 72.7 10.6 12.8 100% More than one race, non-Hispanic3.4 79.2 8.7 8.8 100% The names of the variables used in this table are: F1SEX, F2HSSTAT and F1RACE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is F2F1WT.Source: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Sophomores.Computation by NCES PowerStats on 6/8/2015. kfbfg182 Percent distribution of highest level of education earned as of June 2013 by Sex. High school credential or less(%) Some college(%) Bachelor's degree or post-baccalaureate certificate(%) Master's degree or higher(%) Total Estimates Total11.8 50.7 29.9 7.5 100% Sex Male15.3 50.2 28.8 5.6 100% Female8.5 51.2 30.9 9.3 100% The names of the variables used in this table are: F3ATTAINMENT and F1SEX. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is F3F1PNLWT. Source: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Seniors. Computation by NCES PowerStats on 5/18/2015. cchbbx93 Average postsecondary GPA at all known institutions attended by highest level of education student expected. Transcript: GPA at all known institutions attended(Avg) Estimates Total2.6 Student's expected achievement in school: base year Less than high school graduation2.2 High school graduation or GED only2.3 Attend or complete 2-year college/school2.5 Attend college, 4-year degree incomplete2.3 Graduate from college2.6 Obtain Master's degree or equivalent2.8 Obtain PhD, MD, or other advanced degree2.8 The names of the variables used in this table are: F3TZGPAALL and BYSTEXP. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTN000. Source: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Sophomores. Computation by NCES PowerStats on 5/12/2015. cchbbx24 Percentage distribution of highest degree attained as of June 2013 by parent's highest level of education. Some college(%) Bachelor's degree(%) Master's degree or higher(%) Total Estimates Total27.7 57.1 15.2 100% Parent's highest level of education High school diploma or less47.5 44.4 8.0 100% Some college36.4 53.6 10.0 100% Bachelor's degree19.4 63.1 17.5 100% Master's degree or higher11.6 63.7 24.7 100% The names of the variables used in this table are: F1PARED, F3EVRATT and F3TZHIGHDEG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTN000. Source: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Sophomores. Computation by NCES PowerStats on 5/11/2015. bbebfp1a5 Respondent's income from employment by Employment status as of third follow-up interview and Highest level of education earned as of third follow-up interview. Centile [i] 10th25th50th75th90th Zero Estimates Total6,000.0 15,000.0 25,000.0 40,000.0 55,000.0 10.4 Employment status as of third follow-up interview Unemployed1,500.0 4,500.0 12,500.0 22,000.0 30,000.0 36.9 Out of the labor force2,000.0 7,000.0 17,000.0 28,000.0 40,000.0 54.2 Working 0-34 hours/week2,000.0 7,000.0 12,000.0 20,000.0 30,000.0 7.4 Working 35+ hours/week10,000.0 20,000.0 30,000.0 42,000.0 60,000.0 3.1 Highest level of education earned as of third follow-up interview Less than High school completion6,000.0 ! 10,000.0 20,000.0 30,000.0 40,000.0 17.9 High school diploma or equivalent4,000.0 13,000.0 23,000.0 35,000.0 50,000.0 18.0 Some postsecondary enrollment4,500.0 12,000.0 22,000.0 34,000.0 45,000.0 13.5 Undergraduate certificate5,000.0 13,000.0 22,000.0 34,000.0 49,000.0 8.5 Associates degree6,000.0 15,000.0 25,000.0 37,000.0 50,000.0 8.7 Bachelor's degree or higher10,000.0 19,000.0 32,000.0 45,000.0 60,000.0 6.3 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: F3EMPSTAT, F3ATTAINMENT and F3ERN2011. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTN000. Source: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Seniors. Computation by NCES PowerStats on 5/12/2015. bcebfee81 High school completion status: 2006 by Sex and Student's race/ethnicity. Pre-fall 2003 graduate(%) Fall 2003 - Summer 2004 graduate(%) Post-academic year 2003-2004 graduate, GED recipient or high school equivalency recipient(%) Not completed(%) Total Estimates Total2.7 83.5 6.7 7.0 100% Sex Male2.9 80.5 8.1 8.4 100% Female2.6 86.4 5.4 5.7 100% Student's race/ethnicity White1.6 88.9 5.1 4.4 100% Asian or Pacific Islander2.4 88.9 4.7 4.0 100% Black or African American6.1 73.3 9.0 11.6 100% Hispanic3.9 72.7 10.6 12.8 100% More than one race, non-Hispanic3.4 79.2 8.7 8.8 100% The names of the variables used in this table are: F1SEX, F2HSSTAT and F1RACE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is F2F1WT.Source: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Sophomores.Computation by NCES PowerStats on 6/8/2015. kfbfg182 Percent distribution of highest level of education earned as of June 2013 by Sex. High school credential or less(%) Some college(%) Bachelor's degree or post-baccalaureate certificate(%) Master's degree or higher(%) Total Estimates Total11.8 50.7 29.9 7.5 100% Sex Male15.3 50.2 28.8 5.6 100% Female8.5 51.2 30.9 9.3 100% The names of the variables used in this table are: F3ATTAINMENT and F1SEX. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is F3F1PNLWT. Source: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Seniors. Computation by NCES PowerStats on 5/18/2015. ExTable2_051815.xml3 Average postsecondary GPA at all known institutions attended by highest level of education student expected. Transcript: GPA at all known institutions attended(Avg) Estimates Total2.6 Student's expected achievement in school: base year Less than high school graduation2.2 High school graduation or GED only2.3 Attend or complete 2-year college/school2.5 Attend college, 4-year degree incomplete2.3 Graduate from college2.6 Obtain Master's degree or equivalent2.8 Obtain PhD, MD, or other advanced degree2.8 The names of the variables used in this table are: F3TZGPAALL and BYSTEXP. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTN000. Source: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Sophomores. Computation by NCES PowerStats on 5/12/2015. bbebfaa94 Percentage distribution of highest degree attained as of June 2013 by parent's highest level of education. Some college(%) Bachelor's degree(%) Master's degree or higher(%) Total Estimates Total27.7 57.1 15.2 100% Parent's highest level of education High school diploma or less47.5 44.4 8.0 100% Some college36.4 53.6 10.0 100% Bachelor's degree19.4 63.1 17.5 100% Master's degree or higher11.6 63.7 24.7 100% The names of the variables used in this table are: F1PARED, F3EVRATT and F3TZHIGHDEG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTN000. Source: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Sophomores. Computation by NCES PowerStats on 5/11/2015. bbebfp1a5 Respondent's income from employment by Employment status as of third follow-up interview and Highest level of education earned as of third follow-up interview. Centile [i] 10th25th50th75th90th Zero Estimates Total6,000.0 15,000.0 25,000.0 40,000.0 55,000.0 10.4 Employment status as of third follow-up interview Unemployed1,500.0 4,500.0 12,500.0 22,000.0 30,000.0 36.9 Out of the labor force2,000.0 7,000.0 17,000.0 28,000.0 40,000.0 54.2 Working 0-34 hours/week2,000.0 7,000.0 12,000.0 20,000.0 30,000.0 7.4 Working 35+ hours/week10,000.0 20,000.0 30,000.0 42,000.0 60,000.0 3.1 Highest level of education earned as of third follow-up interview Less than High school completion6,000.0 ! 10,000.0 20,000.0 30,000.0 40,000.0 17.9 High school diploma or equivalent4,000.0 13,000.0 23,000.0 35,000.0 50,000.0 18.0 Some postsecondary enrollment4,500.0 12,000.0 22,000.0 34,000.0 45,000.0 13.5 Undergraduate certificate5,000.0 13,000.0 22,000.0 34,000.0 49,000.0 8.5 Associates degree6,000.0 15,000.0 25,000.0 37,000.0 50,000.0 8.7 Bachelor's degree or higher10,000.0 19,000.0 32,000.0 45,000.0 60,000.0 6.3 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: F3EMPSTAT, F3ATTAINMENT and F3ERN2011. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTN000. Source: U.S. Department of Education, National Center for Education Statistics, Education Longitudinal Study of 2002 (ELS:2002), High School Seniors. Computation by NCES PowerStats on 5/12/2015. bcebfee81 Percent of schools with at least one violent incident recorded, and percent of schools with at least one serious violent incidents recorded Total number of violent incidents recorded(%>0) Total number of serious violent incidents recorded(%>0) Estimates Total 73.8 16.4 School grades offered - based on 07-08 CCD frame variables (School) Primary64.4 13.0 Middle90.5 18.9 High school90.9 27.6 Combined73.7 15.5 School size categories - based on 07-08 CCD frame variables (School) < 30062.8 10.4 300 - 49971.3 15.7 500 - 99976.4 15.9 1,000 +95.4 32.8 Urbanicity - Based on Urban-centric location of school City74.9 21.7 Suburb73.5 15.5 Town80.3 15.6 Rural70.2 13.2 Percent White enrollment (categorical) More than 95 percent69.6 12.6 Between 80 and 95 percent67.9 9.9 Between 50 and 80 percent75.9 18.6 50 percent or less78.2 21.1 The names of the variables used in this table are: VIOINC10, PERCWHT, SVINC10, FR_SIZE, FR_LVEL and FR_URBAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2009–10 School Survey on Crime and Safety (SSOCS), 2010.Computation by NCES PowerStats on 6/22/2015. cefbfh772 Average number of incidents recorded, average number of violent incidents recorded, and average number of serious violent incidents recorded by urbanicity and school level Total number of incidents recorded(Avg) Total number of violent incidents recorded(Avg) Total number of serious violent incidents recorded(Avg) Estimates Total22.7 14.3 0.6 Urbanicity - Based on Urban-centric location of school City29.8 18.4 0.8 Suburb24.5 15.6 0.7 Town21.1 13.8 0.5 Rural15.6 9.9 0.5 School grades offered - based on 07-08 CCD frame variables (School) Primary12.8 9.9 0.4 Middle35.9 24.6 0.9 High school48.2 21.6 1.1 Combined17.9 9.8 0.5 !! !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: FR_LVEL, VIOINC10, FR_URBAN, INCID10 and SVINC10. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2009–10 School Survey on Crime and Safety (SSOCS), 2010.Computation by NCES PowerStats on 6/25/2015. cffbfahd2d3 Percentage distribution of parent participation in parent-teacher conferences 0-25%(%) 26-50%(%) 51-75%(%) 76-100%(%) School does not offer(%) Total Estimates Total6.4 17.0 23.1 50.9 2.7 100% School grades offered - based on 07-08 CCD frame variables (School) Primary2.2 ! 9.8 19.0 68.3 0.7 !! 100% Middle8.3 23.1 30.9 33.0 4.7 100% High school19.0 34.0 25.9 13.1 8.0 100% Combined9.0 ! 24.8 29.9 32.3 4.0 ! 100% School size categories - based on 07-08 CCD frame variables (School) < 3006.6 14.6 22.0 54.2 2.7 ! 100% 300 - 4993.8 15.5 23.3 56.2 1.2 ! 100% 500 - 9996.0 16.6 22.6 52.5 2.3 100% 1,000 +14.0 27.4 26.3 23.4 8.9 100% Urbanicity - Based on Urban-centric location of school City6.6 19.9 20.5 51.4 1.6 100% Suburb4.0 11.8 24.4 56.3 3.5 100% Town7.6 15.2 22.4 50.6 4.2 100% Rural7.8 20.2 24.3 45.5 2.3 100% Percent White enrollment (categorical) More than 95 percent10.4 19.8 21.3 46.2 2.4 ! 100% Between 80 and 95 percent3.7 11.0 22.3 59.0 4.0 100% Between 50 and 80 percent4.0 15.0 27.4 50.7 2.8 100% 50 percent or less8.2 21.3 21.4 47.2 1.9 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: FR_SIZE, PERCWHT, C0198, FR_LVEL and FR_URBAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2009–10 School Survey on Crime and Safety (SSOCS), 2010. Computation by NCES PowerStats on 6/22/2015. cefbfh914 Percentage distribution of schools with a program for Counseling, social work, psychological, or therapeutic activity for students Yes(%) No(%) Total Estimates Total93.2 6.8 100% School grades offered - based on 07-08 CCD frame variables (School) Primary93.6 6.4 100% Middle95.7 4.3 100% High school90.1 9.9 100% Combined90.2 9.8 ! 100% School size categories - based on 07-08 CCD frame variables (School) < 30087.6 12.4 100% 300 - 49994.4 5.6 100% 500 - 99994.7 5.3 100% 1,000 +96.6 3.4 100% Urbanicity - Based on Urban-centric location of school City94.4 5.6 100% Suburb94.1 5.9 100% Town94.9 5.1 100% Rural90.5 9.5 100% Percent White enrollment (categorical) More than 95 percent91.0 9.0 100% Between 80 and 95 percent92.1 7.9 100% Between 50 and 80 percent92.5 7.5 100% 50 percent or less95.3 4.7 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: FR_SIZE, PERCWHT, C0178, FR_URBAN and FR_LVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2009–10 School Survey on Crime and Safety (SSOCS), 2010. Computation by NCES PowerStats on 6/22/2015. cefbfhb55 Percentage distribution of the extent to which schools are limited by inadequate funds Limits in major way(%) Limits in minor way(%) Does not limit(%) Total Estimates Total25.3 36.6 38.2 100% Percent White enrollment (categorical) More than 95 percent26.3 41.5 32.2 100% Between 80 and 95 percent20.5 35.7 43.8 100% Between 50 and 80 percent24.0 37.0 39.0 100% 50 percent or less29.0 35.0 36.0 100% Q30. Level of crime where school is located High level of crime37.9 27.2 34.9 100% Moderate level of crime33.4 36.9 29.7 100% Low level of crime21.8 37.3 40.9 100% The names of the variables used in this table are: PERCWHT, C0562 and C0294. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2009–10 School Survey on Crime and Safety (SSOCS), 2010. Computation by NCES PowerStats on 6/22/2015. cefbfh7d1 Attainment or level of last institution enrolled through June 2017 by Control and level of first institution (IPEDS sector) 2011-12. Attained bachelor's degree(%) Attained associate's degree(%) Attained certificate(%) No degree, enrolled at 4-year(%) No degree, enrolled at less-than-4-year(%) No degree, not enrolled(%) Total Estimates Total36.8 10.9 8.5 6.4 5.7 31.7 100% Control and level of first institution (IPEDS sector) 2011-12 Public 4-year59.4 5.8 2.4 9.3 2.8 20.3 100% Private nonprofit 4-year73.6 2.9 1.3 5.4 2.1 14.7 100% Private for profit 4-year14.1 16.1 5.0 11.4 3.3 50.2 100% Public 2-year12.7 18.1 8.4 4.8 9.8 46.3 100% Private nonprofit 2-year10.6 ! 21.0 ! 20.4 ! 1.9 !! 7.8 ! 38.4 100% Private for profit 2-year0.6 !! 16.0 44.4 1.4 6.8 30.7 100% Public less-than-2-year‡ 3.6 !! 55.9 ‡ 6.2 !! 32.5 100% Private nonprofit less-than-2-year‡ ‡ 68.1 ‡ 2.9 !! 28.8 !! 100% Private for profit less-than-2-year‡ 2.4 !! 56.2 1.1 !! 8.6 31.1 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: FSECTOR and PRLVL6Y. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).Computation by NCES PowerStats on 6/28/2019. ckfbmne12 Attainment or level of last institution enrolled through June 2017 by Undergraduate degree program 2011-12 and Attendance intensity through June 2017, for Control and level of first institution (IPEDS sector) 2011-12 (Public 2-year). Attained bachelor's degree(%) Attained associate's degree(%) Attained certificate(%) No degree, enrolled at 4-year(%) No degree, enrolled at less-than-4-year(%) No degree, not enrolled(%) Total Estimates Total12.7 18.1 8.4 4.8 9.8 46.3 100% Undergraduate degree program 2011-12 Not in a degree program or others22.2 10.4 ! 6.8 ! 5.4 !! 10.8 ! 44.4 100% Certificate1.7 ! 3.4 ! 39.7 1.5 ! 7.5 ! 46.1 100% Associate's degree13.1 19.4 6.3 5.0 9.9 46.3 100% Attendance intensity through June 2017 Always full-time17.8 14.2 6.9 2.6 1.0 57.5 100% Always part-time‡ 5.2 6.3 0.5 !! 10.1 78.0 100% Mixed15.4 22.7 9.3 6.5 11.6 34.5 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: UGDEG, ENINPT6Y, FSECTOR and PRLVL6Y. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).Computation by NCES PowerStats on 6/28/2019. ckfbmn663 Percentage of students who borrowed and, of those who borrowed, average amount borrowed, by control and level of first institution Total aid amount 2011-12(%>0) Total aid amount 2011-12(Avg>0) Estimates Total81.312,810.3Control and level of first institution (IPEDS sector) 2011-12 Public 4-year83.912,485.9 Private nonprofit 4-year91.227,073.4 Private for profit 4-year92.913,070.0 Public 2-year69.94,909.0 Private nonprofit 2-year94.718,060.0 Private for profit 2-year98.413,744.4 Public less-than-2-year68.26,413.0 Private nonprofit less-than-2-year82.97,341.4 ! Private for profit less-than-2-year96.611,816.7 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: FSECTOR and TOTAID. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).Computation by NCES PowerStats on 9/18/2019. cmgbfek9b4 Student's marital status in 2017 by Control and level of first institution (IPEDS sector) 2011-12, Gender. Single, never married(%) Married(%) Separated(%) Divorced(%) Widowed(%) Living with partner(%) Total Control and level of first institution (IPEDS sector) 2011-12 = TotalsEstimates Total74.2 16.8 1.1 2.2 0.2 5.4 100% Gender Male77.1 15.7 0.9 1.5 0.1 ! 4.8 100% Female71.9 17.7 1.4 2.8 0.3 5.9 100% Control and level of first institution (IPEDS sector) 2011-12 = Public 2-yearEstimates Total70.2 20.8 1.3 2.1 0.1 ! 5.5 100% Gender Male74.4 18.9 0.9 ! 1.4 0.1 !! 4.4 100% Female66.5 22.5 1.6 2.8 0.2 ! 6.4 100% Control and level of first institution (IPEDS sector) 2011-12 = 4-yearEstimates Total79.3 12.8 0.8 1.7 0.1 5.3 100% Gender Male81.7 11.9 0.6 1.1 # 4.7 100% Female77.4 13.5 1.0 2.1 0.2 5.8 100% Control and level of first institution (IPEDS sector) 2011-12 = All othersEstimates Total59.4 24.8 2.5 6.1 1.1 ! 6.1 100% Gender Male55.4 27.9 3.0 ! 5.0 ! 0.5 !! 8.2 100% Female61.4 23.4 2.3 6.6 1.3 ! 5.0 100% # Rounds to zero ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: GENDER, FSECTOR and SMAR17. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17). Computation by NCES PowerStats on 6/28/2019. ckfbmna35 Cumulative student loans: total amount borrowed through 2017 0 by Retention at first institution through June 2017. Cumulative student loans: total amount borrowed through 2017(Avg) Estimates Total18,226.2 Retention at first institution through June 2017 Attained bachelor's degree33,687.8 Attained associate's degree13,391.5 Attained certificate9,654.6 No degree, still enrolled11,438.6 No degree, not enrolled‡ No degree, transferred18,282.7 No degree, left without return6,945.9 ‡ Reporting standards not met. The names of the variables used in this table are: CUMULN17 and PROUTF6Y. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17).Computation by NCES PowerStats on 6/28/2019. ckfbmn1f1 Percentage distribution of student educational attainment expectations for fall 2009 ninth-graders in 2012, by student sex and race/ethnicity. High School or Less(%) Some College(%) College Graduation(%) Graduate / Professional Degree(%) Don't know(%) Total Estimates Total16.9 11.6 27.8 33.1 10.6 100% F1 Student's sex Male21.2 11.3 28.9 27.3 11.3 100% Female12.6 11.9 26.6 39.0 9.9 100% BY Student's race/ethnicity-composite White, non-Hispanic14.9 11.2 30.9 33.3 9.7 100% Black/African-American, non-Hispanic17.6 10.0 22.3 39.9 10.1 100% Hispanic22.3 14.0 23.4 28.3 12.1 100% Asian, non-Hispanic8.3 6.3 27.5 44.5 13.4 100% All other races, non - Hispanic17.4 12.4 29.0 29.5 11.7 100% The names of the variables used in this table are: X2STUEDEXPCT, X1RACE and X2SEX. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is W2W1STU.Source: U.S. Department of Education, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09).Computation by NCES PowerStats on 2/23/2016 ccbbgekc542 Percentage distribution of postsecondary education and work plans or status of fall 2009 ninth-graders in 2013, by student and family characteristics. Taking postsecondary classes, not working(%) Taking postsecondary classes, not working(%) Both taking classes and working(%) Neither taking classes nor working(%) Undecided(%) Total Estimates Total36.0 22.1 14.4 4.8 22.7 100% F1 Student's sex Female38.7 23.5 11.7 4.8 21.3 100% Male33.5 20.6 16.9 4.8 24.2 100% F1 Student's race/ethnicity-composite Asian, non-Hispanic33.3 35.3 3.8 2.7 ! 24.8 100% Black/African-American, non-Hispanic32.1 17.7 14.9 6.6 28.7 100% Hispanic36.6 15.9 15.7 5.9 26.0 100% White, non-Hispanic36.2 25.8 14.5 3.8 19.7 100% More than one race, non-Hispanic41.2 17.5 14.2 5.8 21.3 100% Other, non-Hispanic38.7 15.9 ! 9.8 ! 5.4 !! 30.2 100% F1 Quintile coding of X2SES composite Lowest Fifth32.8 10.8 18.7 7.0 30.7 100% Middle Three Fifths37.9 19.9 16.1 4.9 21.2 100% Fifth Quintile33.7 40.3 4.4 2.3 19.3 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: X2SESQ5, X2RACE, X2SEX and X3CLGANDWORK. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is W3W2STU.Source: U.S. Department of Education, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09).Computation by NCES PowerStats on 2/23/2016 bhbbgcbe3 Percentage of fall 2009 ninth-graders who graduated early or were dropouts in the spring term of 2012, by socioeconomic status and school sector. Percent graduated early Percent dropped out Estimates Total1.1 2.7 Socioeconomic status (2012) Lowest Fifth1.6 4.7 Middle Three Fifths1.1 2.7 Highest Fifth0.4 ! 0.6 ! School Sector (2009) Public1.2 2.9 Private0.2 !! 0.2 !! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: X1CONTROL, X2ENROLSTAT and X2SESQ5. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is W2STUDENT.Source: U.S. Department of Education, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09).Computation by NCES PowerStats on 2/23/2016 ccbbge044 Percentage distribution of high school credits earned in academic courses for fall 2009 ninth-graders, by student sex and race/ethnicity. 0(%) 1 to 8(%) 9 to 12(%) 13 to 16(%) 17 or more(%) Total Estimates Total0.610.36.214.268.7100% Student's sex Male0.812.47.817.062.1100% Female0.58.24.711.475.2100% Student's race/ethnicity-composite Amer. Indian/Alaska Native, non-Hispanic5.5 !!16.412.3 !16.149.7100% Asian, non-Hispanic0.1 !!5.14.4 !8.781.6100% Black/African-American, non-Hispanic1.0 !15.06.912.564.6100% Hispanic, no race specified1.3 !!25.411.016.246.1100% Hispanic, race specified0.9 !12.27.114.965.0100% More than one race, non-Hispanic0.5 !11.45.216.366.6100% Native Hawaiian/Pacific Islander, non-Hispanic#11.0 !!3.2 !!6.5 !!79.4100% White, non-Hispanic0.47.95.814.471.5100% # Rounds to zero ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: X2RACE, X2SEX and X3TCREDACAD. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is W3HSTRANS. Source: U.S. Department of Education, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09).Computation by NCES PowerStats on 2/16/2016.bhbbgcfda5 Percentage distribution of high school credits earned in academic courses for fall 2009 ninth-graders, by student sex and race/ethnicity. Credits earned in: STEM(Median) Estimates Total8.0 Parents' highest level of education Less than high school7.0 High school diploma or GED or alterntive HS credential7.5 Certificate/diploma from school providing occupational training8.0 Associate's degree8.0 Bachelor's degree8.0 Master's degree8.0 Ph.D/M.D/Law/other high lvl prof degree8.5 Mathematics quintile score First (lowest) quintile7.0 Second quintile7.5 Third (middle) quintile7.5 Fouth quintile8.0 Fifth (highest) quintile8.5 The names of the variables used in this table are: X2TXMQUINT, X3TCREDSTEM and X2PAREDU. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is W3W2STUTR. Source: U.S. Department of Education, National Center for Education Statistics, High School Longitudinal Study of 2009 (HSLS:09).Computation by NCES PowerStats on 2/16/2016. bhbbgcp671 Percent of schools with at least one violent incident recorded, and percent of schools with at least one serious violent incidents recorded Total number of violent incidents recorded(%>0) Total number of serious violent incidents recorded(%>0) Estimates Total77.717.2 School grades offered - based on 05-06 CCD frame variables (School) Primary67.311.0 Middle94.425.3 High school95.231.8 Combined83.517.4 School size categories - based on 05-06 CCD frame variables (School) < 30063.711.4 300 - 49977.311.8 500 - 99982.119.2 1,000 +96.537.2 Urbanicity - Based on Urban-centric location of school City82.323.9 Suburb78.215.9 Town82.215.5 Rural72.313.6 Percent minority student enrollment in school (recoded) - based on 05-06 CCD frame variables (School) Less than 5 percent71.613.1 5 to less than 20 percent73.515.7 20 to less than 50 percent79.716.6 50 percent or more82.921.7 The names of the variables used in this table are: SVINC06, FR_LVEL, FR_SIZE, FR_CATMN, VIOINC06 and FR_LOC4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006.Computation by NCES PowerStats on 2/11/2016. babbgkh4f2 Average number of incidents recorded, average number of violent incidents recorded, and average number of serious violent incidents recorded by urbanicity and school level Total number of incidents recorded(Avg)Total number of violent incidents recorded(Avg)Total number of serious violent incidents recorded(Avg) Estimates Total26.317.90.7 Urbanicity - Based on Urban-centric location of school City37.326.01.4 Suburb28.219.00.6 Town23.315.80.4 Rural16.610.90.3 School grades offered - based on 05-06 CCD frame variables (School) Primary14.511.60.4 ! Middle46.533.61.2 High school52.326.81.5 Combined20.8 12.4 0.5 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: FR_LOC4, VIOINC06, SVINC06, INCID06 and FR_LVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006.Computation by NCES PowerStats on 2/11/2016. babbgkb53 Percentage distribution of parent participation in parent-teacher conferences 0-25%(%)26-50%(%)51-75%(%)76-100%(%)School does not offer(%)Total Estimates Total6.714.523.952.62.3100% School grades offered - based on 05-06 CCD frame variables (School) Primary1.2 !7.320.071.00.5 !!100% Middle9.722.329.833.44.8100% High school19.228.229.615.87.2100% Combined16.323.927.831.01.1 !!100% School size categories - based on 05-06 CCD frame variables (School) < 3007.610.422.058.81.2 !!100% 300 - 4993.414.721.858.91.2 !100% 500 - 9995.714.425.252.32.3100% 1,000 +16.023.529.323.87.4100% Urbanicity - Based on Urban-centric location of school City7.115.625.849.12.4100% Suburb5.710.921.159.72.7100% Town6.6 !15.324.751.81.7 !100% Rural7.417.225.048.51.9100% Percent minority student enrollment in school (recoded) - based on 05-06 CCD frame variables (School) Less than 5 percent4.416.118.558.52.5 !100% 5 to less than 20 percent4.911.121.159.83.1100% 20 to less than 50 percent7.013.222.755.12.0100% 50 percent or more8.716.729.043.81.8100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: FR_SIZE, FR_CATMN, FR_LOC4, C0198 and FR_LVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006.Computation by NCES PowerStats on 2/11/2016. babbgkeb4 Percentage distribution of schools with a program for Counseling, social work, psychological, or therapeutic activity for students Yes(%) No(%) Total Estimates Total91.68.4100% School grades offered - based on 05-06 CCD frame variables (School) Primary92.08.0100% Middle95.54.5100% High school90.010.0100% Combined83.316.7100% School size categories - based on 05-06 CCD frame variables (School) < 30087.412.6100% 300 - 49993.56.5100% 500 - 99992.47.6100% 1,000 +93.76.3100% Urbanicity - Based on Urban-centric location of school City92.97.1100% Suburb93.56.5100% Town92.47.6100% Rural88.311.7100% Percent minority student enrollment in school (recoded) - based on 05-06 CCD frame variables (School) Less than 5 percent88.711.3100% 5 to less than 20 percent91.88.2100% 20 to less than 50 percent92.37.7100% 50 percent or more92.57.5100% The names of the variables used in this table are: FR_SIZE, FR_CATMN, C0178, FR_LVEL and FR_LOC4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006. Computation by NCES PowerStats on 2/11/2016. babbgkk695 Percentage distribution of the extent to which schools are limited by inadequate funds Limits in major way(%) Limits in minor way(%) Does not limit(%) Total Estimates Total17.333.449.3100% Percent minority student enrollment in school (recoded) - based on 05-06 CCD frame variables (School) Less than 5 percent13.234.352.6100% 5 to less than 20 percent15.933.350.9100% 20 to less than 50 percent14.733.352.0100% 50 percent or more22.232.445.5100% Q30. Level of crime where school is located High level of crime31.127.041.9100% Moderate level of crime23.832.943.2100% Low level of crime14.534.151.4100% The names of the variables used in this table are: FR_CATMN, C0294 and C0562. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006. Computation by NCES PowerStats on 2/11/2016. babbgk8a1 Percent of schools with at least one violent incident recorded, and percent of schools with at least one serious violent incidents recorded Total number of violent incidents recorded(%>0) Total number of serious violent incidents recorded(%>0) Estimates Total75.5 17.2 School grades offered - based on 05-06 CCD frame variables (School) Primary65.1 13.0 Middle94.3 22.0 High school94.0 28.9 Combined75.5 16.4 School size categories - based on 05-06 CCD frame variables (School) < 30060.6 12.3 300 - 49969.1 11.4 500 - 99983.4 19.8 1,000 +97.0 34.0 Urbanicity - Based on Urban-centric location of school City82.1 20.2 Suburb73.7 17.4 Town80.0 17.6 Rural69.5 14.4 Percent minority student enrollment in school (recoded) - based on 05-06 CCD frame variables (School) Less than 5 percent66.7 15.0 5 to less than 20 percent72.7 13.7 20 to less than 50 percent77.3 15.2 50 percent or more80.5 22.5 The names of the variables used in this table are: VIOINC08, FR_LVEL, FR_SIZE, SVINC08, FR_CATMN and FR_URBAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007–08 School Survey on Crime and Safety (SSOCS), 2008.Computation by NCES PowerStats on 2/9/2016. mbbgkgeb2 Average number of incidents recorded, average number of violent incidents recorded, and average number of serious violent incidents recorded by urbanicity and school level Total number of incidents recorded(Avg) Total number of violent incidents recorded(Avg) Total number of serious violent incidents recorded(Avg) Estimates Total24.6 16.1 0.7 Urbanicity - Based on Urban-centric location of school City34.8 23.3 1.3 Suburb25.2 15.9 0.6 Town20.4 13.5 0.6 Rural17.6 11.5 0.3 School grades offered - based on 05-06 CCD frame variables (School) Primary15.2 12.0 0.5 Middle39.3 26.2 1.2 High school48.9 23.3 1.2 Combined16.2 9.9 0.3 The names of the variables used in this table are: VIOINC08, SVINC08, FR_LVEL, FR_URBAN and INCID08. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007–08 School Survey on Crime and Safety (SSOCS), 2008.Computation by NCES PowerStats on 2/9/2016. mbbgkha513 Percentage distribution of parent participation in parent-teacher conferences 0-25%(%) 26-50%(%) 51-75%(%) 76-100%(%) School does not offer(%) Total Estimates Total7.1 16.1 22.9 51.0 3.0 100% School grades offered - based on 05-06 CCD frame variables (School) Primary2.9 9.4 19.3 67.3 1.1 ! 100% Middle9.4 23.6 29.1 34.3 3.7 100% High school19.1 31.0 26.1 14.0 9.8 100% Combined11.3 21.8 29.6 34.5 2.8 !! 100% School size categories - based on 05-06 CCD frame variables (School) < 3008.5 12.9 17.2 58.5 2.8 ! 100% 300 - 4994.7 13.4 22.3 58.5 1.1 ! 100% 500 - 9996.0 16.1 26.1 49.1 2.7 100% 1,000 +13.9 29.4 26.0 21.7 8.9 100% Urbanicity - Based on Urban-centric location of school City6.1 16.9 23.0 51.6 2.4 100% Suburb4.4 12.7 22.3 56.4 4.2 100% Town7.4 17.9 25.7 47.3 1.8 100% Rural10.2 17.7 22.1 47.1 2.9 100% Percent minority student enrollment in school (recoded) - based on 05-06 CCD frame variables (School) Less than 5 percent12.2 16.0 19.5 47.4 4.9 ! 100% 5 to less than 20 percent3.0 13.9 22.0 58.2 2.9 100% 20 to less than 50 percent5.5 15.1 20.6 55.3 3.5 100% 50 percent or more8.9 18.6 26.9 43.9 1.7 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: FR_SIZE, FR_CATMN, C0198, FR_LVEL and FR_URBAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007–08 School Survey on Crime and Safety (SSOCS), 2008.Computation by NCES PowerStats on 2/9/2016. mbbgkh914 Percentage distribution of schools with a program for Counseling, social work, psychological, or therapeutic activity for students Yes(%) No(%) Total Estimates Total92.5 7.5 100% School grades offered - based on 05-06 CCD frame variables (School) Primary91.8 8.2 100% Middle96.2 3.8 100% High school91.1 8.9 100% Combined91.7 8.3 ! 100% School size categories - based on 05-06 CCD frame variables (School) < 30083.7 16.3 100% 300 - 49994.6 5.4 100% 500 - 99995.6 4.4 100% 1,000 +94.9 5.1 100% Urbanicity - Based on Urban-centric location of school City94.8 5.2 100% Suburb94.1 5.9 100% Town92.2 7.8 100% Rural89.2 10.8 100% Percent minority student enrollment in school (recoded) - based on 05-06 CCD frame variables (School) Less than 5 percent88.7 11.3 100% 5 to less than 20 percent91.5 8.5 100% 20 to less than 50 percent93.8 6.2 100% 50 percent or more94.2 5.8 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: FR_SIZE, FR_CATMN, C0178, FR_URBAN and FR_LVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007–08 School Survey on Crime and Safety (SSOCS), 2008.Computation by NCES PowerStats on 2/9/2016. mbbgkkaa65 Percentage distribution of the extent to which schools are limited by inadequate funds Limits in major way(%) Limits in minor way(%) Does not limit(%) Total Estimates Total23.7 39.6 36.7 100% Percent minority student enrollment in school (recoded) - based on 05-06 CCD frame variables (School) Less than 5 percent25.1 41.9 33.0 100% 5 to less than 20 percent19.7 42.6 37.7 100% 20 to less than 50 percent21.2 39.8 39.0 100% 50 percent or more27.8 36.1 36.1 100% Q30. Level of crime where school is located High level of crime43.2 34.2 22.6 100% Moderate level of crime30.9 41.2 27.9 100% Low level of crime20.3 39.7 40.0 100% The names of the variables used in this table are: FR_CATMN, C0294 and C0562. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2007–08 School Survey on Crime and Safety (SSOCS), 2008.Computation by NCES PowerStats on 2/9/2016. mbbgkkb01 Type of School by School Typology, Level of Instruction, Size of School (K-12, UG), Census Region and Urban-Centric Community Type. Regular elementary or secondary(%) Montessori(%) Special program emphasis(%) Special education(%) Career/technical/vocational(%) Alternative/other(%) Early childhood program/child care center(%) Total Estimates Total69.6 7.9 2.2 6.0 # 2.6 11.6 100% School Typology Catholic, parochial99.1 0.2 ‡ ‡ ‡ ‡ 0.5 100% Catholic, diocesan97.5 0.2 0.3 1.1 ‡ ‡ 0.8 100% Catholic, private84.1 3.4 1.6 3.7 ‡ 1.1 6.0 100% Other religious, conservative Christian92.5 0.3 1.1 0.6 ‡ 2.0 3.6 100% Other relig., affiliated w/ established denomination87.7 0.7 2.2 1.4 ‡ 0.9 7.1 100% Other relig., not affiliated w/ any denomination82.3 1.7 1.1 1.5 ‡ 1.7 11.6 100% Nonsectarian, regular school52.1 ‡ ‡ ‡ ‡ ‡ 47.9 100% Nonsectarian, special program‡ 68.4 14.1 ‡ 0.1 17.3 ‡ 100% Nonsectarian, special education‡ ‡ ‡ 100.0 ‡ ‡ ‡ 100% Level of Instruction Elementary65.6 11.7 1.9 1.5 ‡ 1.3 18.1 100% Secondary75.3 0.4 ! 3.1 11.3 ‡ 9.8 ‡ 100% Combined elementary and secondary77.1 1.5 2.7 14.9 ‡ 3.5 0.3 100% Size of School (K-12, UG) Less than 50 students46.4 13.9 1.9 7.7 ‡ 4.1 26.0 100% 50-149 students78.2 6.2 3.2 8.7 ‡ 2.8 0.9 100% 150-299 students93.3 1.5 2.0 2.4 ‡ 0.6 0.1 100% 300-499 students96.1 0.3 2.3 0.8 ‡ 0.4 ‡ 100% 500-749 students97.9 ‡ 1.2 ‡ ‡ 0.5 ‡ 100% 750 students or more98.7 ‡ 0.7 ‡ ‡ ‡ ‡ 100% Census Region Northeast62.6 6.1 2.2 10.1 ‡ 2.3 16.8 100% Midwest81.3 6.5 1.1 3.0 ‡ 1.3 6.7 100% South70.9 8.1 2.2 5.7 ‡ 3.1 10.0 100% West61.2 11.6 3.5 5.5 ‡ 4.1 14.1 100% Urban-Centric Community Type City (ulocale=11, 12, 13)68.6 8.5 2.9 6.7 # 2.4 10.8 100% Suburb (ulocale=21, 22, 23)59.1 10.9 2.0 7.6 ‡ 2.4 18.0 100% Town (ulocale=31, 32, 33)83.9 5.0 1.3 2.2 ‡ 1.9 5.7 100% Rural (ulocale=41, 42, 43)81.5 3.6 1.9 4.2 ‡ 3.6 5.2 100% Counts Total21486 2439 676 1859 6 813 3583 30861 School Typology Catholic, parochial‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Catholic, diocesan‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Catholic, private‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Other religious, conservative Christian‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Other relig., affiliated w/ established denomination‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Other relig., not affiliated w/ any denomination‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Nonsectarian, regular school‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Nonsectarian, special program‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Nonsectarian, special education‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Level of Instruction Elementary‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Secondary‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Combined elementary and secondary‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Size of School (K-12, UG) Less than 50 students‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ 50-149 students‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ 150-299 students‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ 300-499 students‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ 500-749 students‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ 750 students or more‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Census Region Northeast‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Midwest‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ South‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ West‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Urban-Centric Community Type City (ulocale=11, 12, 13)6865 846 285 675 5 244 1085 10005 Suburb (ulocale=21, 22, 23)‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Town (ulocale=31, 32, 33)‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ Rural (ulocale=41, 42, 43)‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ # Rounds to zero ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: TYPOLOGY, P415, SIZE, UCOMMTYP, LEVEL and REGION. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Private School Universe Survey (PSS), 2011–12. Computation by NCES PowerStats on 4/7/2017. kpbgf902 Total Number of Students in School (K-12, UG) 0 by Level of Instruction, School Typology, Type of School, Size of School (K-12, UG), Census Region and Urban-Centric Community Type. Total Number of Students in School (K-12, UG)(Avg) Level of Instruction = TotalsEstimates Total145.6 School Typology Catholic, parochial250.4 Catholic, diocesan284.4 Catholic, private354.4 Other religious, conservative Christian137.8 Other relig., affiliated w/ established denomination159.1 Other relig., not affiliated w/ any denomination85.0 Nonsectarian, regular school125.3 Nonsectarian, special program53.0 Nonsectarian, special education64.8 Type of School Regular elementary or secondary191.0 Montessori36.4 Special program emphasis124.8 Special education63.1 Career/technical/vocational‡ Alternative/other54.6 Early childhood program/child care center15.4 Size of School (K-12, UG) Less than 50 students19.9 50-149 students91.4 150-299 students212.3 300-499 students384.6 500-749 students603.1 750 students or more1,080.8 Census Region Northeast144.8 Midwest138.1 South159.7 West135.5 Urban-Centric Community Type City (ulocale=11, 12, 13)190.0 Suburb (ulocale=21, 22, 23)153.3 Town (ulocale=31, 32, 33)103.7 Rural (ulocale=41, 42, 43)88.1 Level of Instruction = ElementaryEstimates Total107.8 School Typology Catholic, parochial237.8 Catholic, diocesan229.5 Catholic, private146.9 Other religious, conservative Christian91.2 Other relig., affiliated w/ established denomination106.7 Other relig., not affiliated w/ any denomination54.9 Nonsectarian, regular school51.1 Nonsectarian, special program41.9 Nonsectarian, special education47.7 Type of School Regular elementary or secondary149.6 Montessori34.0 Special program emphasis88.9 Special education46.7 Career/technical/vocational‡ Alternative/other44.4 Early childhood program/child care center15.3 Size of School (K-12, UG) Less than 50 students18.6 50-149 students93.3 150-299 students210.6 300-499 students383.6 500-749 students589.0 750 students or more942.4 Census Region Northeast101.4 Midwest113.6 South109.1 West106.1 Urban-Centric Community Type City (ulocale=11, 12, 13)135.5 Suburb (ulocale=21, 22, 23)117.1 Town (ulocale=31, 32, 33)91.2 Rural (ulocale=41, 42, 43)51.0 Level of Instruction = SecondaryEstimates Total283.0 School Typology Catholic, parochial439.5 Catholic, diocesan534.1 Catholic, private568.9 Other religious, conservative Christian129.4 Other relig., affiliated w/ established denomination164.5 Other relig., not affiliated w/ any denomination126.8 Nonsectarian, regular school185.4 Nonsectarian, special program60.9 Nonsectarian, special education42.9 Type of School Regular elementary or secondary357.5 Montessori‡ Special program emphasis140.5 Special education42.8 Career/technical/vocational‡ Alternative/other43.8 Early childhood program/child care center‡ Size of School (K-12, UG) Less than 50 students21.4 50-149 students88.9 150-299 students218.0 300-499 students387.0 500-749 students607.4 750 students or more1,049.6 Census Region Northeast285.1 Midwest350.8 South256.8 West236.2 Urban-Centric Community Type City (ulocale=11, 12, 13)354.9 Suburb (ulocale=21, 22, 23)304.3 Town (ulocale=31, 32, 33)156.8 Rural (ulocale=41, 42, 43)142.9 Level of Instruction = Combined elementary and secondaryEstimates Total190.1 School Typology Catholic, parochial348.4 Catholic, diocesan325.1 Catholic, private338.4 Other religious, conservative Christian164.5 Other relig., affiliated w/ established denomination272.3 Other relig., not affiliated w/ any denomination150.5 Nonsectarian, regular school361.1 Nonsectarian, special program113.9 Nonsectarian, special education73.9 Type of School Regular elementary or secondary221.5 Montessori77.4 Special program emphasis176.4 Special education71.7 Career/technical/vocational‡ Alternative/other72.7 Early childhood program/child care center23.7 Size of School (K-12, UG) Less than 50 students24.0 50-149 students88.6 150-299 students214.8 300-499 students385.1 500-749 students610.8 750 students or more1,120.4 Census Region Northeast196.8 Midwest143.0 South212.4 West180.7 Urban-Centric Community Type City (ulocale=11, 12, 13)266.4 Suburb (ulocale=21, 22, 23)207.5 Town (ulocale=31, 32, 33)116.4 Rural (ulocale=41, 42, 43)132.6 # Rounds to zero ‡ Reporting standards not met. The names of the variables used in this table are: NUMSTUDS, TYPOLOGY, P415, SIZE, UCOMMTYP, LEVEL and REGION. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Private School Universe Survey (PSS), 2011–12. Computation by NCES PowerStats on 4/7/2017. kpbgf913 Size of School (K-12, UG) by School Typology, Level of Instruction, Type of School, Census Region and Urban-Centric Community Type. Less than 50 students(%) 50-149 students(%) 150-299 students(%) 300-499 students(%) 500-749 students(%) 750 students or more(%) Total Estimates Total43.6 24.8 17.8 7.9 3.6 2.3 100% School Typology Catholic, parochial3.6 23.6 45.0 20.2 6.1 1.5 100% Catholic, diocesan3.6 23.4 40.0 19.1 9.7 4.1 100% Catholic, private18.2 18.3 20.0 16.9 13.9 12.8 100% Other religious, conservative Christian38.3 33.0 17.4 6.3 2.9 2.0 100% Other relig., affiliated w/ established denomination35.1 31.8 19.4 7.4 3.5 2.9 100% Other relig., not affiliated w/ any denomination63.1 22.3 9.0 3.2 1.3 1.0 100% Nonsectarian, regular school60.5 15.9 10.7 6.7 3.2 3.1 100% Nonsectarian, special program68.8 23.8 5.3 1.5 0.5 0.2 100% Nonsectarian, special education54.1 37.0 7.5 1.2 ‡ ‡ 100% Level of Instruction Elementary49.9 23.3 18.0 6.8 1.7 0.3 100% Secondary28.5 19.9 16.2 14.2 12.0 9.2 100% Combined elementary and secondary33.8 29.9 17.8 8.6 5.2 4.7 100% Type of School Regular elementary or secondary29.1 27.9 23.8 10.9 5.0 3.2 100% Montessori76.7 19.5 3.4 0.3 ‡ ‡ 100% Special program emphasis37.2 35.7 16.2 8.2 2.0 0.7 100% Special education55.5 36.1 7.1 1.1 ‡ ‡ 100% Career/technical/vocational‡ ‡ ‡ ‡ ‡ ‡ 100% Alternative/other67.8 26.0 3.9 1.3 0.7 ‡ 100% Early childhood program/child care center97.8 1.9 0.2 ‡ ‡ ‡ 100% Census Region Northeast46.6 21.7 18.7 7.8 3.0 2.3 100% Midwest42.0 27.9 16.9 7.8 3.8 1.6 100% South40.9 25.8 17.4 8.6 4.2 3.0 100% West46.2 23.2 18.4 7.3 3.0 1.9 100% Urban-Centric Community Type City (ulocale=11, 12, 13)34.0 24.2 22.6 10.4 5.1 3.7 100% Suburb (ulocale=21, 22, 23)43.4 22.1 18.7 9.3 4.2 2.3 100% Town (ulocale=31, 32, 33)39.5 38.3 16.0 4.7 1.2 0.3 100% Rural (ulocale=41, 42, 43)59.3 24.5 10.3 3.6 1.4 0.9 100% Counts Total13459 7667 5488 2447 1103 698 30861 School Typology Catholic, parochial104 686 1310 589 178 44 2910 Catholic, diocesan106 684 1170 559 283 120 2922 Catholic, private189 190 209 176 145 133 1041 Other religious, conservative Christian1751 1511 796 290 135 92 4574 Other relig., affiliated w/ established denomination1074 973 593 227 106 88 3060 Other relig., not affiliated w/ any denomination4152 1470 594 214 85 65 6579 Nonsectarian, regular school2953 776 523 326 155 150 4882 Nonsectarian, special program2256 781 174 48 16 6 3280 Nonsectarian, special education‡ ‡ ‡ ‡ ‡ ‡ ‡ Level of Instruction Elementary9829 4592 3544 1333 340 58 19697 Secondary763 533 434 381 320 245 2677 Combined elementary and secondary2866 2542 1510 732 443 395 8488 Type of School Regular elementary or secondary6249 5995 5123 2350 1080 689 21486 Montessori‡ ‡ ‡ ‡ ‡ ‡ ‡ Special program emphasis251 242 110 55 14 5 676 Special education‡ ‡ ‡ ‡ ‡ ‡ ‡ Career/technical/vocational‡ ‡ ‡ ‡ ‡ ‡ ‡ Alternative/other‡ ‡ ‡ ‡ ‡ ‡ ‡ Early childhood program/child care center‡ ‡ ‡ ‡ ‡ ‡ ‡ Census Region Northeast3468 1615 1389 578 227 171 7447 Midwest3341 2223 1345 619 304 131 7963 South3761 2378 1602 795 387 280 9203 West2888 1452 1152 455 185 117 6249 Urban-Centric Community Type City (ulocale=11, 12, 13)3399 2419 2260 1041 511 375 10005 Suburb (ulocale=21, 22, 23)4736 2414 2037 1015 458 251 10911 Town (ulocale=31, 32, 33)1147 1110 464 138 34 7 2900 Rural (ulocale=41, 42, 43)4177 1724 727 253 100 65 7045 # Rounds to zero ‡ Reporting standards not met. The names of the variables used in this table are: TYPOLOGY, P415, SIZE, UCOMMTYP, LEVEL and REGION. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Private School Universe Survey (PSS), 2011–12.Computation by NCES PowerStats on 4/7/2017. kpbgf924 Coeducational Indicator by School Typology, Level of Instruction, Type of School, Size of School (K-12, UG), Census Region and Urban-Centric Community Type. Yes(%) No, it is an all-female school(%) No, it is an all-male school(%) Total Estimates Total95.8 1.8 2.3 100% School Typology Catholic, parochial98.8 0.9 0.3 100% Catholic, diocesan97.7 1.2 1.1 100% Catholic, private68.0 17.0 15.0 100% Other religious, conservative Christian99.4 0.3 0.4 100% Other relig., affiliated w/ established denomination92.4 3.4 4.2 100% Other relig., not affiliated w/ any denomination96.2 1.3 2.5 100% Nonsectarian, regular school97.0 1.6 1.4 100% Nonsectarian, special program97.0 0.7 2.3 100% Nonsectarian, special education94.2 1.5 4.3 100% Level of Instruction Elementary98.9 0.4 0.6 100% Secondary72.5 12.4 15.1 100% Combined elementary and secondary96.1 1.7 2.2 100% Type of School Regular elementary or secondary95.3 2.3 2.4 100% Montessori99.8 ‡ ‡ 100% Special program emphasis95.7 1.6 2.8 100% Special education93.6 1.6 4.8 100% Career/technical/vocational‡ ‡ ‡ 100% Alternative/other85.7 3.4 10.8 100% Early childhood program/child care center99.8 0.2 ‡ 100% Size of School (K-12, UG) Less than 50 students97.6 0.8 1.6 100% 50-149 students96.7 1.1 2.2 100% 150-299 students96.0 2.1 2.0 100% 300-499 students92.2 4.5 3.3 100% 500-749 students85.2 10.0 4.8 100% 750 students or more80.9 5.4 13.7 100% Census Region Northeast91.2 3.7 5.1 100% Midwest97.9 1.0 1.1 100% South97.0 1.3 1.7 100% West97.0 1.5 1.5 100% Urban-Centric Community Type City (ulocale=11, 12, 13)93.6 3.2 3.2 100% Suburb (ulocale=21, 22, 23)96.2 1.7 2.1 100% Town (ulocale=31, 32, 33)99.1 0.3 0.6 100% Rural (ulocale=41, 42, 43)97.1 0.7 2.2 100% Counts Total29578 562 721 30861 School Typology Catholic, parochial2875 26 9 2910 Catholic, diocesan2854 34 34 2922 Catholic, private708 177 156 1041 Other religious, conservative Christian4546 12 16 4574 Other relig., affiliated w/ established denomination2829 103 129 3060 Other relig., not affiliated w/ any denomination6329 86 165 6579 Nonsectarian, regular school4737 77 67 4882 Nonsectarian, special program3182 23 75 3280 Nonsectarian, special education1519 24 70 1613 Level of Instruction Elementary19485 85 127 19697 Secondary1940 333 403 2677 Combined elementary and secondary8154 143 191 8488 Type of School Regular elementary or secondary20479 485 522 21486 Montessori‡ ‡ ‡ ‡ Special program emphasis647 11 19 676 Special education1740 29 89 1859 Career/technical/vocational‡ ‡ ‡ ‡ Alternative/other697 28 88 813 Early childhood program/child care center‡ ‡ ‡ ‡ Size of School (K-12, UG) Less than 50 students13136 109 214 13459 50-149 students7417 81 169 7667 150-299 students5266 114 108 5488 300-499 students2255 110 81 2447 500-749 students940 110 53 1103 750 students or more564 38 96 698 Census Region Northeast6790 273 383 7447 Midwest7797 76 89 7963 South8929 119 154 9203 West6062 93 94 6249 Urban-Centric Community Type City (ulocale=11, 12, 13)9366 318 321 10005 Suburb (ulocale=21, 22, 23)10497 184 230 10911 Town (ulocale=31, 32, 33)2873 10 17 2900 Rural (ulocale=41, 42, 43)6842 51 152 7045 # Rounds to zero ‡ Reporting standards not met. The names of the variables used in this table are: TYPOLOGY, P415, SIZE, UCOMMTYP, LEVEL, REGION and P335. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Private School Universe Survey (PSS), 2011–12.Computation by NCES PowerStats on 4/7/2016. kpbgf935 Student-Teacher Ratio 1 by Level of Instruction, School Typology, Type of School, Size of School (K-12, UG), Census Region and Urban-Centric Community Type. Student-Teacher Ratio(Avg>0) Level of Instruction = TotalsEstimates Total10.5 School Typology Catholic, parochial14.9 Catholic, diocesan14.2 Catholic, private11.2 Other religious, conservative Christian9.4 Other relig., affiliated w/ established denomination10.6 Other relig., not affiliated w/ any denomination11.5 Nonsectarian, regular school9.1 Nonsectarian, special program7.0 Nonsectarian, special education6.3 Type of School Regular elementary or secondary11.7 Montessori6.3 Special program emphasis8.7 Special education6.3 Career/technical/vocational‡ Alternative/other8.9 Early childhood program/child care center9.2 Size of School (K-12, UG) Less than 50 students8.1 50-149 students10.3 150-299 students13.3 300-499 students14.3 500-749 students15.6 750 students or more16.2 Census Region Northeast10.9 Midwest11.1 South9.8 West10.7 Urban-Centric Community Type City (ulocale=11, 12, 13)10.7 Suburb (ulocale=21, 22, 23)10.4 Town (ulocale=31, 32, 33)9.7 Rural (ulocale=41, 42, 43)10.9 Level of Instruction = ElementaryEstimates Total11.1 School Typology Catholic, parochial15.1 Catholic, diocesan14.4 Catholic, private10.3 Other religious, conservative Christian9.3 Other relig., affiliated w/ established denomination10.9 Other relig., not affiliated w/ any denomination12.3 Nonsectarian, regular school9.0 Nonsectarian, special program6.6 Nonsectarian, special education6.0 Type of School Regular elementary or secondary12.7 Montessori6.2 Special program emphasis8.0 Special education6.0 Career/technical/vocational‡ Alternative/other9.2 Early childhood program/child care center9.2 Size of School (K-12, UG) Less than 50 students8.7 50-149 students11.1 150-299 students14.6 300-499 students16.1 500-749 students17.6 750 students or more45.7 Census Region Northeast11.9 Midwest11.7 South9.7 West10.9 Urban-Centric Community Type City (ulocale=11, 12, 13)10.9 Suburb (ulocale=21, 22, 23)10.8 Town (ulocale=31, 32, 33)10.4 Rural (ulocale=41, 42, 43)12.2 Level of Instruction = SecondaryEstimates Total10.4 School Typology Catholic, parochial13.2 Catholic, diocesan13.9 Catholic, private12.4 Other religious, conservative Christian8.5 Other relig., affiliated w/ established denomination10.3 Other relig., not affiliated w/ any denomination10.2 Nonsectarian, regular school8.3 Nonsectarian, special program7.8 Nonsectarian, special education5.8 Type of School Regular elementary or secondary11.4 Montessori‡ Special program emphasis7.2 Special education5.7 Career/technical/vocational‡ Alternative/other9.1 Early childhood program/child care center‡ Size of School (K-12, UG) Less than 50 students6.0 50-149 students9.4 150-299 students11.9 300-499 students12.4 500-749 students14.2 750 students or more15.1 Census Region Northeast9.4 Midwest11.7 South10.7 West10.0 Urban-Centric Community Type City (ulocale=11, 12, 13)11.4 Suburb (ulocale=21, 22, 23)10.2 Town (ulocale=31, 32, 33)9.6 Rural (ulocale=41, 42, 43)8.7 Level of Instruction = Combined elementary and secondaryEstimates Total9.3 School Typology Catholic, parochial11.3 Catholic, diocesan10.6 Catholic, private10.6 Other religious, conservative Christian9.6 Other relig., affiliated w/ established denomination10.1 Other relig., not affiliated w/ any denomination9.8 Nonsectarian, regular school9.6 Nonsectarian, special program9.3 Nonsectarian, special education6.5 Type of School Regular elementary or secondary9.9 Montessori9.5 Special program emphasis10.4 Special education6.5 Career/technical/vocational‡ Alternative/other8.4 Early childhood program/child care center10.8 Size of School (K-12, UG) Less than 50 students6.6 50-149 students9.2 150-299 students10.8 300-499 students12.2 500-749 students15.2 750 students or more12.6 Census Region Northeast8.8 Midwest8.6 South9.6 West10.2 Urban-Centric Community Type City (ulocale=11, 12, 13)9.7 Suburb (ulocale=21, 22, 23)9.4 Town (ulocale=31, 32, 33)8.4 Rural (ulocale=41, 42, 43)9.3 # Rounds to zero ‡ Reporting standards not met. The names of the variables used in this table are: TYPOLOGY, P415, STTCH_RT, SIZE, UCOMMTYP, LEVEL and REGION. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Private School Universe Survey (PSS), 2011–12.Computation by NCES PowerStats on 4/7/2017. kpbgf941 Attendance intensity at all schools by NPSAS institution sector. Exclusively full-time(%) Exclusively part-time(%) Mixed full-time and part-time(%) Estimates Total50.5 31.7 17.9 NPSAS institution sector - 10 categories Public less-than-2-year71.0 16.6 12.4 Public 2-year32.5 50.0 17.5 Public 4-year non-doctorate-granting48.4 30.4 21.2 Public 4-year doctorate-granting61.8 15.4 22.8 Private nonprofit lt 4-year86.7 7.3 ! 6.0 Private nonprofit 4-year nondoctorate70.3 16.2 13.5 Private nonprofit 4-year doctorate-granting70.9 15.2 13.9 Private for profit less-than-2-year75.3 14.1 10.6 Private for profit 2-year76.2 10.1 13.7 Private for profit 4-year66.2 20.6 13.3 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: SECTOR10 and ATTNPTRN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES PowerStats on 5/24/2013. cgcbd1e2 Federal Pell grant with (percent > 0) by Total income by dependency. Federal Pell grant(%>0) Estimates Total41.3 Total income by dependency (categorical) Dependent: Less than $10,00081.8 Dependent: $10,000-$19,99982.1 Dependent: $20,000-$29,99975.5 Dependent: $30,000-$39,99965.6 Dependent: $40,000-$49,99963.1 Dependent: $50,000-$59,99944.2 Dependent: $60,000-$69,99918.5 Dependent: $70,000-$79,9998.2 Dependent: $80,000-$99,9992.4 Dependent: $100,000-$119,9990.8 Dependent: $120,000-$149,9990.4 ! Dependent: $150,000 or more0.2 ! Independent: Less than $5,00065.0 Independent: $5,000-$9,99969.0 Independent: $10,000-$19,99965.2 Independent: $20,000-$29,99945.4 Independent: $30,000-$49,99933.9 Independent: $50,000 or more13.4 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: INCOME and PELLAMT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES PowerStats on 5/24/2013. cgcbd593 Total aid amount by Attendance intensity (all schools) and NPSAS institution sector (with multiple). Total aid amount(Avg>0) Estimates Total10,775.8 Attendance intensity (all schools) Exclusively full-time13,217.1 Exclusively part-time5,025.3 Mixed full-time and part-time10,387.1 NPSAS institution sector (with multiple) Public less-than-2-year5,450.0 Public 2-year4,737.4 Public 4-year non-doctorate-granting8,758.7 Public 4-year doctorate-granting12,358.6 Private nonprofit less-than-4-year11,476.3 Private nonprofit 4-year non-doctorate-granting21,089.5 Private nonprofit 4-year doctorate-granting23,767.8 Private for profit less-than-2-year10,017.9 Private for profit 2-year10,631.2 Private for profit 4-year11,551.3 Attended more than one institution11,050.9 The names of the variables used in this table are: TOTAID, ATTNPTRN and AIDSECT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES PowerStats on 5/24/2013. cgcbd534 Dependent students: Parent's income by NPSAS institution sector (4 with multiple). Less than $29,000(%) $29,000-63,999(%) $64,000-103,999(%) $104,000 or more(%) Estimates Total24.5 24.4 25.0 26.1 NPSAS institution sector (4 with multiple) Public 4-year21.2 22.9 25.5 30.4 Private nonprofit 4-year17.7 21.9 26.5 33.9 Public 2-year29.7 27.8 24.9 17.7 Private for profit44.6 26.7 16.9 11.7 Others or attended more than one school21.2 22.5 25.2 31.1 The names of the variables used in this table are: DEPINC and SECTOR4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES PowerStats on 5/24/2013. cgcbd9c5 Average Total aid amount by Undergraduate degree program. Total aid amount(Avg) Estimates Total7,618.5 Undergraduate degree program Certificate5,501.9 Associate's degree3,888.9 Bachelor's degree11,834.2 Not in a degree program or others1,357.8 The names of the variables used in this table are: TOTAID and UGDEG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES PowerStats on 5/24/2013. cgcbd0a1 Direct Subsidized and Unsubsidized Loans with (percent > 0.5) by Graduate degree program. Direct Subsidized and Unsubsidized Loans(%>0.5) Estimates Total43.0 Graduate degree program Master's degree43.7 Post-baccalaureate or post-master's certificate25.3 Doctor's degree - research/scholarship23.2 Doctor's degree - professional practice78.8 Doctor's degree - other48.1 Not in a degree program9.3 The names of the variables used in this table are: GRADDEG and STAFFAMT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES PowerStats on 5/24/2013. cgcbd6a2 Total assistantships amount with (percent > 0.5) by Attendance intensity (all schools). Total assistantships amount(%>0.5) Estimates Total11.8 Attendance intensity (all schools) Exclusively full-time18.0 Exclusively part-time4.3 Mixed full-time and part-time11.6 The names of the variables used in this table are: GRASTAMT and ATTNPTRN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES PowerStats on 5/24/2013. cgcbdf53 Institutional tuition & fee waivers with (percent > 0.5) by Graduate degree program. Institutional tuition & fee waivers(%>0.5) Estimates Total8.0 Graduate degree program Master's degree6.2 Post-baccalaureate or post-master's certificate4.3 ! Doctor's degree - research/scholarship24.4 Doctor's degree - professional practice4.2 Doctor's degree - other7.9 Not in a degree program4.0 !! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: GRADDEG and INSWAIV. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES PowerStats on 5/24/2013. cgcbd144 Total loans with (percent > 0.5) by Total income (continuous). Total loans(%>0.5) Estimates Total45.1 Total income (continuous) Less than $15,00056.8 $15,000-34,29948.2 $34,270-68,05942.3 $68,060 or more30.4 The names of the variables used in this table are: CINCOME and TOTLOAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES PowerStats on 5/24/2013. cgcbdd15 Average Total loans by NPSAS institution type: Graduate (with multiple). Total loans(Avg) Estimates Total9,656.8 NPSAS institution type: Graduate (with multiple) Public 4-year non-doctorate-granting4,215.1 Public 4-year doctorate-granting7,540.4 Private nonprofit 4-year nondoctorate7,216.5 Private nonprofit 4-year doctorate-granting13,393.3 Private for profit 4-year9,931.8 Attended more than one institution11,335.2 The names of the variables used in this table are: AIDSECTG and TOTLOAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2011-12 National Postsecondary Student Aid Study (NPSAS:12).Computation by NCES PowerStats on 5/24/2013. cgcbd191 Attendance intensity (all schools) by NPSAS institution type. Exclusively full-time(%) Exclusively part-time(%) Mixed full-time and part-time(%) Total Estimates Total47.7 35.4 16.9 100% NPSAS institution type Public less-than-2-year63.6 31.4 5.0 ! 100% Public 2-year26.8 57.1 16.2 100% Public 4-year nondoctorate53.7 27.5 18.8 100% Public 4-year doctorate63.4 15.4 21.2 100% Private not-for-profit less than 4-year54.5 29.0 16.6 100% Private not-for-profit 4-yr nondoctorate67.7 18.3 14.0 100% Private not-for-profit 4-year doctorate72.7 13.7 13.7 100% Private for-profit less-than-2-year74.4 15.7 9.9 100% Private for-profit 2 years or more66.2 18.5 15.3 100% ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent. NOTE: Data users who plan to compare student loan estimates from NPSAS:08 with prior years should be aware that the data on Stafford loans are currently only directly comparable with NPSAS:04. For more information, visit http://nces.ed.gov/das. The names of the variables used in this table are: SECTOR9 and ATTNPTRN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08). Computation by NCES PowerStats on 10/1/2010. cgeakc92 Federal Pell grant with (percent > 0) by Total income by dependency. Federal Pell grant(%>0) Estimates Total27.3 Total income by dependency Dependent: Less than $10,00063.2 Dependent: $10,000-$19,99972.7 Dependent: $20,000-$29,99964.9 Dependent: $30,000-$39,99953.5 Dependent: $40,000-$49,99932.0 Dependent: $50,000-$59,99915.4 Dependent: $60,000-$69,9992.3 Dependent: $70,000-$79,9990.0 Dependent: $80,000-$99,9990.0 Dependent: $100,000 or more0.0 Independent: Less than $5,00053.3 Independent: $5,000-$9,99965.5 Independent: $10,000-$19,99952.3 Independent: $20,000-$29,99934.8 Independent: $30,000-$49,99928.2 Independent: $50,000 or more0.2 ! ! Interpret data with caution. Relative standard error (RSE) falls between 30 and 50 percent. NOTE: Data users who plan to compare student loan estimates from NPSAS:08 with prior years should be aware that the data on Stafford loans are currently only directly comparable with NPSAS:04. For more information, visit http://nces.ed.gov/das. The names of the variables used in this table are: INCOME and PELLAMT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08). Computation by NCES PowerStats on 10/1/2010. cgfak1b3 Average>0 Student budget minus all aid by Institution sector (with multiple), for Attendance intensity (all schools) (Exclusively full-time). Student budget minus all aid(Avg>0) Estimates Total11,658.9 Institution sector (with multiple) Public less-than-2-year9,667.4 Public 2-year7,560.8 Public 4-year nondoctorate8,922.5 Public 4-year doctorate11,625.2 Private not-for-profit less than 4-year10,782.5 Private not-for-profit 4-yr nondoctorate14,462.2 Private not-for-profit 4-year doctorate20,047.5 Private for-profit less-than-2-year10,298.3 Private for-profit 2 years or more14,406.9 Attended more than one institution‡ ‡ Reporting standards not met.NOTE: Data users who plan to compare student loan estimates from NPSAS:08 with prior years should be aware that the data on Stafford loans are currently only directly comparable with NPSAS:04. For more information, visit http://nces.ed.gov/das. The names of the variables used in this table are: NETCST1, ATTNPTRN and AIDSECT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08). Computation by NCES PowerStats on 10/1/2010. cgeakf74 Dependent parent income by Institution sector (4 with multiple). Less than $36,000(%) $36,000-66,999(%) $67,000-104,999(%) $105,000 or more(%) Total Estimates Total24.8 25.5 25.0 24.7 100% Institution sector (4 with multiple) Public 4-year20.6 22.7 27.4 29.2 100% Private not-for-profit 4-year17.5 20.9 25.3 36.4 100% Public 2-year30.6 31.4 23.2 14.8 100% Private for-profit50.1 25.1 15.9 8.9 100% NOTE: Data users who plan to compare student loan estimates from NPSAS:08 with prior years should be aware that the data on Stafford loans are currently only directly comparable with NPSAS:04. For more information, visit http://nces.ed.gov/das. The names of the variables used in this table are: DEPINC and SECTOR4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08). Computation by NCES PowerStats on 10/1/2010. cgeak3a5 Student budget minus all aid by Undergraduate degree program. Student budget minus all aid(Avg) Estimates Total8,043.5 Undergraduate degree program Certificate6,905.4 Associate's degree6,018.8 Bachelor's degree10,344.9 Not in a degree program or others5,315.6 NOTE: Data users who plan to compare student loan estimates from NPSAS:08 with prior years should be aware that the data on Stafford loans are currently only directly comparable with NPSAS:04. For more information, visit http://nces.ed.gov/das. The names of the variables used in this table are: NETCST1 and UGDEG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08). Computation by NCES PowerStats on 10/1/2010. cgeaka81 Cumulative amount borrowed for grad with (percent > .5) by Graduate degree program. Cumulative amount borrowed for grad(%>0.5) Estimates Total53.2 Graduate degree program Master's degree52.8 Doctoral degree46.5 First-professional degree82.1 Post-BA or post-master's certificate51.6 Not in a degree program34.8 NOTE: Data users who plan to compare student loan estimates from NPSAS:08 with prior years should be aware that the data on Stafford loans are currently only directly comparable with NPSAS:04. For more information, visit http://nces.ed.gov/das. The names of the variables used in this table are: GRADDEG and BORAMT2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08). Computation by NCES PowerStats on 10/1/2010. ckeake32 Total assistantships amount with (percent > .5) by Attendance intensity (all schools). Total assistantships amount(%>0.5) Estimates Total15.2 Attendance intensity (all schools) Exclusively full-time23.3 Exclusively part-time6.5 Mixed full-time and part-time19.8 NOTE: Data users who plan to compare student loan estimates from NPSAS:08 with prior years should be aware that the data on Stafford loans are currently only directly comparable with NPSAS:04. For more information, visit http://nces.ed.gov/das. The names of the variables used in this table are: GRASTAMT and ATTNPTRN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08). Computation by NCES PowerStats on 10/1/2010. ckeak413 Average>0 Institutional tuition & fee waivers by Graduate degree program. Institutional tuition & fee waivers(Avg>0) Estimates Total6,785.2 Graduate degree program Master's degree6,387.0 Doctoral degree7,826.7 First-professional degree8,521.4 Post-BA or post-master's certificate‡ Not in a degree program2,206.9 ‡ Reporting standards not met. NOTE: Data users who plan to compare student loan estimates from NPSAS:08 with prior years should be aware that the data on Stafford loans are currently only directly comparable with NPSAS:04. For more information, visit http://nces.ed.gov/das. The names of the variables used in this table are: GRADDEG and INSWAIV. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08). Computation by NCES PowerStats on 10/1/2010. ckeak624 Total loans with (percent > .5) by Total income: Parents and independent. Total loans(%>0.5) Estimates Total42.7 Total income: Parents and independent Less than $13,20055.3 $13,200-37,39950.4 $37,400-71,59938.6 $71,600 or more26.4 NOTE: Data users who plan to compare student loan estimates from NPSAS:08 with prior years should be aware that the data on Stafford loans are currently only directly comparable with NPSAS:04. For more information, visit http://nces.ed.gov/das. The names of the variables used in this table are: CINCOME and TOTLOAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08). Computation by NCES PowerStats on 10/1/2010. ckeaka75 Average>0 Total loans by Institution type: Graduate (with multiple). Total loans(Avg>0) Estimates Total18,494.7 Institution type: Graduate (with multiple) Public 4-year nondoctorate-granting10,668.2 Public 4-year doctorate-granting16,470.2 Private not-for-profit 4-yr nondoctorate-granting14,748.3 Private not-for-profit 4-year doctorate-granting23,496.8 Private for profit 4-year17,680.3 Attended more than one institution17,270.5 NOTE: Data users who plan to compare student loan estimates from NPSAS:08 with prior years should be aware that the data on Stafford loans are currently only directly comparable with NPSAS:04. For more information, visit http://nces.ed.gov/das. The names of the variables used in this table are: AIDSECTG and TOTLOAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. Source: U.S. Department of Education, National Center for Education Statistics, 2007-08 National Postsecondary Student Aid Study (NPSAS:08). Computation by NCES PowerStats on 10/1/2010. bbfakc71 Q76 Gender by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level, Collapsed total K-12 and ungraded enrollment in school, and Percentage of enrolled students approved for the NSLP at school. Male(%) Female(%) Total Estimates Total25.0 75.0 100% Charter school identifier School is a public charter school27.0 73.0 100% School is not a public charter school25.0 75.0 100% Urbanicity of the school Large or mid-size central city24.2 75.8 100% Urban fringe of a large or mid-size central city25.1 74.9 100% Small town/rural25.9 74.1 100% Four-category school level Primary11.6 88.4 100% Middle31.0 69.0 100% High43.6 56.4 100% Combined32.2 67.8 100% Collapsed total K-12 and ungraded enrollment in school 01-4929.4 70.6 100% 50-9928.9 71.1 100% 100-14927.6 72.4 100% 150-19921.8 78.2 100% 200-34919.1 80.9 100% 350-49919.3 80.7 100% 500-74919.3 80.7 100% 750-99925.4 74.6 100% 1,000-1,19930.3 69.7 100% 1,200-1,49939.1 60.9 100% 1,500-1,99940.6 59.4 100% 2,000 or more42.2 57.8 100% The names of the variables used in this table are: T0408, SCHSIZE, SCHLEVE2, URBANS03 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2003-04.Computation by NCES PowerStats on 9/21/2017.cbkbhg302 Q1 Teacher's main position at the school by Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Itinerant teacher(%) Long-term substitute(%) Total Estimates Total92.4 3.4 3.4 0.7 100% Four-category school level Primary90.8 3.4 5.1 0.7 100% Middle93.3 3.1 2.8 0.8 100% High94.6 3.4 1.2 0.8 100% Combined93.5 4.3 1.5 0.7 100% Collapsed total K-12 and ungraded enrollment in school 01-4983.4 10.0 5.3 ! 1.2 ! 100% 50-9985.5 8.0 5.8 0.7 !! 100% 100-14984.6 6.4 7.8 1.2 ! 100% 150-19988.3 3.6 7.8 ‡ 100% 200-34988.2 3.9 7.4 0.5 ! 100% 350-49990.2 3.9 5.0 0.8 100% 500-74992.8 3.3 3.1 0.8 100% 750-99995.0 2.6 1.7 0.7 100% 1,000-1,19996.0 2.7 0.8 0.5 !! 100% 1,200-1,49995.1 2.8 1.4 0.7 ! 100% 1,500-1,99994.7 3.1 1.3 1.0 100% 2,000 or more96.6 2.3 0.4 0.7 ! 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: T0026, SCHSIZE and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2003-04.Computation by NCES PowerStats on 9/21/2017.cbkbhgd33 Average estimated number of full-time equivalent teachers in the school (>0) and Average Q71 Base salary for the entire 2007-08 school year (>0) by Collapsed total K-12 and ungraded enrollment in school and Urbanicity of the school. Estimated number of full-time equivalent teachers in the school(Avg>0) Q71. Base salary for the school year(Avg>0) Estimates Total52.2 43,680.0 Collapsed total K-12 and ungraded enrollment in school 01-498.0 36,907.7 50-9910.2 35,803.7 100-14914.4 36,350.1 150-19916.4 38,569.7 200-34923.5 41,427.0 350-49931.3 43,136.9 500-74942.2 43,527.3 750-99957.0 44,551.4 1,000-1,19967.8 45,721.5 1,200-1,49984.5 44,495.9 1,500-1,999104.0 46,513.6 2,000 or more141.5 48,477.5 Urbanicity of the school Large or mid-size central city59.2 44,873.7 Urban fringe of a large or mid-size central city54.4 45,335.0 Small town/rural35.9 37,310.2 The names of the variables used in this table are: NUMTCH, SCHSIZE, T0399 and URBANS03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhbcb84 Q42a Professional development: reading instruction by Charter school identifier, Urbanicity of the school, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Yes(%) No(%) Total Estimates Total60.4 39.6 100% Charter school identifier School is a public charter school58.1 41.9 100% School is not a public charter school60.4 39.6 100% Urbanicity of the school Large or mid-size central city69.0 31.0 100% Urban fringe of a large or mid-size central city58.3 41.7 100% Small town/rural53.3 46.7 100% Four-category school level Primary76.3 23.7 100% Middle52.3 47.7 100% High39.9 60.1 100% Combined46.6 53.4 100% Collapsed total K-12 and ungraded enrollment in school 01-4951.0 49.0 100% 50-9948.5 51.5 100% 100-14950.5 49.5 100% 150-19961.0 39.0 100% 200-34964.2 35.8 100% 350-49965.3 34.7 100% 500-74965.9 34.1 100% 750-99963.6 36.4 100% 1,000-1,19952.0 48.0 100% 1,200-1,49948.1 51.9 100% 1,500-1,99945.8 54.2 100% 2,000 or more47.2 52.8 100% The names of the variables used in this table are: T0249, SCHSIZE, SCHLEVE2, URBANS03 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhbddb5 Q22a Teacher currently holds a master's degree by Three-category school level and Program type of school. Yes(%) No(%) Total Estimates Total47.2 52.8 100% Three-category school level Elementary46.4 53.6 100% Secondary49.9 50.1 100% Combined41.4 58.6 100% Program type of school Regular47.1 52.9 100% Special program emphasis46.6 53.4 100% Special Education64.2 35.8 100% Vocational Education50.0 50.0 100% Alternative45.4 54.6 100% The names of the variables used in this table are: PGMTYPE, T0123 and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhbec61 Q78 Gender by Three-level private school typology, Urbanicity of the school, Four-category school level, and Collapsed total K-12 and ungraded enrollment in school. Male(%) Female(%) Total Estimates Total23.6 76.4 100% Three-level private school typology Roman Catholic19.8 80.2 100% Other religious23.9 76.1 100% Nonsectarian28.1 71.9 100% Urbanicity of the school Large or mid-size central city23.9 76.1 100% Urban fringe of a large or mid-size central city23.0 77.0 100% Small town/rural25.2 74.8 100% Four-category school level Primary12.9 87.1 100% Middle28.7 ! 71.3 100% High46.3 53.7 100% Combined27.3 72.7 100% Collapsed total K-12 and ungraded enrollment in school 01-4919.3 !! 80.7 100% 50-9922.8 77.2 100% 100-14918.1 81.9 100% 150-19918.4 81.6 100% 200-34922.0 78.0 100% 350-49924.5 75.5 100% 500-74923.4 76.6 100% 750-99929.8 70.2 100% 1,000-1,19948.1 51.9 100% 1,200-1,49940.8 59.2 100% 1,500-1,99937.9 62.1 100% 2,000 or more44.9 55.1 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: T0408, SCHSIZE, SCHLEVE2, URBANS03 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhbg442 Q1 Teacher's main position at the school by Three-level private school typology, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Itinerant teacher(%) Long-term substitute(%) Total Estimates Total82.3 16.6 0.7 0.4 100% Three-level private school typology Roman Catholic85.5 12.7 1.3 0.5 !! 100% Other religious75.4 23.9 0.4 ! 0.3 ! 100% Nonsectarian87.9 11.3 0.5 ! 0.4 ! 100% Four-category school level Primary82.6 16.2 1.0 0.3 ! 100% Middle62.9 31.1 ! ‡ ‡ 100% High85.0 13.4 1.1 ! 0.5 !! 100% Combined81.1 18.2 0.3 ! 0.4 !! 100% Collapsed total K-12 and ungraded enrollment in school 01-4980.2 18.7 ! 0.5 !! 0.7 !! 100% 50-9979.8 19.1 0.6 ! 0.5 ! 100% 100-14979.3 19.0 0.9 ! 0.9 !! 100% 150-19980.1 18.6 1.2 ! ‡ 100% 200-34979.9 18.5 1.3 0.3 !! 100% 350-49985.0 14.4 ‡ ‡ 100% 500-74986.5 13.3 ‡ ‡ 100% 750-99987.7 11.5 ‡ ‡ 100% 1,000-1,19986.1 11.5 ! ‡ ‡ 100% 1,200-1,49989.6 9.9 !! ‡ ‡ 100% 1,500-1,99976.7 23.3 !! ‡ ‡ 100% 2,000 or more92.9 ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: T0026, SCHSIZE, SCHLEVE2 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2003-04.Computation by NCES PowerStats on 9/21/2017.cbkbhgc63 Average estimated number of full-time equivalent teachers in the school (>0) and Average Q72 Base salary for the entire 2007-08 school year (>0) by Collapsed total K-12 and ungraded enrollment in school and Urbanicity of the school. Estimated number of full-time equivalent teachers in the school(Avg>0) Q72. Base salary for the school year(Avg>0) Estimates Total33.0 29,274.4 Collapsed total K-12 and ungraded enrollment in school 01-495.1 20,289.1 50-999.7 22,901.0 100-14914.7 24,525.2 150-19916.4 26,350.9 200-34922.6 28,544.7 350-49935.1 31,747.5 500-74948.1 34,510.6 750-99972.2 36,024.4 1,000-1,19998.3 42,597.5 1,200-1,499112.8 39,566.7 1,500-1,999131.1 36,347.2 2,000 or more217.6 ! 50,808.9 Urbanicity of the school Large or mid-size central city39.1 31,353.2 Urban fringe of a large or mid-size central city31.1 29,181.7 Small town/rural19.6 21,922.8 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: NUMTCH, SCHSIZE, T0399 and URBANS03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhbk704 Q43a Professional development: reading instruction by Three-level private school typology, Urbanicity of the school and Four-category school level. Yes(%) No(%) Total Estimates Total28.3 71.7 100% Three-level private school typology Roman Catholic33.7 66.3 100% Other religious25.0 75.0 100% Nonsectarian26.0 74.0 100% Urbanicity of the school Large or mid-size central city29.8 70.2 100% Urban fringe of a large or mid-size central city27.9 72.1 100% Small town/rural24.7 ! 75.3 100% Four-category school level Primary39.4 60.6 100% Middle10.9 !! 89.1 100% High14.5 85.5 100% Combined20.3 79.7 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: T0249, SCHLEVE2, URBANS03 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhbm9b5 Q22a Teacher currently holds a master's degree by Private school typology and Four-category school level. Yes(%) No(%) Total Estimates Total37.1 62.9 100% Nine-level private school typology Roman Catholic- Parochial (or inter-parochial)28.2 71.8 100% Roman Catholic- Diocesan34.1 65.9 100% Roman Catholic- Private55.1 44.9 100% Other religious- Conservative Christian23.0 77.0 100% Other religious, Affiliated with a Religious School Association42.0 58.0 100% Other religious, Not Affiliated with a Religious School Association29.8 70.2 100% Nonsectarian- Regular49.6 50.4 100% Nonsectarian- Special Emphasis42.9 57.1 100% Nonsectarian- Special Education32.6 67.4 100% Four-category school level Primary30.0 70.0 100% Middle40.9 59.1 100% High52.5 47.5 100% Combined38.8 61.2 100% The names of the variables used in this table are: SCHLEVE2, T0123 and TYPOLOGY. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhbnca1 Q76/Q78 Gender by Urbanicity of the school, Four-category school level, Collapsed total K-12 and ungraded enrollment in school, and Percentage of enrolled students approved for the NSLP at school. Male(%) Female(%) Total Estimates Total24.8 75.2 100% Urbanicity of the school Large or mid-size central city24.1 75.9 100% Urban fringe of a large or mid-size central city24.9 75.1 100% Small town/rural25.8 74.2 100% Four-category school level Primary11.8 88.2 100% Middle31.0 69.0 100% High43.8 56.2 100% Combined29.7 70.3 100% Collapsed total K-12 and ungraded enrollment in school 01-4922.6 ! 77.4 100% 50-9925.2 74.8 100% 100-14922.9 77.1 100% 150-19920.5 79.5 100% 200-34919.7 80.3 100% 350-49919.8 80.2 100% 500-74919.6 80.4 100% 750-99925.7 74.3 100% 1,000-1,19931.4 68.6 100% 1,200-1,49939.1 60.9 100% 1,500-1,99940.5 59.5 100% 2,000 or more42.3 57.7 100% Percentage of enrolled students approved for the NSLP at school 0% to 25%27.2 72.8 100% 26% to 50%25.3 74.7 100% 51% to 75%22.2 77.8 100% More than 75%20.4 79.6 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: T0408, NSLAPP_S, SCHSIZE, SCHLEVE2 and URBANS03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhb8a2 Q1 Teacher's main position at the school by Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Itinerant teacher(%) Long-term substitute(%) Total Estimates Total91.2 5.0 3.1 0.7 100% Four-category school level Primary89.8 5.0 4.6 0.6 100% Middle93.2 3.3 2.7 0.8 100% High93.9 4.1 1.2 0.8 100% Combined87.4 11.1 0.9 0.6 100% Collapsed total K-12 and ungraded enrollment in school 01-4981.3 15.7 2.1 ! 0.9 !! 100% 50-9982.0 14.7 2.6 0.6 ! 100% 100-14982.0 12.5 4.4 1.0 ! 100% 150-19985.0 9.6 5.1 0.2 !! 100% 200-34986.5 6.9 6.1 0.5 100% 350-49989.7 5.0 4.5 0.8 100% 500-74992.4 3.9 2.9 0.8 100% 750-99994.5 3.2 1.6 0.7 100% 1,000-1,19995.4 3.2 0.9 0.5 ! 100% 1,200-1,49994.9 3.1 1.3 0.7 100% 1,500-1,99994.2 3.6 1.3 0.9 100% 2,000 or more96.5 2.4 0.4 0.7 ! 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: T0026, SCHSIZE and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 2003-04.Computation by NCES PowerStats on 9/21/2017.cbkbhg7b3 Average estimated number of full-time equivalent teachers in the school (>0) and Average Q71/Q72 Base salary for the entire 2007-08 school year (>0) by Collapsed total K-12 and ungraded enrollment in school and Urbanicity of the school. Estimated number of full-time equivalent teachers in the school(Avg>0) Q71. / Q72. Base salary for the school year(Avg>0) Estimates Total49.8 41,882.1 Collapsed total K-12 and ungraded enrollment in school 01-496.0 26,001.0 50-999.9 28,048.5 100-14914.6 30,539.4 150-19916.4 33,629.9 200-34923.3 38,770.5 350-49931.7 41,966.0 500-74942.5 42,976.0 750-99958.0 43,967.8 1,000-1,19969.6 45,535.8 1,200-1,49985.6 44,311.7 1,500-1,999104.8 46,230.9 2,000 or more142.6 48,510.2 Urbanicity of the school Large or mid-size central city55.9 42,665.7 Urban fringe of a large or mid-size central city51.5 43,386.4 Small town/rural34.7 36,191.9 The names of the variables used in this table are: NUMTCH, SCHSIZE, T0399 and URBANS03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhbfb4 Q42a/Q43a Professional development: reading instruction by Urbanicity of the school, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Yes(%) No(%) Total Estimates Total56.4 43.6 100% Urbanicity of the school Large or mid-size central city62.5 37.5 100% Urban fringe of a large or mid-size central city54.6 45.4 100% Small town/rural51.2 48.8 100% Four-category school level Primary71.8 28.2 100% Middle52.1 47.9 100% High37.9 62.1 100% Combined33.4 66.6 100% Collapsed total K-12 and ungraded enrollment in school 01-4935.3 64.7 100% 50-9937.7 62.3 100% 100-14941.4 58.6 100% 150-19950.0 50.0 100% 200-34957.3 42.7 100% 350-49961.6 38.4 100% 500-74963.4 36.6 100% 750-99960.6 39.4 100% 1,000-1,19950.0 50.0 100% 1,200-1,49946.9 53.1 100% 1,500-1,99944.9 55.1 100% 2,000 or more46.7 53.3 100% The names of the variables used in this table are: T0249, SCHSIZE, SCHLEVE2 and URBANS03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhb805 Q22a Teacher currently holds a master's degree by Four-category school level and Program type of school. Yes(%) No(%) Total Estimates Total46.0 54.0 100% Four-category school level Primary44.4 55.6 100% Middle46.6 53.4 100% High50.5 49.5 100% Combined40.3 59.7 100% Program type of school Regular45.9 54.1 100% Montessori33.6 66.4 100% Special program emphasis46.9 53.1 100% Special Education50.9 49.1 100% Vocational Education50.0 50.0 100% Alternative44.2 55.8 100% Early childhood program or day care center‡ ‡ 100% ‡ Reporting standards not met. The names of the variables used in this table are: PGMTYPE, T0123 and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhb791 Q67 Gender by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level, Collapsed total K-12 and ungraded enrollment in school, and Percentage of enrolled students approved for the NSLP at school. Male(%) Female(%) Total Estimates Total24.1 75.9 100% Charter school identifier School is a public charter school23.7 76.3 100% School is not a public charter school24.1 75.9 100% Collapsed urban-centric school locale code City24.6 75.4 100% Suburb23.4 76.6 100% Town24.4 75.6 100% Rural24.4 75.6 100% Four-category school level Primary11.1 88.9 100% Middle28.5 71.5 100% High41.7 58.3 100% Combined30.1 69.9 100% Collapsed total K-12 and ungraded enrollment in school 1-4928.5 71.5 100% 50-9929.2 70.8 100% 100-14928.9 71.1 100% 150-19923.8 76.2 100% 200-34919.7 80.3 100% 350-49917.0 83.0 100% 500-74918.3 81.7 100% 750-99924.6 75.4 100% 1,000-1,19930.3 69.7 100% 1,200-1,49936.2 63.8 100% 1,500-1,99938.7 61.3 100% 2,000 or more39.4 60.6 100% Percentage of enrolled students approved for the NSLP at school 0% to 25%26.3 73.7 100% 26% to 50%24.6 75.4 100% 51% to 75%21.7 78.3 100% More than 75%21.0 79.0 100% The names of the variables used in this table are: URBANS12, CHARFLAG, NSLAPP_S, SCHLEVE2, SCHSIZE and T0352. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhbf702 Q1 Teacher's main position at the school by Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Administrator(%) Other(%) Total Estimates Total91.5 3.2 0.3 5.0 100% Four-category school level Primary88.8 3.6 0.1 !! 7.5 100% Middle94.5 2.1 0.1 !! 3.2 100% High93.8 3.3 0.6 2.3 100% Combined92.8 3.4 0.7 3.1 100% Collapsed total K-12 and ungraded enrollment in school 1-4980.6 11.2 ! 1.1 !! 7.0 100% 50-9982.5 5.0 1.7 !! 10.8 100% 100-14984.0 4.9 ! 2.0 ! 9.2 100% 150-19987.2 3.3 0.3 !! 9.3 100% 200-34986.3 5.2 0.4 !! 8.1 100% 350-49989.4 3.7 0.1 ! 6.8 100% 500-74992.3 2.7 0.1 ! 4.9 100% 750-99993.4 2.5 0.1 !! 4.0 100% 1,000-1,19994.8 2.5 0.1 !! 2.7 ! 100% 1,200-1,49995.3 1.8 0.4 !! 2.5 100% 1,500-1,99994.1 3.2 0.7 ! 2.0 100% 2,000 or more95.2 2.7 0.5 ! 1.6 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: SCHSIZE, SCHLEVE2 and T0025. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhb703 Average estimated number of full-time equivalent teachers in the school (>0) and Average Q62 Base salary for the entire 2007-08 school year (>0) by Collapsed total K-12 and ungraded enrollment in school and Collapsed urban-centric school locale code. Estimated number of full-time equivalent teachers in the school(Avg>0) Q62. Base salary for the entire 2007-08 school year(Avg>0) Estimates Total54.7 49,035.3 Collapsed total K-12 and ungraded enrollment in school 1-495.3 42,287.4 50-999.9 41,735.5 100-14914.9 42,185.2 150-19917.2 44,373.1 200-34923.4 46,053.3 350-49932.2 48,029.3 500-74942.3 48,896.1 750-99956.8 49,777.8 1,000-1,19971.0 51,242.9 1,200-1,49990.5 51,817.5 1,500-1,999105.5 51,251.0 2,000 or more146.9 53,664.0 Collapsed urban-centric school locale code City59.5 50,585.9 Suburb63.2 53,569.7 Town43.7 44,727.8 Rural44.0 43,416.4 The names of the variables used in this table are: NUMTCH, URBANS12, SCHSIZE and T0343. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhb8b4 Q43a Professional development: reading instruction by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Yes(%) No(%) Total Estimates Total60.8 39.2 100% Charter school identifier School is a public charter school57.9 42.1 100% School is not a public charter school60.9 39.1 100% Collapsed urban-centric school locale code City64.3 35.7 100% Suburb60.8 39.2 100% Town61.1 38.9 100% Rural57.3 42.7 100% Four-category school level Primary75.5 24.5 100% Middle53.8 46.2 100% High42.8 57.2 100% Combined51.2 48.8 100% Collapsed total K-12 and ungraded enrollment in school 1-4950.5 49.5 100% 50-9950.4 49.6 100% 100-14953.5 46.5 100% 150-19959.6 40.4 100% 200-34962.2 37.8 100% 350-49968.9 31.1 100% 500-74965.9 34.1 100% 750-99962.7 37.3 100% 1,000-1,19953.2 46.8 100% 1,200-1,49951.1 48.9 100% 1,500-1,99945.8 54.2 100% 2,000 or more47.7 52.3 100% The names of the variables used in this table are: T0240, SCHSIZE, URBANS12, SCHLEVE2 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhb4c5 Highest degree earned by Four-category school level and Program type of school. Associate's degree or no college degree(%) Bachelor's degree(%) Master's degree(%) Education specialist or Certificate of Advanced Graduate Studies(%) Doctorate or Professional degree(%) Total Estimates Total0.8 47.4 44.5 6.4 0.9 100% Four-category school level Primary0.3 48.8 43.9 6.5 0.5 100% Middle0.1 !! 47.8 44.4 6.8 0.9 100% High2.1 43.6 46.7 6.2 1.5 100% Combined0.9 54.6 38.2 5.2 1.1 ! 100% Program type of school Regular0.6 47.7 44.6 6.3 0.8 100% Montessori‡ ‡ ‡ ‡ ‡ 100% Special program emphasis0.5 ! 41.4 49.2 7.4 1.5 ! 100% Special Education‡ 44.1 45.8 7.0 2.9 !! 100% Career/Technical/Vocational Education19.9 29.5 41.8 6.2 2.5 ! 100% Alternative1.6 ! 50.8 36.9 9.5 1.1 ! 100% Early Childhood Program/Daycare Center‡ ‡ ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: PGMTYPE, SCHLEVE2 and HIDEGR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhbn8c1 Q68 Gender by Three-level private school typology, Collapsed urban-centric school locale code, Four-category school level, and Collapsed total K-12 and ungraded enrollment in school. Male(%) Female(%) Total Estimates Total26.0 74.0 100% Three-level private school typology Catholic22.0 78.0 100% Other religious27.3 72.7 100% Nonsectarian29.1 70.9 100% Collapsed urban-centric school locale code City27.9 72.1 100% Suburb25.0 75.0 100% Town19.3 80.7 100% Rural26.8 73.2 100% Four-category school level Primary13.1 86.9 100% Middle21.8 78.2 100% High47.9 52.1 100% Combined31.0 69.0 100% Collapsed total K-12 and ungraded enrollment in school 1-4922.8 77.2 100% 50-9919.5 80.5 100% 100-14921.2 78.8 100% 150-19924.3 75.7 100% 200-34921.7 78.3 100% 350-49925.9 74.1 100% 500-74931.8 68.2 100% 750-99938.9 61.1 100% 1,000-1,19942.8 57.2 100% 1,200-1,49928.2 71.8 100% 1,500-1,99947.5 52.5 100% 2,000 or more‡ ‡ 100% ‡ Reporting standards not met. The names of the variables used in this table are: T0352, SCHSIZE, URBANS12, SCHLEVE2 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhbd02 Q1 Teacher's main position at the school by Three-level private school typology, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Administrator(%) Other(%) Total Estimates Total79.0 15.1 3.2 2.7 100% Three-level private school typology Catholic82.5 12.0 1.8 3.6 100% Other religious76.0 18.4 3.7 1.9 100% Nonsectarian79.4 13.8 4.0 2.8 100% Four-category school level Primary79.4 15.4 2.0 3.2 100% Middle71.7 25.3 ! ‡ ‡ 100% High77.7 14.9 3.8 3.6 100% Combined79.2 14.7 4.2 1.9 100% Collapsed total K-12 and ungraded enrollment in school 1-4971.7 19.4 5.9 3.0 100% 50-9976.4 16.6 4.0 3.1 100% 100-14973.1 19.1 4.4 3.4 100% 150-19975.3 19.5 2.7 ! 2.6 100% 200-34978.0 16.2 2.6 3.1 100% 350-49982.0 13.3 2.9 ! 1.8 ! 100% 500-74984.4 12.0 1.8 ! 1.9 ! 100% 750-99985.2 6.8 ! 4.3 ! 3.7 ! 100% 1,000-1,19989.6 6.7 !! ‡ 2.1 !! 100% 1,200-1,49979.9 17.0 ! ‡ ‡ 100% 1,500-1,99988.3 ‡ ‡ ‡ 100% 2,000 or more‡ ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: SCHSIZE, SCHLEVE2, T0025 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhbdaa3 Average estimated number of full-time equivalent teachers in the school (>0) and Average Q62 Base salary for the entire 2007-08 school year (>0) by Collapsed total K-12 and ungraded enrollment in school and Collapsed urban-centric school locale code. Estimated number of full-time equivalent teachers in the school(Avg>0) Q62. Base salary for the entire 2007-08 school year(Avg>0) Estimates Total34.1 33,674.1 Collapsed total K-12 and ungraded enrollment in school 1-496.0 23,833.2 50-9910.1 26,876.4 100-14914.3 28,040.8 150-19917.5 30,922.8 200-34923.8 31,934.3 350-49934.4 36,139.2 500-74949.6 38,162.0 750-99976.3 43,930.3 1,000-1,19987.7 44,689.4 1,200-1,499113.9 46,370.7 1,500-1,999146.8 54,644.5 2,000 or more‡ ‡ Collapsed urban-centric school locale code City39.3 35,907.5 Suburb36.1 35,163.1 Town20.0 26,594.0 Rural21.3 26,938.6 ‡ Reporting standards not met. The names of the variables used in this table are: NUMTCH, URBANS12, SCHSIZE and T0343. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhb2e4 Q43a Professional development: reading instruction by Three-level private school typology and Four-category school level. Yes(%) No(%) Total Estimates Total30.7 69.3 100% Three-level private school typology Catholic33.9 66.1 100% Other religious29.8 70.2 100% Nonsectarian28.1 71.9 100% Four-category school level Primary40.9 59.1 100% Middle16.4 ! 83.6 100% High17.5 82.5 100% Combined25.6 74.4 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: T0240, SCHLEVE2 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhbfa5 Highest degree earned by Program type of school and Four-category school level. Associate's degree or no college degree(%) Bachelor's degree(%) Master's degree(%) Education specialist or Certificate of Advanced Graduate Studies(%) Doctorate or Professional degree(%) Total Estimates Total8.1 53.9 32.8 2.8 2.4 100% Program type of school Regular7.9 54.3 32.6 2.7 2.6 100% Montessori17.8 57.2 22.0 ‡ ‡ 100% Special program emphasis10.8 ! 40.5 42.2 5.7 ! ‡ 100% Special Education2.5 ! 52.2 39.6 5.5 ! ‡ 100% Career/Technical/Vocational Education‡ ‡ ‡ ‡ ‡ 100% Alternative14.6 ! 52.4 29.9 2.8 ! ‡ 100% Early Childhood Program/Daycare Center‡ ‡ ‡ ‡ ‡ 100% Four-category school level Primary9.0 60.6 27.7 2.1 0.6 ! 100% Middle‡ 61.7 32.6 ‡ ‡ 100% High2.3 ! 44.2 45.0 4.8 3.7 100% Combined9.7 50.6 33.3 2.8 3.7 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: PGMTYPE, SCHLEVE2 and HIDEGR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhb601 Q67/Q68 Gender by Collapsed urban-centric school locale code, Four-category school level, Collapsed total K-12 and ungraded enrollment in school, and Percentage of enrolled students approved for the NSLP at school. Male(%) Female(%) Total Estimates Total24.4 75.6 100% Collapsed urban-centric school locale code City25.2 74.8 100% Suburb23.6 76.4 100% Town24.0 76.0 100% Rural24.6 75.4 100% Four-category school level Primary11.3 88.7 100% Middle28.5 71.5 100% High42.2 57.8 100% Combined30.6 69.4 100% Collapsed total K-12 and ungraded enrollment in school 1-4924.6 75.4 100% 50-9923.3 76.7 100% 100-14925.3 74.7 100% 150-19924.0 76.0 100% 200-34920.2 79.8 100% 350-49917.8 82.2 100% 500-74919.3 80.7 100% 750-99925.3 74.7 100% 1,000-1,19931.4 68.6 100% 1,200-1,49935.7 64.3 100% 1,500-1,99938.9 61.1 100% 2,000 or more39.5 60.5 100% Percentage of enrolled students approved for the NSLP at school 0% to 25%25.7 74.3 100% 26% to 50%24.8 75.2 100% 51% to 75%21.6 78.4 100% More than 75%21.4 78.6 100% The names of the variables used in this table are: T0352, SCHSIZE, URBANS12, SCHLEVE2 and NSLAPP_S. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhbf92 Q1 Teacher's main position at the school by Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Administrator(%) Other(%) Total Estimates Total89.9 4.7 0.6 4.7 100% Four-category school level Primary87.7 4.9 0.3 7.1 100% Middle94.4 2.3 0.1 !! 3.2 100% High92.7 4.2 0.8 2.4 100% Combined85.2 9.7 2.7 2.4 100% Collapsed total K-12 and ungraded enrollment in school 1-4974.4 16.9 4.5 4.2 100% 50-9978.8 12.0 3.1 6.1 100% 100-14978.8 11.7 3.1 6.4 100% 150-19982.4 9.8 1.2 6.6 100% 200-34984.5 7.5 0.9 7.0 100% 350-49988.7 4.6 0.4 6.3 100% 500-74991.7 3.4 0.2 4.7 100% 750-99993.0 2.7 0.3 ! 4.0 100% 1,000-1,19994.3 2.8 0.2 !! 2.6 ! 100% 1,200-1,49994.3 2.8 0.5 ! 2.5 100% 1,500-1,99994.0 3.3 0.7 ! 2.0 100% 2,000 or more94.9 2.7 0.6 ! 1.7 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: SCHSIZE, SCHLEVE2 and T0025. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhbb03 Average estimated number of full-time equivalent teachers in the school (>0) and Average Q62 Base salary for the entire 2007-08 school year (>0) by Collapsed total K-12 and ungraded enrollment in school and Collapsed urban-centric school locale code. Estimated number of full-time equivalent teachers in the school(Avg>0) Q62. Base salary for the entire 2007-08 school year(Avg>0) Estimates Total52.1 47,114.2 Collapsed total K-12 and ungraded enrollment in school 1-495.8 29,521.8 50-9910.1 32,702.2 100-14914.6 35,476.6 150-19917.3 39,000.8 200-34923.5 43,017.4 350-49932.4 46,969.8 500-74942.8 48,109.6 750-99957.9 49,464.6 1,000-1,19972.4 50,677.3 1,200-1,49992.0 51,476.4 1,500-1,999106.3 51,323.7 2,000 or more146.8 53,726.0 Collapsed urban-centric school locale code City55.7 47,845.6 Suburb59.7 51,179.1 Town42.0 43,412.1 Rural42.3 42,195.7 The names of the variables used in this table are: NUMTCH, URBANS12, SCHSIZE and T0343. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhb4f4 Q43a Professional development: reading instruction by Collapsed urban-centric school locale code, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Yes(%) No(%) Total Estimates Total57.1 42.9 100% Collapsed urban-centric school locale code City57.9 42.1 100% Suburb56.8 43.2 100% Town59.5 40.5 100% Rural55.1 44.9 100% Four-category school level Primary71.6 28.4 100% Middle53.5 46.5 100% High41.0 59.0 100% Combined36.9 63.1 100% Collapsed total K-12 and ungraded enrollment in school 1-4936.8 63.2 100% 50-9940.7 59.3 100% 100-14944.9 55.1 100% 150-19948.2 51.8 100% 200-34956.0 44.0 100% 350-49965.6 34.4 100% 500-74963.3 36.7 100% 750-99960.5 39.5 100% 1,000-1,19950.1 49.9 100% 1,200-1,49949.3 50.7 100% 1,500-1,99945.1 54.9 100% 2,000 or more47.5 52.5 100% The names of the variables used in this table are: SCHSIZE, URBANS12, SCHLEVE2 and T0240. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhb655 Highest degree earned by Four-category school level and Program type of school. Associate's degree or no college degree(%) Bachelor's degree(%) Master's degree(%) Education specialist or Certificate of Advanced Graduate Studies(%) Doctorate or Professional degree(%) Total Estimates Total1.7 48.2 43.1 6.0 1.1 100% Four-category school level Primary1.3 50.1 42.1 6.0 0.5 100% Middle0.1 !! 47.9 44.3 6.7 0.9 100% High2.1 43.6 46.5 6.1 1.7 100% Combined5.8 52.3 35.5 3.9 2.5 100% Program type of school Regular1.5 48.5 43.1 5.9 1.0 100% Montessori17.8 57.2 22.0 ‡ ‡ 100% Special program emphasis1.7 ! 41.3 48.4 7.2 1.4 ! 100% Special Education1.1 ! 47.5 43.2 6.4 1.8 !! 100% Career/Technical/Vocational Education19.9 29.5 41.8 6.2 2.5 ! 100% Alternative3.5 51.1 35.9 8.5 1.0 ! 100% Early Childhood Program/Daycare Center‡ ‡ ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: PGMTYPE, SCHLEVE2 and HIDEGR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhb301 Q64. Gender by Q3a. Teacher's school is a public charter school, Urbanicity of the school, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Male(%) Female(%) Total Estimates Total25.1 74.9 100% Q3a. Teacher's school is a public charter school Yes25.7 74.3 100% No25.1 74.9 100% Urbanicity of the school Large or mid-size central city24.5 75.5 100% Urban fringe of large or mid-size city24.6 75.4 100% Small town/Rural26.9 73.1 100% Four-category school level Elementary11.6 88.4 100% Middle school28.9 71.1 100% Secondary45.1 54.9 100% Combined32.7 67.3 100% Collapsed total K-12 and ungraded enrollment in school 01-4933.0 67.0 100% 50-9932.8 67.2 100% 100-14926.4 73.6 100% 150-19926.6 73.4 100% 200-34920.8 79.2 100% 350-49917.3 82.7 100% 500-74919.9 80.1 100% 750-99923.9 76.1 100% 1,000-1,19932.7 67.3 100% 1,200-1,49936.8 63.2 100% 1,500-1,99939.0 61.0 100% 2,000 or more44.8 55.2 100% The names of the variables used in this table are: SCHSIZE, URBANIC, T0356, SCHLEVE2 and T0055. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhafc42 Q1a. Teacher's main position at the school by Three-category school level, Teacher's race/ethnicity (collapsed) and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Administrator(%) Other(%) Total Estimates Total91.3 3.0 0.3 5.4 100% Three-category school level Elementary90.4 2.9 0.1 ! 6.6 100% Secondary93.2 3.2 0.7 3.0 100% Combined92.1 3.1 1.0 3.8 100% Teacher's race/ethnicity (collapsed) Hispanic, regardless of race93.0 1.9 0.2 ! 4.9 100% White, non-Hispanic91.0 3.2 0.3 5.5 100% Black or African American, non-Hispanic94.2 1.5 0.2 ! 4.1 100% Asian/Pacific Islander, non-Hispanic91.8 2.6 ‡ 5.5 100% American Indian/Alaska Native, non-Hispanic89.5 2.8 1.2 !! 6.5 ! 100% Collapsed total K-12 and ungraded enrollment in school 01-4983.7 7.2 2.7 ! 6.4 100% 50-9982.5 8.3 1.2 8.1 100% 100-14982.7 5.4 1.0 ! 10.9 100% 150-19986.5 4.0 0.4 ! 9.2 100% 200-34986.4 3.5 0.2 9.8 100% 350-49989.9 3.1 0.2 ! 6.9 100% 500-74991.7 2.8 0.1 ! 5.3 100% 750-99993.5 2.6 0.2 ! 3.7 100% 1,000-1,19994.0 2.7 0.5 2.8 100% 1,200-1,49994.1 2.7 0.5 2.7 100% 1,500-1,99993.9 3.2 0.5 2.4 100% 2,000 or more95.4 2.0 0.6 2.0 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: SCHSIZE, T0051, RACECOLTCH and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhafb3 Average Estimated number of full-time equivalent teachers in the school (>0) and Average Q62b(1) Base salary for the school year (>0) by Collapsed total K-12 and ungraded enrollment in school and Urbanicity of the school. Estimated number of full-time equivalent teachers in the school(Avg>0) Q62b(1). Base salary for the school year(Avg>0) Estimates Total50.8 39,312.5 Collapsed total K-12 and ungraded enrollment in school 01-496.2 31,344.3 50-999.0 32,728.9 100-14912.1 32,218.0 150-19915.7 33,488.0 200-34921.0 36,670.9 350-49929.8 38,208.1 500-74940.6 39,134.6 750-99954.6 40,588.6 1,000-1,19967.4 40,241.1 1,200-1,49981.4 41,853.8 1,500-1,999101.6 42,708.3 2,000 or more142.4 43,697.6 Urbanicity of the school Large or mid-size central city58.4 40,403.5 Urban fringe of large or mid-size city54.0 41,206.7 Small town/Rural35.5 33,801.9 The names of the variables used in this table are: NUMTCH, URBANIC, T0347 and SCHSIZE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbha484 Q28e Professional development: student assessment by Q3a Charter school identifier, Urbanicity of the school, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Yes(%) No(%) Total Estimates Total63.4 36.6 100% Q3a. Teacher's school is a public charter school Yes59.8 40.2 100% No63.4 36.6 100% Urbanicity of the school Large or mid-size central city67.0 33.0 100% Urban fringe of large or mid-size city62.5 37.5 100% Small town/Rural61.1 38.9 100% Four-category school level Elementary69.0 31.0 100% Middle school61.2 38.8 100% Secondary55.5 44.5 100% Combined60.5 39.5 100% Collapsed total K-12 and ungraded enrollment in school 01-4957.8 42.2 100% 50-9959.0 41.0 100% 100-14957.3 42.7 100% 150-19958.5 41.5 100% 200-34966.6 33.4 100% 350-49966.9 33.1 100% 500-74964.2 35.8 100% 750-99965.3 34.7 100% 1,000-1,19961.0 39.0 100% 1,200-1,49959.2 40.8 100% 1,500-1,99958.9 41.1 100% 2,000 or more55.8 44.2 100% The names of the variables used in this table are: T0171, URBANIC, SCHSIZE, SCHLEVE2 and T0055. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhac95 Q10a. Teacher currently holds a master's degree by Four-category school level and Program type of school. Yes(%) No(%) Total Estimates Total46.5 53.5 100% Four-category school level Elementary43.8 56.2 100% Middle school48.5 51.5 100% Secondary49.7 50.3 100% Combined49.3 50.7 100% Program type of school Regular elementary or secondary school46.1 53.9 100% Elementary or secondary school with a special program emphasis51.5 48.5 100% Special education school61.7 38.3 100% Vocational/technical school48.8 51.2 100% Alternative school45.9 54.1 100% The names of the variables used in this table are: PGMTYPE, T0080 and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbha8b1 Q64. Gender by 3-level affiliation code, Urbanicity of the school, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Male(%) Female(%) Total Estimates Total23.9 76.1 100% 3-level affiliation code Catholic20.3 79.7 100% Other religious24.5 75.5 100% Nonsectarian28.0 72.0 100% Urbanicity of the school Large or mid-size central city23.7 76.3 100% Urban fringe of large or mid-size city23.0 77.0 100% Small town/Rural27.8 72.2 100% Four-category school level Elementary13.1 86.9 100% Middle school30.1 69.9 100% Secondary46.3 53.7 100% Combined27.8 72.2 100% Collapsed total K-12 and ungraded enrollment in school 01-4920.7 79.3 100% 50-9922.7 77.3 100% 100-14922.5 77.5 100% 150-19920.8 79.2 100% 200-34918.8 81.2 100% 350-49920.6 79.4 100% 500-74928.1 71.9 100% 750-99938.1 61.9 100% 1,000-1,19935.4 64.6 100% 1,200-1,49937.0 63.0 100% 1,500-1,99948.7 51.3 100% 2,000 or more‡ ‡ 100% ‡ Reporting standards not met. The names of the variables used in this table are: SCHSIZE, URBANIC, T0356, SCHLEVE2 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbha962 Q1a. Teacher's main position at the school by 3-level affiliation code, Three-category school level and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Administrator(%) Other(%) Total Estimates Total81.4 13.4 2.3 2.9 100% 3-level affiliation code Catholic83.5 11.0 1.6 3.9 100% Other religious78.3 16.7 2.9 2.1 100% Nonsectarian83.2 11.8 2.3 2.6 100% Three-category school level Elementary80.8 14.8 1.4 3.0 100% Secondary83.6 10.1 3.7 2.6 100% Combined81.2 13.0 2.8 2.9 100% Collapsed total K-12 and ungraded enrollment in school 01-4974.2 19.7 4.5 1.6 ! 100% 50-9975.6 17.6 3.2 3.5 100% 100-14978.5 16.7 1.5 3.3 100% 150-19981.0 14.2 1.2 3.6 100% 200-34980.8 14.5 2.2 2.6 100% 350-49984.0 11.8 1.9 2.3 100% 500-74983.8 11.0 2.4 2.8 100% 750-99984.6 6.8 4.5 4.2 100% 1,000-1,19988.3 6.9 1.4 ! 3.4 !! 100% 1,200-1,49993.0 6.6 ‡ ‡ 100% 1,500-1,99994.6 ‡ ‡ 5.4 ! 100% 2,000 or more‡ ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: SCHSIZE, T0051, SCHLEVEL and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhada3 Average Estimated number of full-time equivalent teachers in the school (>0) and Average Q62b(1) Base salary for the school year (>0) by Collapsed total K-12 and ungraded enrollment in school and Urbanicity of the school. Estimated number of full-time equivalent teachers in the school(Avg>0) Q62b(1). Base salary for the school year(Avg>0) Estimates Total30.1 25,504.1 Collapsed total K-12 and ungraded enrollment in school 01-494.9 17,291.5 50-999.3 21,800.8 100-14913.4 22,118.2 150-19915.7 24,111.4 200-34921.5 23,906.4 350-49933.0 26,655.0 500-74946.5 28,829.8 750-99963.7 32,604.0 1,000-1,19988.0 32,907.3 1,200-1,49987.8 36,396.7 1,500-1,999107.9 38,934.5 2,000 or more‡ ‡ Urbanicity of the school Large or mid-size central city32.6 26,508.0 Urban fringe of large or mid-size city30.4 25,877.8 Small town/Rural18.6 20,045.1 ‡ Reporting standards not met. The names of the variables used in this table are: NUMTCH, URBANIC, T0347 and SCHSIZE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhafd44 Q28a. Professional development: content of the subject taught by 3-level affiliation code, Urbanicity of the school and Four-category school level. Yes(%) No(%) Total Estimates Total42.1 57.9 100% 3-level affiliation code Catholic44.1 55.9 100% Other religious37.6 62.4 100% Nonsectarian46.0 54.0 100% Urbanicity of the school Large or mid-size central city46.2 53.8 100% Urban fringe of large or mid-size city40.0 60.0 100% Small town/Rural32.8 67.2 100% Four-category school level Elementary44.4 55.6 100% Middle school52.9 47.1 100% Secondary41.6 58.4 100% Combined38.9 61.1 100% The names of the variables used in this table are: T0159, URBANIC, SCHLEVE2 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbha555 Q10a. Teacher currently holds a master's degree by 9-level school orientation and Three-category school level. Yes(%) No(%) Total Estimates Total36.5 63.5 100% 9-level school orientation Catholic, parochial28.0 72.0 100% Catholic, diocesan31.6 68.4 100% Catholic, private61.8 38.2 100% Other religious, conservative Christian28.2 71.8 100% Other religious, affiliated with an established religious group or denomination39.2 60.8 100% Other religious, not affiliated with any established religious group or denomination28.7 71.3 100% Nonsectarian, regular school43.9 56.1 100% Nonsectarian, special program45.2 54.8 100% Nonsectarian, special education42.9 57.1 100% Three-category school level Elementary28.5 71.5 100% Secondary51.4 48.6 100% Combined40.3 59.7 100% The names of the variables used in this table are: T0080, TYPOLOGY and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhaca1 Q64. Gender by Urbanicity of the school, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Male(%) Female(%) Total Estimates Total24.9 75.1 100% Urbanicity of the school Large or mid-size central city24.4 75.6 100% Urban fringe of large or mid-size city24.4 75.6 100% Small town/Rural26.9 73.1 100% Four-category school level Elementary11.8 88.2 100% Middle school28.9 71.1 100% Secondary45.2 54.8 100% Combined29.4 70.6 100% Collapsed total K-12 and ungraded enrollment in school 01-4925.1 74.9 100% 50-9927.6 72.4 100% 100-14924.4 75.6 100% 150-19924.1 75.9 100% 200-34920.3 79.7 100% 350-49917.7 82.3 100% 500-74920.4 79.6 100% 750-99924.9 75.1 100% 1,000-1,19932.8 67.2 100% 1,200-1,49936.8 63.2 100% 1,500-1,99939.3 60.7 100% 2,000 or more44.7 55.3 100% The names of the variables used in this table are: SCHSIZE, URBANIC, T0356 and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbha522 Q1a. Teacher's main position at the school by Three-category school level, Teacher's race/ethnicity (collapsed) and Collapsed total K-12 and ungraded enrollment in school. Regular full-time teacher(%) Regular part-time teacher(%) Administrator(%) Other(%) Total Estimates Total90.0 4.4 0.6 5.0 100% Three-category school level Elementary89.4 4.1 0.2 6.2 100% Secondary92.5 3.7 0.9 3.0 100% Combined84.7 9.9 2.2 3.2 100% Teacher's race/ethnicity (collapsed) Hispanic, regardless of race91.5 3.5 0.3 4.7 100% White, non-Hispanic89.7 4.6 0.6 5.1 100% Black or African American, non-Hispanic93.5 2.3 0.3 4.0 100% Asian/Pacific Islander, non-Hispanic91.0 3.8 # 5.2 100% American Indian/Alaska Native, non-Hispanic88.5 3.8 1.7 ! 5.9 ! 100% Collapsed total K-12 and ungraded enrollment in school 01-4977.6 15.2 3.8 3.3 100% 50-9978.9 13.1 2.2 5.8 100% 100-14980.6 11.2 1.3 7.0 100% 150-19984.1 8.4 0.7 6.8 100% 200-34985.0 6.3 0.7 8.0 100% 350-49989.2 4.0 0.3 6.4 100% 500-74991.3 3.3 0.3 5.2 100% 750-99992.9 2.9 0.5 3.7 100% 1,000-1,19993.7 3.0 0.6 2.8 100% 1,200-1,49994.0 2.8 0.5 2.6 100% 1,500-1,99993.9 3.1 0.5 2.5 100% 2,000 or more95.2 2.1 0.6 2.0 100% # Rounds to zero ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: SCHSIZE, T0051, RACECOLTCH and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbha4d3 Average Estimated number of full-time equivalent teachers in the school (>0) and Average Q62b(1) Base salary for the school year (>0) by Collapsed total K-12 and ungraded enrollment in school and Urbanicity of the school. Estimated number of full-time equivalent teachers in the school(Avg>0) Q62b(1). Base salary for the school year(Avg>0) Estimates Total48.2 37,523.7 Collapsed total K-12 and ungraded enrollment in school 01-495.4 22,402.3 50-999.2 27,174.3 100-14912.7 27,074.7 150-19915.7 29,475.5 200-34921.1 33,394.6 350-49930.1 36,914.8 500-74941.0 38,513.5 750-99955.2 40,024.8 1,000-1,19968.6 39,821.2 1,200-1,49981.7 41,649.2 1,500-1,999101.8 42,596.4 2,000 or more142.6 43,602.4 Urbanicity of the school Large or mid-size central city53.1 37,536.7 Urban fringe of large or mid-size city51.5 39,538.6 Small town/Rural34.3 32,834.9 The names of the variables used in this table are: NUMTCH, URBANIC, T0347 and SCHSIZE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhad94 Q28e. Professional development: student assessment by Urbanicity of the school, Four-category school level and Collapsed total K-12 and ungraded enrollment in school. Yes(%) No(%) Total Estimates Total60.0 40.0 100% Urbanicity of the school Large or mid-size central city61.1 38.9 100% Urban fringe of large or mid-size city59.8 40.2 100% Small town/Rural59.1 40.9 100% Four-category school level Elementary65.2 34.8 100% Middle school61.1 38.9 100% Secondary54.4 45.6 100% Combined42.4 57.6 100% Collapsed total K-12 and ungraded enrollment in school 01-4942.7 57.3 100% 50-9947.8 52.2 100% 100-14946.5 53.5 100% 150-19949.1 50.9 100% 200-34958.7 41.3 100% 350-49963.8 36.2 100% 500-74962.6 37.4 100% 750-99963.5 36.5 100% 1,000-1,19959.6 40.4 100% 1,200-1,49958.9 41.1 100% 1,500-1,99958.1 41.9 100% 2,000 or more55.7 44.3 100% The names of the variables used in this table are: T0171, URBANIC, SCHSIZE and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhadb5 Q10a. Teacher currently holds a master's degree by Four-category school level and Program type of school. Yes(%) No(%) Total Estimates Total45.3 54.7 100% Four-category school level Elementary42.0 58.0 100% Middle school48.4 51.6 100% Secondary49.8 50.2 100% Combined43.5 56.5 100% Program type of school Regular elementary or secondary school44.9 55.1 100% Montessori41.3 58.7 100% Elementary or secondary school with a special program emphasis50.7 49.3 100% Special education school51.7 48.3 100% Vocational/technical school48.8 51.2 100% Alternative school43.6 56.4 100% The names of the variables used in this table are: PGMTYPE, T0080 and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private Teachers Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhah371Principal's age 1, Principal's age 1 by Charter school flag, Four-category school level (primary/middle/high/combined) and Collapsed total K-12 and ungraded enrollment in school. Principal's age(Avg>0) Principal's age(Median>0) Estimates Total49.3 50.0 Charter school flag Public charter school48.3 49.0 Not a public charter school49.3 50.0 Four-category school level (primary/middle/high/combined) Elementary49.5 50.0 Middle school48.6 49.0 Secondary49.2 50.0 Combined49.3 50.0 Collapsed total K-12 and ungraded enrollment in school 01-4949.2 50.0 50-9948.9 50.0 100-14947.7 48.0 150-19948.6 50.0 200-34948.9 50.0 350-49949.1 50.0 500-74949.5 50.0 750-99950.6 51.0 1,000-1,19949.3 50.0 1,200-1,49949.7 50.0 1,500-1,99950.4 51.0 2,000 or more51.0 51.0 The names of the variables used in this table are: AGE_P, SCHSIZE, SCHLEVE2 and OP_YRS. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhf472Q25 Annual salary- before taxes and deductions 1 by Urbanicity of school and Three-category school level (elementary/secondary/combined). Q25 Annual salary- before taxes and deductions(Avg>0) Estimates Total66,504.2 Urbanicity of school Large or mid-size central city69,825.3 Urban fringe of large or mid-size city71,982.5 Small town/Rural56,005.8 Three-category school level (elementary/secondary/combined) Elementary66,001.6 Secondary68,553.6 Combined62,880.1 The names of the variables used in this table are: URBANIC, A0226 and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhfc8b3Estimated number of full-time equivalent teachers in the school 1, Total K-12 and ungraded enrollment in school 1 by Urbanicity of school, Three-category school level (elementary/secondary/combined) and Collapsed total K-12 and ungraded enrollment in school. Estimated number of full-time equivalent teachers in the school(Avg>0) Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total34.4 536.9 Urbanicity of school Large or mid-size central city39.9 637.2 Urban fringe of large or mid-size city38.3 616.0 Small town/Rural24.7 350.7 Three-category school level (elementary/secondary/combined) Elementary30.9 482.7 Secondary46.7 737.1 Combined22.8 278.8 Collapsed total K-12 and ungraded enrollment in school 01-494.0 26.9 50-998.0 74.2 100-14910.8 122.8 150-19914.2 171.6 200-34920.0 276.7 350-49928.6 424.0 500-74939.1 607.6 750-99952.5 852.2 1,000-1,19964.7 1,082.7 1,200-1,49978.4 1,327.8 1,500-1,99996.1 1,710.2 2,000 or more136.1 2,540.9 The names of the variables used in this table are: NUMTCH, ENRK12UG, URBANIC, SCHSIZE and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhfe24Q5. Years principal at other schools 0, Q5. Years principal at this school 0 by Urbanicity of school, Collapsed total K-12 and ungraded enrollment in school and Four-category school level (primary/middle/high/combined). Q5. Years principal at other schools(Avg) Q5. Years principal at this school(Avg) Estimates Total4.0 5.0 Urbanicity of school Large or mid-size central city3.8 4.5 Urban fringe of large or mid-size city4.0 5.1 Small town/Rural3.9 5.3 Collapsed total K-12 and ungraded enrollment in school 01-494.6 4.9 50-993.8 3.7 100-1493.6 5.0 150-1994.0 5.6 200-3494.1 5.6 350-4994.1 5.3 500-7493.5 4.8 750-9994.5 4.8 1,000-1,1994.0 4.6 1,200-1,4993.8 4.3 1,500-1,9994.3 4.3 2,000 or more4.0 4.4 Four-category school level (primary/middle/high/combined) Elementary4.1 5.3 Middle school3.6 4.5 Secondary3.9 4.6 Combined3.6 4.5 The names of the variables used in this table are: A0054, URBANIC, SCHSIZE, SCHLEVE2 and A0053. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhf0b5Q10. Influence on spending by Three-category school level (elementary/secondary/combined), Urbanicity of school and Collapsed total K-12 and ungraded enrollment in school. No influence(%) Some influence(%) A moderate amount of influence(%) A good deal of influence(%) A great deal of influence(%) Total Estimates Total1.6 3.7 12.9 33.3 48.5 100% Three-category school level (elementary/secondary/combined) Elementary1.5 3.4 11.9 32.8 50.5 100% Secondary1.6 4.3 15.4 34.8 43.9 100% Combined3.0 6.1 ! 16.2 32.7 42.0 100% Urbanicity of school Large or mid-size central city1.3 2.8 11.4 29.1 55.4 100% Urban fringe of large or mid-size city1.2 3.1 10.4 33.1 52.2 100% Small town/Rural2.3 5.3 17.8 36.8 37.9 100% Collapsed total K-12 and ungraded enrollment in school 01-493.7 2.1 16.7 36.6 40.9 100% 50-992.0 10.1 19.8 32.8 35.2 100% 100-1493.3 ! 6.8 19.1 35.6 35.1 100% 150-1992.0 6.3 20.3 35.7 35.7 100% 200-3491.8 4.1 15.2 36.0 42.9 100% 350-4991.2 3.3 12.4 30.9 52.1 100% 500-7491.2 3.1 11.0 32.7 52.0 100% 750-9991.4 ! 2.5 ! 7.3 33.5 55.3 100% 1,000-1,1991.5 !! 1.7 ! 7.3 33.2 56.3 100% 1,200-1,499‡ 1.4 13.7 31.8 52.7 100% 1,500-1,999‡ 2.2 7.8 30.6 57.7 100% 2,000 or more1.0 ! ‡ 10.1 28.9 58.8 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: A0125, URBANIC, SCHSIZE and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhfd81Principal's age 1, Principal's age 1 by Four-category school level (primary/middle/high/combined), Collapsed total K-12 and ungraded enrollment in school and Three-level private school typology. Principal's age(Avg>0) Principal's age(Median>0) Estimates Total49.9 50.0 Four-category school level (primary/middle/high/combined) Elementary50.5 51.0 Middle school‡ ‡ Secondary50.3 50.0 Combined48.3 48.0 Collapsed total K-12 and ungraded enrollment in school 01-4948.3 49.0 50-9948.9 48.0 100-14948.9 49.0 150-19951.3 51.0 200-34950.9 51.0 350-49951.8 52.0 500-74950.5 50.0 750-99952.1 51.0 1,000-1,19955.3 54.0 1,200-1,499‡ ‡ 1,500-1,999‡ ‡ 2,000 or more‡ ‡ Three-level private school typology Roman Catholic52.4 52.0 Other religious48.1 48.0 Nonsectarian50.1 50.0 ‡ Reporting standards not met. The names of the variables used in this table are: AGE_P, SCHSIZE, SCHLEVE2 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhf292Q22 Annual salary- before taxes and deductions 1 by Three-level private school typology, Four-category school level (primary/middle/high/combined) and Urbanicity of school. Q22 Annual salary- before taxes and deductions(Avg>0) Estimates Total43,094.4 Three-level private school typology Roman Catholic41,042.1 Other religious38,133.1 Nonsectarian56,969.3 Four-category school level (primary/middle/high/combined) Elementary39,907.9 Middle school‡ Secondary57,382.1 Combined45,018.7 Urbanicity of school Large or mid-size central city45,492.1 Urban fringe of large or mid-size city44,454.5 Small town/Rural33,914.2 ‡ Reporting standards not met. The names of the variables used in this table are: URBANIC, A0226, SCHLEVE2 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhfbe3Estimated number of full-time equivalent teachers in the school 1, Total K-12 and ungraded enrollment in school 1 by Three-level private school typology, Four-category school level (primary/middle/high/combined) and Collapsed total K-12 and ungraded enrollment in school. Estimated number of full-time equivalent teachers in the school(Avg>0) Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total15.3 202.5 Three-level private school typology Roman Catholic18.7 311.5 Other religious12.2 150.5 Nonsectarian17.5 152.1 Four-category school level (primary/middle/high/combined) Elementary12.0 184.2 Middle school‡ ‡ Secondary25.7 322.7 Combined18.8 202.4 Collapsed total K-12 and ungraded enrollment in school 01-493.9 28.2 50-997.8 71.0 100-14911.7 122.6 150-19913.6 174.0 200-34917.7 260.0 350-49929.1 419.0 500-74940.2 593.7 750-99958.3 857.6 1,000-1,19976.1 1,081.1 1,200-1,499‡ ‡ 1,500-1,999‡ ‡ 2,000 or more‡ ‡ ‡ Reporting standards not met. The names of the variables used in this table are: NUMTCH, ENRK12UG, SCHSIZE, SCHLEVE2 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhf304Q5. Years principal at other schools 0, Q5. Years principal at this school 0 by Three-level private school typology, Four-category school level (primary/middle/high/combined) and Urbanicity of school. Q5. Years principal at other schools(Avg) Q5. Years principal at this school(Avg) Estimates Total3.9 6.3 Three-level private school typology Roman Catholic5.3 5.6 Other religious3.4 6.0 Nonsectarian2.9 8.0 Four-category school level (primary/middle/high/combined) Elementary4.2 6.1 Middle school‡ ‡ Secondary3.2 6.6 Combined3.5 6.6 Urbanicity of school Large or mid-size central city3.9 6.3 Urban fringe of large or mid-size city4.0 6.7 Small town/Rural3.7 5.6 ‡ Reporting standards not met. The names of the variables used in this table are: A0054, URBANIC, SCHLEVE2, A0053 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhf4f5Q10. Influence on spending by Three-category school level (elementary/secondary/combined), Three-level private school typology and Collapsed total K-12 and ungraded enrollment in school. No influence(%) Some influence(%) A moderate amount of influence(%) A good deal of influence(%) A great deal of influence(%) Total Estimates Total1.8 1.6 7.8 23.4 65.4 100% Three-category school level (elementary/secondary/combined) Elementary1.1 1.4 6.7 24.5 66.3 100% Secondary3.3 ‡ 8.6 24.5 63.2 100% Combined2.9 2.5 9.7 20.7 64.2 100% Three-level private school typology Roman Catholic0.6 ! 0.8 ! 5.5 22.0 71.2 100% Other religious2.9 2.6 9.0 26.4 59.2 100% Nonsectarian1.3 ! 0.7 ! 8.5 18.6 71.0 100% Collapsed total K-12 and ungraded enrollment in school 01-493.0 ! 2.5 ! 9.8 22.0 62.8 100% 50-994.1 ‡ 11.7 25.4 57.6 100% 100-149‡ 0.6 ! 6.6 21.5 70.9 100% 150-199‡ 3.2 !! 5.6 30.5 60.0 100% 200-3490.9 ! 0.8 6.0 21.0 71.2 100% 350-499‡ 0.8 !! 3.7 20.6 74.6 100% 500-749‡ ‡ 7.9 25.7 62.0 100% 750-999‡ ‡ 3.9 23.1 72.6 100% 1,000-1,199‡ ‡ 13.1 ! 20.2 61.8 100% 1,200-1,499‡ ‡ ‡ ‡ ‡ 100% 1,500-1,999‡ ‡ ‡ ‡ ‡ 100% 2,000 or more‡ ‡ ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: A0125, RELIG, SCHSIZE and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhf971Principal's age 1, Principal's age 1 by Four-category school level (primary/middle/high/combined) and Collapsed total K-12 and ungraded enrollment in school. Principal's age(Avg>0) Principal's age(Median>0) Estimates Total49.4 50.0 Four-category school level (primary/middle/high/combined) Elementary49.8 50.0 Middle school48.7 50.0 Secondary49.3 50.0 Combined48.6 49.0 Collapsed total K-12 and ungraded enrollment in school 01-4948.6 50.0 50-9948.9 49.0 100-14948.3 49.0 150-19949.7 50.0 200-34949.5 50.0 350-49949.4 50.0 500-74949.6 50.0 750-99950.7 51.0 1,000-1,19949.6 50.0 1,200-1,49949.8 50.0 1,500-1,99950.5 51.0 2,000 or more51.1 51.0 The names of the variables used in this table are: AGE_P, SCHSIZE and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhf702Q25/Q22 Annual salary- before taxes and deductions 1 by Four-category school level (primary/middle/high/combined) and Urbanicity of school. Q25/Q22 Annual salary- before taxes and deductions(Avg>0) Estimates Total61,065.4 Four-category school level (primary/middle/high/combined) Elementary59,745.5 Middle school66,424.6 Secondary67,494.6 Combined50,553.1 Urbanicity of school Large or mid-size central city61,208.7 Urban fringe of large or mid-size city66,215.3 Small town/Rural52,837.2 The names of the variables used in this table are: URBANIC, A0226 and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhf863Estimated number of full-time equivalent teachers in the school 1, Total K-12 and ungraded enrollment in school 1 by Urbanicity of school, Three-category school level (elementary/secondary/combined) and Collapsed total K-12 and ungraded enrollment in school. Estimated number of full-time equivalent teachers in the school(Avg>0) Total K-12 and ungraded enrollment in school(Avg>0) Estimates Total30.0 460.1 Urbanicity of school Large or mid-size central city32.0 497.6 Urban fringe of large or mid-size city33.7 531.8 Small town/Rural22.5 314.3 Three-category school level (elementary/secondary/combined) Elementary27.0 422.3 Secondary44.3 690.8 Combined20.1 226.5 Collapsed total K-12 and ungraded enrollment in school 01-493.9 27.8 50-997.9 72.4 100-14911.2 122.7 150-19914.0 172.5 200-34919.4 272.3 350-49928.7 423.5 500-74939.1 606.8 750-99952.9 852.5 1,000-1,19965.3 1,082.6 1,200-1,49978.5 1,327.1 1,500-1,99996.5 1,707.6 2,000 or more136.4 2,542.4 The names of the variables used in this table are: NUMTCH, ENRK12UG, URBANIC, SCHSIZE and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhfee4Q5. Years principal at other schools 0, Q5. Years principal at this school 0 by Urbanicity of school, Collapsed total K-12 and ungraded enrollment in school and Four-category school level (primary/middle/high/combined). Q5. Years principal at other schools(Avg) Q5. Years principal at this school(Avg) Estimates Total3.9 5.3 Urbanicity of school Large or mid-size central city3.8 5.1 Urban fringe of large or mid-size city4.0 5.4 Small town/Rural3.9 5.3 Collapsed total K-12 and ungraded enrollment in school 01-493.1 5.7 50-993.4 5.4 100-1493.6 5.6 150-1994.4 5.8 200-3494.3 5.6 350-4994.2 5.4 500-7493.5 5.0 750-9994.6 5.0 1,000-1,1994.1 4.8 1,200-1,4993.8 4.4 1,500-1,9994.4 4.3 2,000 or more4.0 4.6 Four-category school level (primary/middle/high/combined) Elementary4.1 5.5 Middle school3.6 4.6 Secondary3.8 4.9 Combined3.5 6.0 The names of the variables used in this table are: A0054, URBANIC, SCHSIZE, SCHLEVE2 and A0053. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhf6e5Q10. Influence on spending by Three-category school level (elementary/secondary/combined), Urbanicity of school and Collapsed total K-12 and ungraded enrollment in school. No influence(%) Some influence(%) A moderate amount of influence(%) A good deal of influence(%) A great deal of influence(%) Total Estimates Total1.6 3.2 11.7 30.9 52.5 100% Three-category school level (elementary/secondary/combined) Elementary1.4 2.9 10.8 31.1 53.8 100% Secondary1.8 3.8 14.7 33.6 46.1 100% Combined3.0 3.6 11.6 24.2 57.7 100% Urbanicity of school Large or mid-size central city1.3 2.4 10.2 26.8 59.3 100% Urban fringe of large or mid-size city1.3 2.7 9.6 30.6 55.8 100% Small town/Rural2.5 4.8 16.6 35.6 40.6 100% Collapsed total K-12 and ungraded enrollment in school 01-493.2 2.3 11.9 26.5 56.0 100% 50-993.2 5.1 15.2 28.6 47.9 100% 100-1492.0 4.0 13.6 29.3 51.1 100% 150-1991.5 5.1 14.5 33.7 45.2 100% 200-3491.6 3.2 12.7 31.9 50.6 100% 350-4991.1 3.1 11.5 29.8 54.6 100% 500-7491.2 3.1 10.8 32.3 52.6 100% 750-9991.3 ! 2.3 ! 7.1 32.8 56.4 100% 1,000-1,1991.4 !! 1.9 ! 7.6 32.5 56.6 100% 1,200-1,499‡ 1.3 13.4 31.6 53.3 100% 1,500-1,999‡ 2.2 7.5 31.5 57.0 100% 2,000 or more1.0 ! ‡ 9.9 28.6 59.3 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: A0125, URBANIC, SCHSIZE and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 1999-00.Computation by NCES PowerStats on 4/13/2017.bddbhfn9e1Principal's age 1, Principal's age 1 by Charter school identifier, Four-category school level (primary/middle/high/combined), Collapsed total K-12 and ungraded enrollment in school and Percentage of enrolled students approved for the NSLP at school. Principal's age(Avg>0) Principal's age(Median>0) Estimates Total49.3 51.0 Charter school identifier School is a public charter school48.3 49.0 School is not a public charter school49.4 51.0 Four-category school level (primary/middle/high/combined) Primary49.5 51.0 Middle48.6 50.0 High49.9 51.0 Combined48.2 49.0 Collapsed total K-12 and ungraded enrollment in school 1-4950.0 52.0 50-9950.4 52.0 100-14949.0 50.0 150-19950.3 52.0 200-34948.2 50.0 350-49949.5 51.0 500-74949.2 51.0 750-99949.8 50.0 1,000-1,19950.1 51.0 1,200-1,49950.3 52.0 1,500-1,99949.7 51.0 2,000 or more51.3 53.0 Percentage of enrolled students approved for the NSLP at school 0% to 25%49.4 51.0 26% to 50%48.7 50.0 51% to 75%49.7 51.0 More than 75%50.1 52.0 The names of the variables used in this table are: NSLAPP_S, AGE_P, SCHSIZE, SCHLEVE2 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhgm902Q45 Annual salary- before taxes and deductions 1 by Urbanicity of the school, Four-category school level (primary/middle/high/combined) and Percentage of enrolled students approved for the NSLP at school. Q45 Annual salary- before taxes and deductions(Avg>0) Estimates Total75,500.8 Urbanicity of the school Large or mid-size central city80,227.4 Urban fringe of a large or mid-size central city79,682.0 Small town/rural63,695.5 Four-category school level (primary/middle/high/combined) Primary74,906.6 Middle77,883.9 High79,375.3 Combined64,691.0 Percentage of enrolled students approved for the NSLP at school 0% to 25%81,781.2 26% to 50%72,978.1 51% to 75%72,286.2 More than 75%75,343.4 The names of the variables used in this table are: NSLAPP_S, SCHLEVE2, A0263 and URBANS03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhgpb4e3Q10 Total number of hours spent on all school activities every week 1, Q11 Total number of hours spent on student interaction every week 1 by Charter school identifier, Four-category school level (primary/middle/high/combined) and Collapsed total K-12 and ungraded enrollment in school. Q10 Total number of hours spent on all school activities every week(Avg>0) Q11 Total number of hours spent on student interaction every week(Avg>0) Estimates Total59.0 22.8 Charter school identifier School is a public charter school59.6 23.3 School is not a public charter school59.0 22.8 Four-category school level (primary/middle/high/combined) Primary58.3 21.6 Middle60.0 23.1 High60.8 25.1 Combined57.0 25.9 Collapsed total K-12 and ungraded enrollment in school 1-4944.4 20.0 50-9954.4 24.9 100-14956.0 23.2 150-19956.6 24.4 200-34959.0 23.4 350-49959.3 22.4 500-74959.7 22.1 750-99961.4 22.6 1,000-1,19962.9 24.4 1,200-1,49963.7 23.8 1,500-1,99965.4 23.7 2,000 or more63.7 23.0 The names of the variables used in this table are: A0041, A0040, SCHSIZE, SCHLEVE2 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhg704Q1. Years principal at this or any school 0, Q2. Years principal at this school 0 by Charter school identifier, Urbanicity of the school, Four-category school level (primary/middle/high/combined) and Percentage of enrolled students approved for the NSLP at school. Q1. Years principal at this or any school(Avg) Q2. Years principal at this school(Avg) Estimates Total7.8 4.3 Charter school identifier School is a public charter school6.1 2.6 School is not a public charter school7.8 4.4 Urbanicity of the school Large or mid-size central city7.4 4.0 Urban fringe of a large or mid-size central city7.9 4.4 Small town/rural7.9 4.6 Four-category school level (primary/middle/high/combined) Primary8.1 4.5 Middle6.8 3.7 High7.5 4.3 Combined7.4 4.5 Percentage of enrolled students approved for the NSLP at school 0% to 25%8.5 4.7 26% to 50%7.7 4.3 51% to 75%7.6 4.3 More than 75%6.8 3.9 The names of the variables used in this table are: URBANS03, CHARFLAG, NSLAPP_S, SCHLEVE2, A0026 and A0025. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhga55Q15g. Influence on spending - principal by Four-category school level (primary/middle/high/combined), Urbanicity of the school and Collapsed total K-12 and ungraded enrollment in school. No influence(%) Minor influence(%) Moderate influence(%) Major influence(%) Total Estimates Total0.8 6.3 23.7 69.2 100% Four-category school level (primary/middle/high/combined) Primary0.6 5.5 21.8 72.1 100% Middle0.5 !! 4.9 22.0 72.7 100% High0.9 7.6 27.2 64.3 100% Combined2.2 ! 12.6 32.1 53.1 100% Urbanicity of the school Large or mid-size central city0.7 5.8 17.0 76.5 100% Urban fringe of a large or mid-size central city0.6 5.1 23.1 71.2 100% Small town/rural1.3 9.0 30.8 58.8 100% Collapsed total K-12 and ungraded enrollment in school 1-49‡ 7.0 ! 32.1 59.3 100% 50-991.7 !! 6.2 24.6 67.5 100% 100-1491.5 !! 11.0 27.1 60.4 100% 150-1991.1 ! 8.2 30.1 60.6 100% 200-3490.9 ! 6.4 23.8 68.9 100% 350-4990.9 7.0 26.1 66.0 100% 500-7490.6 ! 5.5 20.6 73.3 100% 750-999‡ 5.7 22.2 71.9 100% 1,000-1,199‡ 5.4 17.9 76.6 100% 1,200-1,499‡ 3.9 19.6 75.3 100% 1,500-1,999‡ 4.5 18.7 76.8 100% 2,000 or more‡ 2.6 20.2 77.2 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: A0105, SCHSIZE, SCHLEVE2 and URBANS03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhg1e1Principal's age 1, Principal's age 1 by Four-category school level (primary/middle/high/combined), Collapsed total K-12 and ungraded enrollment in school and Percentage of enrolled students approved for the NSLP at school. Principal's age(Avg>0) Principal's age(Median>0) Estimates Total50.5 52.0 Four-category school level (primary/middle/high/combined) Primary50.7 52.0 Middle‡ ‡ High51.8 54.0 Combined50.0 51.0 Collapsed total K-12 and ungraded enrollment in school 1-4947.7 48.0 50-9949.5 51.0 100-14951.3 53.0 150-19951.5 52.0 200-34953.0 54.0 350-49952.6 53.0 500-74953.7 55.0 750-99951.9 53.0 1,000-1,199‡ ‡ 1,200-1,499‡ ‡ 1,500-1,999‡ ‡ 2,000 or more‡ ‡ Percentage of enrolled students approved for the NSLP at school 0% to 25%51.3 52.0 26% to 50%54.2 54.0 51% to 75%50.2 51.0 More than 75%49.0 50.0 ‡ Reporting standards not met. The names of the variables used in this table are: NSLAPP_S, AGE_P, SCHSIZE and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhg1c2Q39 Annual salary- before taxes and deductions 1 by Three-level private school typology, Urbanicity of the school and Three-category school level (elementary/secondary/combined). Q39 Annual salary- before taxes and deductions(Avg>0) Estimates Total50,182.0 Three-level private school typology Roman Catholic49,228.1 Other religious41,938.2 Nonsectarian66,523.3 Urbanicity of the school Large or mid-size central city53,738.3 Urban fringe of a large or mid-size central city51,816.0 Small town/rural35,384.5 Three-category school level (elementary/secondary/combined) Elementary47,249.3 Secondary65,006.8 Combined51,576.6 The names of the variables used in this table are: SCHLEVEL, URBANS03, A0263 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhga13Q9 Total number of hours spent on all school activities every week 1, Q10 Total number of hours spent on student interaction every week 1 by Four-category school level (primary/middle/high/combined) and Collapsed total K-12 and ungraded enrollment in school. Q9 Total number of hours spent on all school activities every week(Avg>0) Q10 Total number of hours spent on student interaction every week(Avg>0) Estimates Total54.6 22.2 Four-category school level (primary/middle/high/combined) Primary54.8 22.3 Middle‡ ‡ High60.2 22.4 Combined52.5 22.0 Collapsed total K-12 and ungraded enrollment in school 1-4946.3 26.3 50-9954.4 24.2 100-14957.2 20.2 150-19957.8 19.4 200-34959.1 19.6 350-49959.1 18.7 500-74960.9 19.4 750-99964.4 18.3 1,000-1,199‡ ‡ 1,200-1,499‡ ‡ 1,500-1,999‡ ‡ 2,000 or more‡ ‡ ‡ Reporting standards not met. The names of the variables used in this table are: A0041, A0040, SCHSIZE and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhgf404Q1. Years principal at this or any school 0, Q2. Years principal at this school 0 by Three-level private school typology, Four-category school level (primary/middle/high/combined) and Percentage of enrolled students approved for the NSLP at school. Q1. Years principal at this or any school(Avg) Q2. Years principal at this school(Avg) Estimates Total10.0 6.5 Three-level private school typology Roman Catholic10.5 5.8 Other religious9.5 6.3 Nonsectarian10.5 7.6 Four-category school level (primary/middle/high/combined) Primary9.9 6.1 Middle‡ ‡ High9.3 5.4 Combined10.4 7.5 Percentage of enrolled students approved for the NSLP at school 0% to 25%10.1 5.7 26% to 50%11.1 5.3 51% to 75%9.0 5.8 More than 75%8.8 6.2 ‡ Reporting standards not met. The names of the variables used in this table are: A0026, A0025, SCHLEVE2, NSLAPP_S and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhgd75Q14g. Influence on spending - principal by Three-level private school typology, Three-category school level (elementary/secondary/combined), Urbanicity of the school and Collapsed total K-12 and ungraded enrollment in school. No influence(%) Minor influence(%) Moderate influence(%) Major influence(%) Total Estimates Total1.5 3.3 12.9 82.3 100% Three-level private school typology Roman Catholic0.6 !! 2.7 9.6 87.1 100% Other religious1.7 ! 3.7 16.5 78.1 100% Nonsectarian2.2 ! 3.4 ! 9.8 84.6 100% Three-category school level (elementary/secondary/combined) Elementary1.7 3.7 12.4 82.2 100% Secondary‡ 3.2 ! 12.4 83.6 100% Combined1.3 ! 2.6 14.1 82.0 100% Urbanicity of the school Large or mid-size central city0.4 !! 3.3 12.9 83.4 100% Urban fringe of a large or mid-size central city1.9 2.6 11.9 83.5 100% Small town/rural2.6 ! 5.9 15.9 75.6 100% Collapsed total K-12 and ungraded enrollment in school 1-493.3 4.4 18.4 73.9 100% 50-992.1 ! 4.2 13.0 80.7 100% 100-149‡ 4.1 12.2 83.5 100% 150-199‡ 2.6 ! 11.8 85.6 100% 200-3490.8 !! 2.1 ! 9.6 87.5 100% 350-499‡ 1.9 ! 7.1 91.0 100% 500-749‡ ‡ 10.3 89.3 100% 750-999‡ ‡ 5.0 ! 92.5 100% 1,000-1,199‡ ‡ ‡ ‡ 100% 1,200-1,499‡ ‡ ‡ ‡ 100% 1,500-1,999‡ ‡ ‡ ‡ 100% 2,000 or more‡ ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: A0105, SCHSIZE, URBANS03, SCHLEVEL and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhgf941Principal's age 1, Principal's age 1 by Four-category school level (primary/middle/high/combined), Collapsed total K-12 and ungraded enrollment in school and Percentage of enrolled students approved for the NSLP at school. Principal's age(Avg>0) Principal's age(Avg>0) Estimates Total49.6 49.6 Four-category school level (primary/middle/high/combined) Primary49.8 49.8 Middle48.6 48.6 High50.1 50.1 Combined49.2 49.2 Collapsed total K-12 and ungraded enrollment in school 1-4948.4 48.4 50-9949.9 49.9 100-14950.1 50.1 150-19950.8 50.8 200-34949.2 49.2 350-49949.8 49.8 500-74949.5 49.5 750-99949.9 49.9 1,000-1,19950.2 50.2 1,200-1,49950.4 50.4 1,500-1,99949.6 49.6 2,000 or more51.2 51.2 Percentage of enrolled students approved for the NSLP at school 0% to 25%49.7 49.7 26% to 50%48.8 48.8 51% to 75%49.7 49.7 More than 75%50.0 50.0 The names of the variables used in this table are: NSLAPP_S, AGE_P, SCHSIZE and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhgb6e2Q45/Q39 Annual salary- before taxes and deductions 1 by Urbanicity of the school, Four-category school level (primary/middle/high/combined) and Percentage of enrolled students approved for the NSLP at school. Q45/Q39 Annual salary- before taxes and deductions(Avg>0) Estimates Total69,620.0 Urbanicity of the school Large or mid-size central city72,188.6 Urban fringe of a large or mid-size central city73,016.5 Small town/rural59,822.7 Four-category school level (primary/middle/high/combined) Primary68,244.0 Middle77,556.8 High77,647.9 Combined57,484.8 Percentage of enrolled students approved for the NSLP at school 0% to 25%76,311.1 26% to 50%72,210.1 51% to 75%71,516.5 More than 75%74,300.9 The names of the variables used in this table are: NSLAPP_S, SCHLEVE2, A0263 and URBANS03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhg143Q10/Q9 Total number of hours spent on all school activities every week 1, Q11/Q10 Total number of hours spent on student interaction every week 1 by Four-category school level (primary/middle/high/combined) and Collapsed total K-12 and ungraded enrollment in school. Q10/Q9 Total number of hours spent on all school activities every week(Avg>0) Q11/Q10 Total number of hours spent on student interaction every week(Avg>0) Estimates Total57.9 22.7 Four-category school level (primary/middle/high/combined) Primary57.5 21.7 Middle60.0 23.1 High60.8 24.8 Combined54.4 23.7 Collapsed total K-12 and ungraded enrollment in school 1-4945.8 24.5 50-9954.4 24.5 100-14956.6 21.8 150-19957.1 22.4 200-34959.0 22.6 350-49959.3 22.0 500-74959.7 21.9 750-99961.6 22.4 1,000-1,19963.0 24.3 1,200-1,49963.7 23.6 1,500-1,99965.3 23.6 2,000 or more63.7 23.0 The names of the variables used in this table are: A0041, A0040, SCHSIZE and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhg444Q1. Years principal at this or any school 0, Q2. Years principal at this school 0 by Four-category school level (primary/middle/high/combined), Urbanicity of the school and Percentage of enrolled students approved for the NSLP at school. Q1. Years principal at this or any school(Avg) Q2. Years principal at this school(Avg) Estimates Total8.3 4.8 Four-category school level (primary/middle/high/combined) Primary8.6 4.9 Middle6.8 3.7 High7.7 4.4 Combined9.1 6.2 Urbanicity of the school Large or mid-size central city8.2 4.8 Urban fringe of a large or mid-size central city8.6 5.0 Small town/rural7.9 4.6 Percentage of enrolled students approved for the NSLP at school 0% to 25%8.7 4.8 26% to 50%7.8 4.4 51% to 75%7.7 4.4 More than 75%7.0 4.0 The names of the variables used in this table are: A0026, A0025, SCHLEVE2, NSLAPP_S and URBANS03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhgcbd5Q15g.(4) / Q14g.(2) Influence on spending - principal by Four-category school level (primary/middle/high/combined), Urbanicity of the school and Collapsed total K-12 and ungraded enrollment in school. No influence(%) Minor influence(%) Moderate influence(%) Major influence(%) Total Estimates Total1.0 5.6 21.1 72.3 100% Four-category school level (primary/middle/high/combined) Primary0.9 5.1 19.5 74.5 100% Middle0.5 !! 4.8 21.6 73.1 100% High0.9 7.1 25.4 66.6 100% Combined1.7 6.9 21.8 69.6 100% Urbanicity of the school Large or mid-size central city0.6 5.0 15.8 78.6 100% Urban fringe of a large or mid-size central city0.9 4.5 20.4 74.3 100% Small town/rural1.5 8.6 28.5 61.4 100% Collapsed total K-12 and ungraded enrollment in school 1-492.8 5.2 22.4 69.6 100% 50-992.0 ! 5.0 17.5 75.5 100% 100-1490.9 !! 7.8 20.1 71.2 100% 150-1990.7 ! 5.9 22.6 70.8 100% 200-3490.9 ! 5.5 20.7 72.9 100% 350-4990.8 6.5 24.3 68.4 100% 500-7490.6 ! 5.2 20.0 74.2 100% 750-999‡ 5.5 21.2 73.1 100% 1,000-1,199‡ 5.3 17.3 77.1 100% 1,200-1,499‡ 3.7 19.2 75.8 100% 1,500-1,999‡ 4.5 18.3 77.3 100% 2,000 or more‡ 2.8 20.2 77.0 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: A0105, SCHSIZE, SCHLEVE2 and URBANS03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 2003-04.Computation by NCES PowerStats on 4/13/2017.bddbhg8f1Principal's age 1, Principal's age 1 by Charter school identifier, Four-category school level (primary/middle/high/combined), Collapsed total K-12 and ungraded enrollment in school and Percentage of enrolled students approved for the NSLP at school. Principal's age(Avg>0) Principal's age(Median>0) Estimates Total48.8 50.0 Charter school identifier School is a public charter school48.7 48.0 School is not a public charter school48.8 50.0 Four-category school level (primary/middle/high/combined) Primary49.1 51.0 Middle47.4 48.0 High48.8 50.0 Combined48.6 49.0 Collapsed total K-12 and ungraded enrollment in school 1-4950.9 52.0 50-9949.8 51.0 100-14949.6 50.0 150-19949.4 51.0 200-34947.8 49.0 350-49948.3 49.0 500-74948.9 50.0 750-99948.7 49.0 1,000-1,19949.0 49.0 1,200-1,49950.1 52.0 1,500-1,99948.8 49.0 2,000 or more50.0 52.0 Percentage of enrolled students approved for the NSLP at school 0% to 25%48.7 50.0 26% to 50%48.2 49.0 51% to 75%49.0 51.0 More than 75%48.5 49.0 The names of the variables used in this table are: NSLAPP_S, AGE_P, SCHSIZE, SCHLEVE2 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhh592Q43 Annual salary- before taxes and deductions 1 by Charter school identifier, Collapsed urban-centric school locale code, Three-category school level (elementary/secondary/combined) and Percentage of enrolled students approved for the NSLP at school. Q43 Annual salary- before taxes and deductions(Avg>0) Estimates Total85,745.9 Charter school identifier School is a public charter school77,892.2 School is not a public charter school86,066.4 Collapsed urban-centric school locale code City91,196.6 Suburb96,921.4 Town77,524.4 Rural75,704.2 Three-category school level (elementary/secondary/combined) Elementary85,238.3 Secondary90,259.9 Combined75,766.1 Percentage of enrolled students approved for the NSLP at school 0% to 25%93,171.4 26% to 50%82,881.9 51% to 75%80,909.5 More than 75%85,108.7 The names of the variables used in this table are: URBANS12, A0249, NSLAPP_S, SCHLEVEL and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhh533Q34 Total number of hours spent on all school activities every week 1, Q35 Total number of hours spent on student interaction every week 1 by Charter school identifier, Three-category school level (elementary/secondary/combined) and Collapsed total K-12 and ungraded enrollment in school. Q34 Total number of hours spent on all school activities every week(Avg>0) Q35 Total number of hours spent on student interaction every week(Avg>0) Estimates Total58.4 20.9 Charter school identifier School is a public charter school58.2 21.6 School is not a public charter school58.5 20.8 Three-category school level (elementary/secondary/combined) Elementary58.4 20.0 Secondary59.2 22.8 Combined56.7 23.2 Collapsed total K-12 and ungraded enrollment in school 1-4947.0 19.6 50-9952.9 21.4 100-14955.7 20.1 150-19956.8 22.1 200-34958.3 21.8 350-49958.8 20.8 500-74959.4 19.9 750-99959.8 20.7 1,000-1,19960.9 21.4 1,200-1,49963.6 21.0 1,500-1,99962.9 22.0 2,000 or more65.2 22.1 The names of the variables used in this table are: CHARFLAG, A0226, SCHSIZE, SCHLEVEL and A0225. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhhc44Q1. Years principal at this or any school 0, Q2. Years principal at this school 0 by Charter school identifier, Collapsed urban-centric school locale code, Four-category school level (primary/middle/high/combined) and Percentage of enrolled students approved for the NSLP at school. Q1. Years principal at this or any school(Avg) Q2. Years principal at this school(Avg) Estimates Total7.5 4.2 Charter school identifier School is a public charter school6.6 3.5 School is not a public charter school7.5 4.3 Collapsed urban-centric school locale code City7.0 3.7 Suburb7.7 4.4 Town7.8 4.6 Rural7.5 4.3 Four-category school level (primary/middle/high/combined) Primary7.9 4.4 Middle6.6 3.9 High7.2 4.1 Combined7.1 3.8 Percentage of enrolled students approved for the NSLP at school 0% to 25%8.2 4.5 26% to 50%7.5 4.4 51% to 75%7.1 4.0 More than 75%6.8 3.7 The names of the variables used in this table are: URBANS12, CHARFLAG, NSLAPP_S, SCHLEVE2, A0026 and A0025. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhh855Q12G. Influence on spending - principal by Three-category school level (elementary/secondary/combined), Collapsed urban-centric school locale code and Collapsed total K-12 and ungraded enrollment in school. No Influence(%) Minor Influence(%) Moderate Influence(%) Major Influence(%) Total Estimates Total0.6 4.2 21.0 74.2 100% Three-category school level (elementary/secondary/combined) Elementary0.6 ! 3.0 19.9 76.5 100% Secondary0.6 ! 6.2 21.2 72.0 100% Combined0.9 !! 9.4 30.5 59.2 100% Collapsed urban-centric school locale code City0.4 ! 3.0 17.5 79.0 100% Suburb0.8 !! 3.2 17.3 78.7 100% Town0.7 !! 4.4 23.9 71.0 100% Rural0.6 !! 5.8 25.4 68.2 100% Collapsed total K-12 and ungraded enrollment in school 1-491.2 !! 5.7 ! 16.7 76.4 100% 50-99‡ 5.0 25.3 69.7 100% 100-149‡ 4.5 ! 28.3 65.3 100% 150-1990.5 !! 3.7 ! 29.6 66.2 100% 200-3491.1 !! 5.6 20.1 73.1 100% 350-4990.7 !! 4.4 20.4 74.5 100% 500-7490.3 !! 3.6 20.5 75.7 100% 750-999‡ 1.9 ! 20.2 78.0 100% 1,000-1,199‡ 4.3 ! 16.8 78.5 100% 1,200-1,499‡ 4.6 ! 22.1 73.3 100% 1,500-1,999‡ 4.1 ! 16.6 78.8 100% 2,000 or more1.0 !! 2.0 ! 15.2 81.8 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: A0089, SCHSIZE, URBANS12 and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhhnc71Principal's age 1, Principal's age 1 by Four-category school level (primary/middle/high/combined) and Percentage of enrolled students approved for the NSLP at school. Principal's age(Avg>0) Principal's age(Median>0) Estimates Total51.0 53.0 Four-category school level (primary/middle/high/combined) Primary51.4 53.0 Middle‡ ‡ High51.3 53.0 Combined50.2 52.0 Percentage of enrolled students approved for the NSLP at school 0% to 25%52.1 54.0 26% to 50%50.6 52.0 51% to 75%48.6 49.0 More than 75%51.5 54.0 ‡ Reporting standards not met. The names of the variables used in this table are: NSLAPP_S, AGE_P and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhhaf2Q40 Annual salary- before taxes and deductions 1 by Three-level private school typology, Collapsed urban-centric school locale code and Three-category school level (elementary/secondary/combined). Q40 Annual salary- before taxes and deductions(Avg>0) Estimates Total57,478.2 Three-level private school typology Catholic58,078.7 Other religious47,764.5 Nonsectarian75,464.1 Collapsed urban-centric school locale code City64,928.9 Suburb61,581.9 Town44,268.8 Rural42,857.4 Three-category school level (elementary/secondary/combined) Elementary55,451.9 Secondary75,102.2 Combined55,051.3 The names of the variables used in this table are: URBANS12, A0249, SCHLEVEL and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhhpb23Q32 Total number of hours spent on all school activities every week 1, Q33 Total number of hours spent on student interaction every week 1 by Three-level private school typology, Four-category school level (primary/middle/high/combined) and Collapsed total K-12 and ungraded enrollment in school. Q32 Total number of hours spent on all school activities every week(Avg>0) Q33 Total number of hours spent on student interaction every week(Avg>0) Estimates Total53.8 19.7 Three-level private school typology Catholic57.7 17.8 Other religious50.6 20.5 Nonsectarian55.9 20.4 Four-category school level (primary/middle/high/combined) Primary53.1 19.0 Middle‡ ‡ High57.2 20.4 Combined53.7 20.6 Collapsed total K-12 and ungraded enrollment in school 1-4948.2 24.8 50-9952.9 20.2 100-14955.8 18.4 150-19956.4 18.1 200-34957.2 16.4 350-49957.6 16.4 500-74959.1 15.4 750-99961.2 15.4 1,000-1,199‡ ‡ 1,200-1,499‡ ‡ 1,500-1,999‡ ‡ 2,000 or more‡ ‡ ‡ Reporting standards not met. The names of the variables used in this table are: RELIG, A0226, SCHSIZE, SCHLEVE2 and A0225. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhh344Q1. Years principal at this or any school 0, Q2. Years principal at this school 0 by Collapsed urban-centric school locale code, Four-category school level (primary/middle/high/combined) and Collapsed total K-12 and ungraded enrollment in school. Q1. Years principal at this or any school(Avg) Q2. Years principal at this school(Avg) Estimates Total10.0 6.8 Collapsed urban-centric school locale code City10.8 7.5 Suburb10.5 7.1 Town9.4 6.1 Rural8.1 5.7 Four-category school level (primary/middle/high/combined) Primary10.0 6.4 Middle‡ ‡ High9.9 7.2 Combined10.2 7.5 Collapsed total K-12 and ungraded enrollment in school 1-498.0 6.4 50-999.4 6.6 100-1499.4 6.2 150-19911.1 6.7 200-34911.8 7.3 350-49912.6 7.8 500-74912.9 8.5 750-99911.4 8.8 1,000-1,199‡ ‡ 1,200-1,499‡ ‡ 1,500-1,999‡ ‡ 2,000 or more‡ ‡ ‡ Reporting standards not met. The names of the variables used in this table are: A0026, A0025, URBANS12, SCHLEVE2 and SCHSIZE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhhfa5Q12G. Influence on spending - principal by Three-category school level (elementary/secondary/combined), Collapsed urban-centric school locale code and Collapsed total K-12 and ungraded enrollment in school. No Influence(%) Minor Influence(%) Moderate Influence(%) Major Influence(%) Total Estimates Total1.5 3.0 12.9 82.6 100% Three-category school level (elementary/secondary/combined) Elementary0.8 ! 2.6 12.2 84.5 100% Secondary1.8 !! 3.5 ! 14.1 80.6 100% Combined2.7 ! 3.5 13.9 79.9 100% Collapsed urban-centric school locale code City0.5 !! 4.0 13.0 82.6 100% Suburb0.7 !! 1.6 ! 11.3 86.4 100% Town‡ 3.3 !! 12.1 80.7 100% Rural3.2 ! 3.4 ! 15.8 77.6 100% Collapsed total K-12 and ungraded enrollment in school 1-492.7 ! 1.8 ! 14.5 81.0 100% 50-99‡ 7.4 14.0 77.6 100% 100-149‡ 2.4 !! 16.8 80.4 100% 150-199‡ 3.0 ! 13.4 82.9 100% 200-3491.3 !! 2.4 ! 7.9 88.5 100% 350-499‡ ‡ 10.3 86.1 100% 500-749‡ ‡ 9.0 89.8 100% 750-999‡ ‡ 9.5 ! 88.3 100% 1,000-1,199‡ ‡ ‡ ‡ 100% 1,200-1,499‡ ‡ ‡ ‡ 100% 1,500-1,999‡ ‡ ‡ ‡ 100% 2,000 or more‡ ‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: A0089, SCHSIZE, URBANS12 and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhhc51Principal's age 1, Principal's age 1 by Four-category school level (primary/middle/high/combined), Collapsed total K-12 and ungraded enrollment in school and Percentage of enrolled students approved for the NSLP at school. Principal's age(Avg>0) Principal's age(Median>0) Estimates Total49.3 50.0 Four-category school level (primary/middle/high/combined) Primary49.7 51.0 Middle47.5 48.0 High49.2 50.0 Combined49.5 50.0 Collapsed total K-12 and ungraded enrollment in school 1-4949.4 51.0 50-9949.9 51.0 100-14949.6 50.0 150-19950.6 52.0 200-34949.1 50.0 350-49948.8 50.0 500-74949.3 51.0 750-99948.9 50.0 1,000-1,19949.2 49.0 1,200-1,49950.3 52.0 1,500-1,99948.8 49.0 2,000 or more50.1 52.0 Percentage of enrolled students approved for the NSLP at school 0% to 25%49.3 50.0 26% to 50%48.3 49.0 51% to 75%49.0 51.0 More than 75%48.7 49.0 The names of the variables used in this table are: NSLAPP_S, AGE_P, SCHSIZE and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhk7c2Q43/Q40 Annual salary- before taxes and deductions 1 by Four-category school level (primary/middle/high/combined), Collapsed urban-centric school locale code and Percentage of enrolled students approved for the NSLP at school. Q43/Q40 Annual salary- before taxes and deductions(Avg>0) Estimates Total79,306.4 Four-category school level (primary/middle/high/combined) Primary78,166.3 Middle86,685.3 High88,537.3 Combined63,897.8 Collapsed urban-centric school locale code City83,160.8 Suburb87,482.0 Town72,091.6 Rural70,859.2 Percentage of enrolled students approved for the NSLP at school 0% to 25%86,693.7 26% to 50%81,621.4 51% to 75%79,704.2 More than 75%83,103.6 The names of the variables used in this table are: NSLAPP_S, URBANS12, A0249 and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhkbc33Q34/Q32 Total number of hours spent on all school activities every week 1, Q35/Q33 Total number of hours spent on student interaction every week 1 by Four-category school level (primary/middle/high/combined), Collapsed urban-centric school locale code and Collapsed total K-12 and ungraded enrollment in school. Q34/Q32 Total number of hours spent on all school activities every week(Avg>0) Q35/Q33 Total number of hours spent on student interaction every week(Avg>0) Estimates Total57.3 20.6 Four-category school level (primary/middle/high/combined) Primary56.9 19.6 Middle59.4 21.1 High59.0 22.6 Combined55.0 21.7 Collapsed urban-centric school locale code City58.7 20.9 Suburb57.8 19.2 Town57.1 21.1 Rural55.8 21.5 Collapsed total K-12 and ungraded enrollment in school 1-4947.8 23.2 50-9952.9 20.7 100-14955.8 19.3 150-19956.6 20.6 200-34958.1 20.6 350-49958.7 20.4 500-74959.4 19.6 750-99959.9 20.5 1,000-1,19960.8 21.1 1,200-1,49963.5 20.7 1,500-1,99962.8 21.8 2,000 or more65.2 22.1 The names of the variables used in this table are: SCHSIZE, A0226, URBANS12, SCHLEVE2 and A0225. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhkcf14Q1. Years principal at this or any school 0, Q2. Years principal at this school 0 by Four-category school level (primary/middle/high/combined) and Collapsed urban-centric school locale code. Q1. Years principal at this or any school(Avg) Q2. Years principal at this school(Avg) Estimates Total8.1 4.8 Four-category school level (primary/middle/high/combined) Primary8.4 4.9 Middle6.7 4.0 High7.6 4.5 Combined8.9 5.9 Collapsed urban-centric school locale code City8.2 4.9 Suburb8.5 5.1 Town8.1 4.9 Rural7.6 4.5 The names of the variables used in this table are: A0026, A0025, URBANS12 and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhkdcb5Q12G. Influence on spending - principal by Four-category school level (primary/middle/high/combined), Collapsed urban-centric school locale code and Collapsed total K-12 and ungraded enrollment in school. No Influence(%) Minor Influence(%) Moderate Influence(%) Major Influence(%) Total Estimates Total0.8 3.9 19.1 76.2 100% Four-category school level (primary/middle/high/combined) Primary0.7 ! 2.7 18.2 78.4 100% Middle0.4 !! 3.7 18.7 77.3 100% High0.7 ! 6.1 20.7 72.5 100% Combined1.9 ! 6.0 20.9 71.2 100% Collapsed urban-centric school locale code City0.4 ! 3.3 16.1 80.1 100% Suburb0.8 !! 2.8 15.7 80.8 100% Town1.3 ! 4.2 21.9 72.6 100% Rural1.1 ! 5.4 23.7 69.8 100% Collapsed total K-12 and ungraded enrollment in school 1-492.3 ! 2.9 ! 15.1 79.7 100% 50-99‡ 6.4 18.8 74.3 100% 100-1491.2 !! 3.6 23.1 72.1 100% 150-1990.6 !! 3.5 ! 23.5 72.5 100% 200-3491.2 ! 4.9 17.5 76.4 100% 350-4990.9 ! 4.1 19.5 75.5 100% 500-7490.3 !! 3.4 19.7 76.6 100% 750-999‡ 1.9 ! 19.7 78.4 100% 1,000-1,199‡ 4.2 ! 16.9 78.7 100% 1,200-1,499‡ 4.4 ! 22.3 73.3 100% 1,500-1,999‡ 4.0 ! 16.5 79.0 100% 2,000 or more1.0 !! 2.0 ! 15.1 82.0 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: A0089, SCHSIZE, URBANS12 and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Principals Data File 2007-08.Computation by NCES PowerStats on 4/13/2017.bddbhke8b1 Q22a Number of books - total 0 by Collapsed urban-centric school locale code. Q22a Number of books - total(Avg) Estimates Total10,232.0 Collapsed urban-centric school locale code Large or mid-size central city9,201.2 Urban fringe of large or mid-size city11,532.2 Small town/Rural9,124.8 The names of the variables used in this table are: URBANIC and M0149. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 1999-00. Computation by NCES PowerStats on 1/5/2018. fabka312 Q3d Areas - work area 0 by Four-category school level. Q3d Areas - work area(Avg) Estimates Total0.9 Four-category school level Elementary0.8 Middle school0.9 Secondary0.9 Combined0.9 The names of the variables used in this table are: M0057 and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 1999-00. Computation by NCES PowerStats on 1/5/2018. fabka083 Q21 Distance learning by Collapsed total K-12 and ungraded enrollment in school. No(%) Yes(%) Don't know(%) Total Estimates Total73.921.34.8100% Collapsed total K-12 and ungraded enrollment in school 1-4971.923.74.5 !100% 50-9957.239.23.6 !100% 100-14961.632.26.2 !!100% 150-19971.924.43.7 !100% 200-34975.219.94.8100% 350-49976.618.45.0100% 500-74977.717.64.7100% 750-99974.722.13.3100% 1,000-1,19967.027.06.0100% 1,200-1,49971.125.33.6100% 1,500-1,99965.527.76.9100% 2,000 or more57.431.111.5100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. The names of the variables used in this table are: M0148 and SCHSIZE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 1999-00. Computation by NCES PowerStats on 1/5/2018. fabkaf24 Q29a Flexibility of scheduling for classes/activities by Total K-12 and ungraded enrollment in school. All classes flexibly scheduled(%) All classes regularly scheduled(%) Some classes regularly scheduled, other classes flexibly scheduled(%) Total Estimates Total31.842.825.5100% Total K-12 and ungraded enrollment in school 1 to 10039.826.533.7100% 101 to 50025.846.827.4100% 501 to 100030.244.125.7100% More than 100060.827.112.1100% The names of the variables used in this table are: ENRK12UG and M0176. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 1999-00. Computation by NCES PowerStats on 1/5/2018. fabka0a5 Q10a Volunteers provide services for the library media center. by Q4a Can accommodate full class. No(%) Yes(%) Total Estimates Total45.154.9100% Q4a Can accommodate full class No55.844.2100% Yes44.955.1100% The names of the variables used in this table are: M0061 and M0096. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 1999-00. Computation by NCES PowerStats on 1/5/2018. fabkadd1 Q18a Number of books - total 0 by Charter school identifier. Q18a Number of books - total(Avg) Estimates Total10,923.1 Charter school identifier School is a public charter school7,968.8 School is not a public charter school10,958.9 The names of the variables used in this table are: M0089 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 2003-04. Computation by NCES PowerStats on 1/5/2018. fabkdb62 Q14a Number of computer workstations 0 by Collapsed urban-centric school locale code. Q14a Number of computer workstations(Avg) Estimates Total13.3 Collapsed urban-centric school locale code Large or mid-size central city13.2 Urban fringe of a large or mid-size central city14.4 Small town/rural11.4 The names of the variables used in this table are: URBANS03 and M0075. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 2003-04. Computation by NCES PowerStats on 1/5/2018. fabkd123 Q32 Used as a classroom by Four-category school level. No(%) Yes(%) Total Estimates Total89.710.3100% Four-category school level Primary92.47.6100% Middle86.213.8100% High86.213.8100% Combined83.416.6100% The names of the variables used in this table are: SCHLEVE2 and M0125. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 2003-04. Computation by NCES PowerStats on 1/5/2018. fabkd184 Q25 Flexibility of scheduling for classes/activities by Collapsed total K-12 and ungraded enrollment in school. All classes flexibly scheduled(%) All classes regularly scheduled(%) Some classes regularly scheduled, other classes flexibly scheduled(%) Total Estimates Total33.639.626.9100% Collapsed total K-12 and ungraded enrollment in school 1-4965.614.4 !20.1100% 50-9923.742.334.0100% 100-14928.340.930.8100% 150-19921.244.833.9100% 200-34923.646.629.8100% 350-49920.553.026.5100% 500-74930.141.828.1100% 750-99945.526.128.4100% 1,000-1,19963.815.320.9100% 1,200-1,49976.85.6 !17.5100% 1,500-1,99988.86.2 !5.0 !100% 2,000 or more88.3‡8.1 !100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: M0113 and SCHSIZE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 2003-04. Computation by NCES PowerStats on 1/5/2018. fabkdb15 Collapsed urban-centric school locale code by Percentage of enrolled students approved for the NSLP at school. Large or mid-size central city(%) Urban fringe of a large or mid-size central city(%) Small town/rural(%) Total Estimates Total23.649.127.3100% Percentage of enrolled students approved for the NSLP at school 0%‡‡‡100% >0% to 15%12.677.210.2100% >15% to 30%17.060.622.4100% >30% to 50%17.444.138.4100% >50% to 75%26.536.736.8100% More than 75%52.226.121.7100% ‡ Reporting standards not met. The names of the variables used in this table are: URBANS03 and NSLAPP_S. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 2003-04. Computation by NCES PowerStats on 1/5/2018. fabkecb21 Q32a(1) Number of books - total 0 by Charter school identifier. Q32a(1) Number of books - total(Avg) Estimates Total11,705.0 Charter school identifier School is a public charter school7,555.7 School is not a public charter school11,782.6 The names of the variables used in this table are: M0106 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 2007-08. Computation by NCES PowerStats on 1/5/2018. fabkf512 Q22a Number of computer workstations 0 by Collapsed urban-centric school locale code. Q22a Number of computer workstations(Avg) Estimates Total15.2 Collapsed urban-centric school locale code City14.9 Suburb17.6 Town14.3 Rural13.7 The names of the variables used in this table are: URBANS12 and M0075. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 2007-08. Computation by NCES PowerStats on 1/5/2018. fabkfa153 Q24a Access to online, licensed databases - classroom by Four-category school level. No(%) Yes(%) Total Estimates Total10.090.0100% Four-category school level Primary10.589.5100% Middle10.090.0100% High7.492.6100% Combined14.385.7100% The names of the variables used in this table are: M0078 and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 2007-08. Computation by NCES PowerStats on 1/5/2018. fabkfm8a4 Q5 Flexibility of scheduling for classes/activities by Collapsed total K-12 and ungraded enrollment in school. Only flexible scheduling (available as needed)(%) Only regular scheduling (previously specified times)(%) Both flexible and regular scheduling(%) Total Estimates Total22.918.159.1100% Collapsed total K-12 and ungraded enrollment in school 1-4931.014.1 !54.9100% 50-9919.714.6 !65.6100% 100-14915.015.869.2100% 150-19912.814.273.0100% 200-34913.319.567.2100% 350-49912.920.167.1100% 500-74922.025.252.8100% 750-99929.513.457.1100% 1,000-1,19949.26.5 !!44.3100% 1,200-1,49961.23.4 !35.5100% 1,500-1,99974.0‡24.1100% 2,000 or more70.91.0 !!28.0100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: M0033 and SCHSIZE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 2007-08. Computation by NCES PowerStats on 1/5/2018. fabkgec5 Collapsed urban-centric school locale code by Percentage of enrolled students approved for the NSLP at school. City(%) Suburb(%) Town(%) Rural(%) Total Estimates Total23.629.015.132.3100% Percentage of enrolled students approved for the NSLP at school 0%‡‡‡‡100% >0% to 15%12.555.26.625.6100% >15% to 30%13.136.313.237.5100% >30% to 50%16.920.521.840.8100% >50% to 75%24.822.717.634.8100% More than 75%50.616.613.619.1100% ‡ Reporting standards not met. The names of the variables used in this table are: NSLAPP_S and URBANS12. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Library Media Center 2007-08. Computation by NCES PowerStats on 1/5/2018. fabkgh5b1 Q20. Teacher salary - lowest paid full-time 1, Q20. Teacher salary - highest paid full-time 1 by Collapsed urban-centric district locale code. Q20. Teacher salary - lowest paid full-time(Avg>0) Q20. Teacher salary - highest paid full-time(Avg>0) Estimates Total21,093.130,140.1 Collapsed urban-centric district locale code Large or mid-size central city‡‡ Urban fringe of large or mid-size city‡‡ Small town/Rural20,091.927,794.0 ‡ Reporting standards not met. The names of the variables used in this table are: D0507, URBANID and D0508. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 1999-00. Computation by NCES PowerStats on 1/10/2018. baabkba52 Q22a. Teacher benefits - agency other than district makes contributions by Number of schools in district, as reported in CCD. No(%) Yes(%) Total Estimates Total77.922.1100% Number of schools in district, as reported in CCD 1 to 279.120.9100% 3 to 577.522.5100% 6 to 1075.824.2100% 11 to 2078.022.0100% More than 2077.622.4100% The names of the variables used in this table are: AG_NOSCH and D0515. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 1999-00. Computation by NCES PowerStats on 1/10/2018. baabkb9e3 Q14a. Count of teachers dismissed - Poor performance (3 or fewer years of experience) 0, Q14b. Count of teachers dismissed - Poor performance (More than 3 years experience) 0 by Percentage of students in district approved for the National School Lunch Program. Q14a. Count of teachers dismissed - Poor performance (3 or fewer years of experience)(Avg) Q14b. Count of teachers dismissed - Poor performance (More than 3 years experience)(Avg) Estimates Total0.80.3 Percentage of students in district approved for the National School Lunch Program 0% to 20%0.70.3 ! More than 20% to 40%0.70.2 More than 40% to 60%1.00.3 More than 60% to 80%1.40.5 More than 80%0.90.5 ! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: D0495, NSLAPP and D0496. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 1999-00. Computation by NCES PowerStats on 1/10/2018. baabkb234 Q15b. Teachers association or union agreement type by Q6f. K-12 Student Enrollment - Total K-12. Collective bargaining(%) Meet-and-confer(%) Total Estimates Total91.88.2100% Q6f. K-12 Student Enrollment - Total K-12 1 to 50088.511.5100% 501 to 1,50093.76.3100% 1,501 to 3,00092.97.1100% 3,001 to 10,00092.47.6100% 10,001 or more89.610.4100% The names of the variables used in this table are: D0498 and D0463. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 1999-00. Computation by NCES PowerStats on 1/10/2018. baabkbne55 Q54d. Salary Incentive to retain teachers in a less desirable location by Percentage of students in district approved for the National School Lunch Program. No(%) Yes(%) Total Estimates Total96.43.6100% Percentage of students in district approved for the National School Lunch Program 0% to 20%98.02.0100% More than 20% to 40%96.63.4100% More than 40% to 60%96.04.0100% More than 60% to 80%94.45.6100% More than 80%90.99.1100% The names of the variables used in this table are: NSLAPP and D0614. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 1999-00. Computation by NCES PowerStats on 1/10/2018. baabkb1a1 Q27. Teacher salary - lowest paid full-time 1, Q27. Teacher salary - lowest paid full-time 1, Q27. Teacher salary - highest paid full-time 1, Q27. Teacher salary - highest paid full-time 1 by Collapsed urban-centric district locale code. Q27. Teacher salary - lowest paid full-time(Avg>0) Q27. Teacher salary - lowest paid full-time(Median>0) Q27. Teacher salary - highest paid full-time(Avg>0) Q27. Teacher salary - highest paid full-time(Median>0) Estimates Total28,001.627,100.040,580.136,000.0 Collapsed urban-centric district locale code Large or mid-size central city30,817.430,000.048,132.245,000.0 Urban fringe of a large or mid-size central city29,804.030,000.042,869.934,600.0 Small town/rural24,353.723,550.033,270.528,550.0 The names of the variables used in this table are: URBAND03, D0123 and D0122. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2003-04. Computation by NCES PowerStats on 1/9/2018. mabkf5d2 Q28e. Teacher Benefits - Tuition Reimbursement by Number of schools in district, post-collapsing, Collapsed urban-centric district locale code and Q3. Total student enrollment- K-12 grade levels. No(%) Yes(%) Total Estimates Total58.241.8100% Number of schools in district, post-collapsing 1 to 262.337.7100% 3 to 553.546.5100% 6 to 1054.445.6100% 11 to 2061.039.0100% More than 2056.643.4100% Collapsed urban-centric district locale code Large or mid-size central city62.937.1100% Urban fringe of a large or mid-size central city54.945.1100% Small town/rural60.439.6100% Q3. Total student enrollment- K-12 grade levels 1 to 1,00062.937.1100% 1,001 to 2,00050.449.6100% 2,001 to 5,00050.949.1100% 5,001 to 10,00058.741.3100% More than 10,00062.337.7100% The names of the variables used in this table are: URBAND03, D0051, D0128 and AG_NOSC2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2003-04. Computation by NCES PowerStats on 1/9/2018. mabkfd23 Q15a. Count of teachers dismissed - Poor performance (3 or fewer years of experience) 0, Q15b. Count of teachers dismissed - Poor performance (More than 3 years experience) 0 by Number of schools in district, post-collapsing, Collapsed urban-centric district locale code and Percentage of students in district approved for the National School Lunch Program. Q15a. Count of teachers dismissed - Poor performance (3 or fewer years of experience)(Avg) Q15b. Count of teachers dismissed - Poor performance (More than 3 years experience)(Avg) Estimates Total1.21.9 Number of schools in district, post-collapsing 1 to 20.40.9 ! 3 to 50.60.8 ! 6 to 101.51.7 11 to 202.65.4 More than 2011.815.7 Collapsed urban-centric district locale code Large or mid-size central city4.77.7 Urban fringe of a large or mid-size central city1.31.7 Small town/rural0.41.0 Percentage of students in district approved for the National School Lunch Program 0% to 20%1.01.3 More than 20% to 40%1.22.5 More than 40% to 60%1.21.9 More than 60% to 80%1.82.2 More than 80%1.71.5 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: D0092, URBAND03, D0091, NSLAPP_D and AG_NOSC2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2003-04. Computation by NCES PowerStats on 1/9/2018. mabkff64 Q17. Teachers association or union agreement by Q3. Total student enrollment- K-12 grade levels and Number of schools in district, post-collapsing. Yes, collective bargaining(%) Yes, meet-and-confer(%) No(%) Total Estimates Total57.8 6.0 36.2 100% Q3. Total student enrollment- K-12 grade levels 1 to 1,00046.95.447.7100% 1,001 to 2,00071.36.921.8100% 2,001 to 5,00070.45.723.9100% 5,001 to 10,00068.56.624.9100% More than 10,00064.19.026.9100% Number of schools in district, post-collapsing 1 to 244.85.250.0100% 3 to 569.46.424.2100% 6 to 1070.36.023.7100% 11 to 2064.47.827.7100% More than 2060.79.429.9100% The names of the variables used in this table are: D0051, D0094 and AG_NOSC2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2003-04. Computation by NCES PowerStats on 1/9/2018. mabkfde5 Q66d. Salary Incentive to retain teachers in a less desirable location by Percentage of students in district approved for the National School Lunch Program and Collapsed urban-centric district locale code. No(%) Yes(%) Total Estimates Total95.44.6100% Percentage of students in district approved for the National School Lunch Program 0% to 20%98.11.9100% More than 20% to 40%96.83.2100% More than 40% to 60%95.34.7100% More than 60% to 80%89.810.2 !100% More than 80%90.19.9100% Collapsed urban-centric district locale code Large or mid-size central city91.09.0100% Urban fringe of a large or mid-size central city95.24.8100% Small town/rural96.23.8100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: D0318, NSLAPP_D and URBAND03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2003-04. Computation by NCES PowerStats on 1/9/2018. mabkf6e1 Q28a. Teacher salary - lowest paid full-time 0, Q28b. Teacher salary - highest paid full-time 0 by Collapsed urban-centric district locale code. Q28a. Teacher salary - lowest paid full-time(Avg) Q28b. Teacher salary - highest paid full-time(Avg) Estimates Total34,019.060,439.3 Collapsed urban-centric district locale code City35,011.658,273.0 Suburb37,933.876,430.5 Town32,696.858,680.7 Rural32,479.854,543.7 The names of the variables used in this table are: D0336, URBAND12 and D0335. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2007-08. Computation by NCES PowerStats on 1/12/2018. bcabkdc5d2 Q29f. Teacher Benefits - Tuition Reimbursement by Number of schools in district, post-collapsing. No(%) Yes(%) Total Estimates Total55.444.6100% Number of schools in district, post-collapsing 1 to 254.345.7100% 3 to 555.444.6100% 6 to 1055.844.2100% 11 to 2058.941.1100% More than 2059.940.1100% The names of the variables used in this table are: AG_NOSC2 and D0343. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2007-08. Computation by NCES PowerStats on 1/12/2018. bcabkdcc3 Q22a. Count of teachers dismissed - Poor performance non-tenured 0, Q22b. Count of teachers dismissed - Poor performance tenured 0 by Number of schools in district, post-collapsing. Q22a. Count of teachers dismissed - Poor performance non-tenured(Avg) Q22b. Count of teachers dismissed - Poor performance tenured(Avg) Estimates Total1.43.0 Number of schools in district, post-collapsing 1 to 20.40.6 3 to 50.62.2 6 to 101.95.3 11 to 203.110.0 More than 2013.915.1 The names of the variables used in this table are: D0319, D0318 and AG_NOSC2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2007-08. Computation by NCES PowerStats on 1/12/2018. bcabkec1b4 Q10. Teachers association or union agreement by Q4f. K-12 Student Enrollment - Total K-12. Yes, meet-and-confer(%) Yes, collective bargaining(%) No(%) Total Estimates Total10.953.535.6100% Q4f. K-12 Student Enrollment - Total K-12 1 to 5009.936.953.3100% 501 to 1,00012.056.231.8100% 1,001 to 3,00011.067.022.0100% 3,001 or more11.664.424.0100% The names of the variables used in this table are: D0296 and D0282. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2007-08. Computation by NCES PowerStats on 1/12/2018. bcabkeka015 Q30c. Salary Incentive to retain teachers in a less desirable location by Percentage of students in district approved for the National School Lunch Program. No(%) Yes(%) Total Estimates Total94.35.7100% Percentage of students in district approved for the National School Lunch Program 0% to 20%98.11.9100% More than 20% to 40%97.42.6100% More than 40% to 60%93.76.3100% More than 60% to 80%89.011.0100% More than 80%88.811.2100% The names of the variables used in this table are: D0349 and NSLAPP_D. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School District Data File 2007-08. Computation by NCES PowerStats on 1/12/2018. bcabke501Q42a Number of students with Individual Education Plan (IEP) with (percent >0.5) by Charter school identifier, Collapsed urban-centric school locale code and Categorical measure of total K-12 enrollment. Q42a Number of students with Individual Education Plan (IEP)(%>0.5) Estimates Total96.8 Charter school identifier School is a public charter school89.5 School is not a public charter school96.9 Collapsed urban-centric school locale code Large or mid-size central city97.4 Urban fringe of large or mid-size city96.6 Small town/Rural96.7 Categorical measure of total K-12 enrollment 1-4967.3 50-9992.5 100-14992.5 150-19995.0 200-34998.2 350-49999.2 500-74999.0 750-99998.5 1,000-1,19998.8 1,200-1,49999.6 1,500-1,999100.0 2,000 or more99.7 The names of the variables used in this table are: SCHSIZE, URBANIC, S0315 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhpf52Q7a Number of students enrolled in grades K-12 1 by Three-category level of school based on grade levels offered. Q7a Number of students enrolled in grades K-12(Avg>0) Estimates Total535.4 Three-category level of school based on grade levels offered Elementary482.9 Secondary730.6 Combined266.8 The names of the variables used in this table are: SCHLEVEL and S0092. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhpc983Q18d Programs offered: Advanced Placement (AP) courses for college credit by Q26c Percentage of students who went to a 4-yr college . No(%) Yes(%) Total Estimates Total85.4 14.6 100% Q26c Percentage of students who went to a 4-yr college Less than 50%46.9 53.1 100% 50% to 69%27.4 72.6 100% 70% to 79%22.0 78.0 100% 80% to 89%31.4 68.6 100% 90% or more23.5 76.5 100% The names of the variables used in this table are: S0165 and S0128. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhpfe4Q40a School had students enrolled who received Title I services by Three-category level of school based on grade levels offered and Number of minority students in the school. No(%) Yes(%) Total Estimates Total47.1 52.9 100% Three-category level of school based on grade levels offered Elementary38.3 61.7 100% Secondary71.2 28.8 100% Combined54.9 45.1 100% Number of minority students in the school Less than 25%49.1 50.9 100% 25% to 49%55.5 44.5 100% 50% to 74%55.3 44.7 100% 75% or more42.9 57.1 100% The names of the variables used in this table are: S0288, NMINST_C and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhpd315Estimated number of full-time equivalent teachers in the school 1 by Charter school identifier and Q13 School type. Estimated number of full-time equivalent teachers in the school(Avg>0) Estimates Total34.3 Charter school identifier School is a public charter school16.2 School is not a public charter school34.5 Q13 School type Regular elementary or secondary35.4 Elementary or secondary school with a special program emphasis41.6 Special education school18.5 Vocational/technical school33.0 Alternative school10.8 The names of the variables used in this table are: NUMTCH, S0110 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhpp231Q83a School has English Language Learners (ELL)/LEP students by Q16 School type and Four-category level of school based on grade levels offered . No(%) Yes(%) Total Estimates Total86.6 13.4 100% Q16 School type Regular elementary or secondary85.8 14.2 100% Montessori91.1 8.9 100% Elementary or secondary school with a special program emphasis85.1 14.9 100% Special education school91.2 8.8 ! 100% Alternative school91.0 9.0 100% Four-category level of school based on grade levels offered Elementary86.9 13.1 100% Middle school‡ ‡ 100% Secondary79.6 20.4 100% Combined87.8 12.2 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: S0110, SCHLEVE2 and S0320. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhp2d2Q31 Percentage of students who went to a 4-yr college 1 by Q33a School charges tuition for all or some of its students and Q35 School has special requirements when admitting students. Q31 Percentage of students who went to a 4-yr college(Avg>0) Estimates Total70.6 Q33a School charges tuition for all or some of its students No‡ Yes70.9 Q35 School has special requirements when admitting students No64.7 Yes71.4 ‡ Reporting standards not met. The names of the variables used in this table are: S0968, S0165 and S0115. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhpd13Q33c Highest tuition charged for a full-time student at the school 1 by Q16 School type, for Q33a School charges tuition for all or some of its students (Yes). Q33c Highest tuition charged for a full-time student at the school(Median>0) Estimates Total2,761.0 Q16 School type Regular elementary or secondary2,600.0 Montessori4,750.0 Elementary or secondary school with a special program emphasis3,750.0 Special education school22,409.0 Alternative school3,412.0 The names of the variables used in this table are: S0968, S0110 and S0970. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhpa1a4Q82a Number of students with Individual Education Plan (IEP) with (percent >0) by Q17 Major role of the school is to support homeschooling and Q18 School is located in a private home that is used as family residence. Q82a Number of students with Individual Education Plan (IEP)(%>0) Estimates Total42.1 Q17 Major role of the school is to support homeschooling No42.3 Yes35.2 Q18 School is located in a private home that is used as family residence No42.0 Yes‡ ‡ Reporting standards not met. The names of the variables used in this table are: S0906, S0905 and S0315. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhp0c5Q52a Normal teacher base salary: bachelor's degree, no teaching experience 1, Q52b Normal teacher base salary: bachelor's & 10 yrs teaching experience 1 by Q16 School type. Q52a Normal teacher base salary: bachelor's degree, no teaching experience(Avg>0) Q52b Normal teacher base salary: bachelor's & 10 yrs teaching experience(Avg>0) Estimates Total20,302.1 25,358.7 Q16 School type Regular elementary or secondary20,000.6 24,773.0 Montessori21,656.3 28,587.4 Elementary or secondary school with a special program emphasis19,635.3 25,506.1 Special education school26,488.3 33,670.4 Alternative school20,782.2 27,972.7 The names of the variables used in this table are: S0501, S0502 and S0110. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhp8d1Q42a/Q82a Number of students with Individual Education Plan (IEP) with (percent >0.5) by Three-category level of school based on grade levels offered and Collapsed urban-centric school locale code. Q42a/Q82a Number of students with Individual Education Plan (IEP)(%>0.5) Estimates Total83.5 Three-category level of school based on grade levels offered Elementary85.4 Secondary90.5 Combined56.2 Collapsed urban-centric school locale code Large or mid-size central city77.1 Urban fringe of large or mid-size city85.3 Small town/Rural87.3 The names of the variables used in this table are: URBANIC, S0315 and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhadfe2Q26c/Q31 Percentage of students who went to a 4-yr college 1, Q26c/Q31 Percentage of students who went to a 2-yr college 1 by School sector and Categorical measure of total K-12 enrollment. Q26c/Q31 Percentage of students who went to a 4-yr college(Avg>0) Q26c/Q31 Percentage of students who went to a 2-yr college(Avg>0) Estimates Total47.7 25.1 School sector Public school39.9 24.4 Private school70.6 28.3 Categorical measure of total K-12 enrollment 1-4951.3 35.8 50-9950.4 32.2 100-14946.6 26.2 150-19943.3 29.0 200-34948.1 22.5 350-49947.4 21.7 500-74946.5 23.0 750-99951.7 20.6 1,000-1,19948.9 21.9 1,200-1,49945.9 22.6 1,500-1,99945.9 26.0 2,000 or more46.3 28.5 The names of the variables used in this table are: SECTOR, SCHSIZE, S0165 and S0166. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhaff8d3Q18d/Q42d Programs offered: Advanced Placement (AP) courses for college credit by School sector and Average number of students per teacher. No(%) Yes(%) Total Estimates Total85.5 14.5 100% School sector Public school85.4 14.6 100% Private school85.9 14.1 100% Average number of students per teacher 0 to 1084.1 15.9 100% 11 to 1385.0 15.0 100% 14 to 1684.7 15.3 100% 17 to 2087.5 12.5 100% More than 2087.7 12.3 100% The names of the variables used in this table are: SECTOR, S0128 and RASTTCH. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhag794Q16/Q35 School has special requirements when admitting students by School sector and Q13/Q16 School type. No(%) Yes(%) Total Estimates Total73.7 26.3 100% School sector Public school86.6 13.4 100% Private school33.4 66.6 100% Q13/Q16 School type Regular elementary or secondary78.9 21.1 100% Montessori60.8 39.2 100% Elementary or secondary school with a special program emphasis56.4 43.6 100% Special education school0.2 99.8 100% Vocational/technical school52.7 47.3 100% Alternative school32.0 68.0 100% The names of the variables used in this table are: S0115, S0110 and SECTOR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhahb85Q40a/Q77 School had students enrolled who received Title I services by School sector, Percentage of students who are of a racial/ethnic minority and Percentage of teachers who are of a racial/ethnic minority. No(%) Yes(%) Total Estimates Total54.6 45.4 100% School sector Public school47.1 52.9 100% Private school78.2 21.8 100% Percentage of students who are of a racial/ethnic minority 0% to 10%59.8 40.2 100% >10% to 20%64.4 35.6 100% >20% to 50%59.9 40.1 100% >50% to 75%41.9 58.1 100% More than 75%34.1 65.9 100% Percentage of teachers who are of a racial/ethnic minority 0%57.1 42.9 100% >0% to 5%62.6 37.4 100% >5% to 15%57.1 42.9 100% >15% to 30%51.5 48.5 100% More than 30%41.3 58.7 100% The names of the variables used in this table are: SECTOR, MINTCH, S0288 and MINENR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 1999-00.Computation by NCES PowerStats on 11/14/2017.benbhake71 Q45 Number of students with Individual Education Plan (IEP) with (percent >0.5) by Charter school identifier, Collapsed urban-centric school locale code and Four-category level of school based on grade levels offered. Q45 Number of students with Individual Education Plan (IEP)(%>0.5) Estimates Total97.7 Charter school identifier School is a public charter school94.2 School is not a public charter school97.8 Collapsed urban-centric school locale code Large or mid-size central city97.6 Urban fringe of a large or mid-size central city98.3 Small town/rural96.7 Four-category level of school based on grade levels offered Primary98.3 Middle99.5 High95.2 Combined96.5 The names of the variables used in this table are: SCHLEVE2, S0604, URBANS03 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhk022 Q33a 12th grades enrolled 2002-03 with (percent =1), Q33c Percentage of students who went to a 4-yr college 1 by Three-category level of school based on grade levels offered, Collapsed urban-centric school locale code and Charter school identifier. Q33a 12th grades enrolled 2002-03(%=1) Q33c Percentage of students who went to a 4-yr college(Avg>0) Estimates Total83.8 39.8 Three-category level of school based on grade levels offered Elementary6.8 ! ‡ Secondary93.3 40.1 Combined87.6 38.6 Collapsed urban-centric school locale code Large or mid-size central city82.1 38.9 Urban fringe of a large or mid-size central city86.1 41.4 Small town/rural82.3 38.6 Charter school identifier School is a public charter school46.1 29.4 School is not a public charter school86.2 40.1 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: CHARFLAG, URBANS03, S0503, S0505 and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhkac3 Q22d Programs offered: Advanced Placement (AP) courses for college credit with (percent >0), Q22e Programs offered: International Baccalaureate (IB) with (percent >0) by Categorical measure of total K-12 enrollment. Q22d Programs offered: Advanced Placement (AP) courses for college credit(%>0) Q22e Programs offered: International Baccalaureate (IB)(%>0) Estimates Total16.2 0.7 Categorical measure of total K-12 enrollment Less than 5009.8 0.1 !! 500 to 99913.9 1.1 1,000 to 1,49944.3 1.5 1,500 or more83.1 6.1 !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: S0465, SCHSIZE and S0466. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhkmb44 Q47a School has English Language Learners (ELL)/LEP students with (percent >0) by Charter school identifier and Urban-centric school locale code. Q47a School has English Language Learners (ELL)/LEP students(%>0) Estimates Total62.9 Charter school identifier School is a public charter school43.3 School is not a public charter school63.4 Urban-centric school locale code Large city73.2 Mid-size city72.6 Urban fringe of a large city77.9 Urban fringe of a mid-size city65.2 Large town68.2 Small town59.2 Rural, outside CBSA35.0 Rural, inside CBSA55.5 The names of the variables used in this table are: S0610, SLOCP_03 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhka45 Q58 School had students enrolled who received Title I services by Q17a School has a magnet program and Q33c Percentage of students who went to a 4-yr college . No(%) Yes(%) Total Estimates Total45.6 54.4 100% Q17a School has a magnet program No45.3 54.7 100% Yes51.2 48.8 100% Q33c Percentage of students who went to a 4-yr college Less than 35%68.4 31.6 100% 35% to 49%76.0 24.0 100% 50% to 74%75.2 24.8 100% 75% or more70.7 29.3 100% The names of the variables used in this table are: S0635, S0505 and S0444. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhkdb11Q78 Number of students with Individual Education Plan (IEP) with (percent >.5) by Q15 School type. Q78 Number of students with Individual Education Plan (IEP)(%>0.5) Estimates Total42.7 Q15 School type Regular40.3 Montessori32.9 Special Program Emphasis24.4 Special Education93.1 Alternative46.7 Early Childhood Program/Day Care Center‡ ‡ Reporting standards not met. The names of the variables used in this table are: S0441 and S0604. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhnad832Three-category level of school based on grade levels offered by Estimated number of full-time equivalent teachers in the school and Categorical measure of total K-12 enrollment. Elementary(%) Secondary(%) Combined(%) Total Estimates Total61.1 9.4 29.6 100% Estimated number of full-time equivalent teachers in the school 0 to 2065.9 6.3 27.8 100% 21 to 4053.6 15.0 31.4 100% 41 to 6017.1 40.1 42.8 100% 61 to 8016.3 ! 39.5 44.2 100% More than 80‡ 22.9 72.6 100% Categorical measure of total K-12 enrollment Less than 50063.2 7.7 29.1 100% 500 to 99936.4 28.1 35.5 100% 1,000 to 1,499‡ 56.3 36.2 100% 1,500 or more‡ ‡ ‡ 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: NUMTCH, SCHSIZE and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhncba3Highest tuition charged by private school 1 by Q2 Total number of students enrolled at the school and Four-category level of school based on grade levels offered . Highest tuition charged by private school(Median>0) Estimates Total3,500.0 Q2 Total number of students enrolled at the school 1 to 1002,950.0 101 to 2003,460.0 201 to 4003,816.0 401 to 6004,300.0 More than 6006,000.0 Four-category level of school based on grade levels offered Primary3,300.0 Middle‡ High7,000.0 Combined3,320.0 ‡ Reporting standards not met. The names of the variables used in this table are: S0734, TUITIN and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhnemce4Q32a 12th grades enrolled 2002-03 with (percent =1), Q34b Percentage of students who went to a 4-yr college with (percent >0.5) by Three-level private school typology, Collapsed urban-centric school locale code and Three-category level of school based on grade levels offered. Q32a 12th grades enrolled 2002-03(%=1) Q34b Percentage of students who went to a 4-yr college(%>0.5) Estimates Total28.9 79.5 Three-level private school typology Roman Catholic16.2 99.4 Other religious33.8 75.0 Nonsectarian33.9 77.1 Collapsed urban-centric school locale code Large or mid-size central city26.1 86.4 Urban fringe of a large or mid-size central city28.3 78.0 Small town/rural37.3 73.1 Three-category level of school based on grade levels offered Elementary0.4 ! ‡ Secondary91.1 89.8 Combined68.2 75.0 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met. The names of the variables used in this table are: SCHLEVEL, URBANS03, S0503, S0505 and RELIG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhnghfe5Estimated number of full-time equivalent teachers in the school 1 by Percentage of enrolled students with an IEP and Percentage of enrolled students who are LEP. Estimated number of full-time equivalent teachers in the school(Avg>0) Estimates Total14.7 Percentage of enrolled students with an IEP 0% to 5%15.2 >5% to 10%15.9 >10% to 15%12.2 >15% to 20%‡ More than 20%9.6 Percentage of enrolled students who are LEP 0%14.0 >0% to 1%29.9 >1% to 2%17.6 >2% to 5%18.0 More than 5%13.0 ‡ Reporting standards not met. The names of the variables used in this table are: NUMTCH, LEP and IEP. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhnhaa1Q58/Q91 School had students enrolled who received Title I services by School sector, Collapsed urban-centric school locale code and Census region, based on ANSI state code. No(%) Yes(%) Total Estimates Total54.3 45.7 100% School sector Public school45.6 54.4 100% Private school81.1 18.9 100% Collapsed urban-centric school locale code Large or mid-size central city52.3 47.7 100% Urban fringe of a large or mid-size central city59.4 40.6 100% Small town/rural46.2 53.8 100% Census region, based on ANSI state code Northeast51.9 48.1 100% Midwest53.8 46.2 100% South54.2 45.8 100% West56.9 43.1 100% The names of the variables used in this table are: SECTOR, S0635, REGION and URBANS03. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhnpaf32Q33c/Q34b Percentage of students who went to a 4-yr college 1 by School sector and Census region, based on ANSI state code. Q33c/Q34b Percentage of students who went to a 4-yr college(Avg>0) Estimates Total47.5 School sector Public school39.8 Private school70.7 Census region, based on ANSI state code Northeast61.9 Midwest46.4 South46.3 West38.3 The names of the variables used in this table are: SECTOR, S0505 and REGION. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhncd3Q22d/Q46d Programs offered: Advanced Placement (AP) courses for college credit by School sector and Estimated number of full-time equivalent teachers in the school. No(%) Yes(%) Total Estimates Total84.2 15.8 100% School sector Public school83.8 16.2 100% Private school85.3 14.7 100% Estimated number of full-time equivalent teachers in the school 0 to 2092.6 7.4 100% 21 to 4087.4 12.6 100% 41 to 6080.7 19.3 100% 61 to 8059.8 40.2 100% More than 8023.6 76.4 100% The names of the variables used in this table are: SECTOR, S0465 and NUMTCH. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhn8c4Percentage of enrolled students who are LEP by School sector and Percentage of students who are of a racial/ethnic minority. 0%(%) >0% to 1%(%) >1% to 2%(%) >2% to 5%(%) More than 5%(%) Total Estimates Total49.3 13.7 6.3 9.5 21.3 100% School sector Public school37.1 17.1 7.4 11.6 26.7 100% Private school86.9 3.4 2.6 2.8 4.2 100% Percentage of students who are of a racial/ethnic minority 0% to 10%70.1 18.6 4.9 4.5 2.0 100% >10% to 20%44.3 17.2 11.2 15.9 11.4 100% >20% to 50%36.7 13.1 7.9 15.7 26.6 100% >50% to 75%31.8 9.0 5.8 11.1 42.3 100% More than 75%37.7 5.0 3.3 5.9 48.0 100% The names of the variables used in this table are: SECTOR, LEP and MINENR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhnbc5Q5f Total number of students enrolled in grades K-12 1 by School sector and Four-category level of school based on grade levels offered . Q5f Total number of students enrolled in grades K-12(Median>0) Estimates Total362.0 School sector Public school446.0 Private school108.0 Four-category level of school based on grade levels offered Primary348.0 Middle609.0 High501.0 Combined121.0 The names of the variables used in this table are: S0422, SCHLEVE2 and SECTOR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 2003-04.Computation by NCES PowerStats on 11/14/2017.benbhnbfe1 Percentage of enrolled students with an IEP with (percent >0.5) by Charter school identifier. Percentage of enrolled students with an IEP(%>0.5) Estimates Total97.0 Charter school identifier School is a public charter school96.7 School is not a public charter school97.0 The names of the variables used in this table are: IEP and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhppb72 Q1 School offers 12th grade with (percent >0) by Three-category level of school based on grade levels offered. Q1 School offers 12th grade(%>0) Estimates Total27.3 Three-category level of school based on grade levels offered Elementary‡ Secondary85.7 Combined93.1 ‡ Reporting standards not met. The names of the variables used in this table are: S0037 and SCHLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhph6f3 Q19d Programs offered: Advanced Placement (AP) courses for college credit by Categorical measure of total K-12 enrollment. No(%) Yes(%) Total Estimates Total82.217.8100% Categorical measure of total K-12 enrollment 1-4992.57.5 !100% 50-9989.910.1 !100% 100-14982.517.5 !100% 150-19986.413.6 !100% 200-34988.211.8100% 350-49991.18.9100% 500-74987.412.6100% 750-99979.320.7100% 1,000-1,19960.839.2100% 1,200-1,49934.066.0100% 1,500-1,99914.585.5100% 2,000 or more8.3 !91.7100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: SCHSIZE and S0084. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhp794 Percentage of enrolled students who are LEP with (percent >0) by Urban-centric school locale code. Percentage of enrolled students who are LEP(%>0) Estimates Total67.1 Urban-centric school locale code City, Large79.6 City, Midsize70.6 City, Small77.7 Suburb, Large81.1 Suburb, Midsize69.4 Suburb, Small69.6 Town, Fringe67.6 Town, Distant67.8 Town, Remote61.3 Rural, Fringe66.7 Rural, Distant43.4 Rural, Remote36.4 The names of the variables used in this table are: SLOCP12 and LEP. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhpaab5 Q43 School had students enrolled who received Title I services by Q18a School has a magnet program. No(%) Yes(%) Total Estimates Total47.552.5100% Q18a School has a magnet program No47.152.9100% Yes52.647.4100% The names of the variables used in this table are: S0218 and S0078. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public School Data File 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhab741 Percentage of enrolled students with an IEP with (percent >0.5) by Q15 School type. Percentage of enrolled students with an IEP(%>0.5) Estimates Total58.0 Q15 School type Regular school54.7 Montessori school51.5 Special Program Emphasis School48.2 Special Education school100.0 Career/Technical/Vocational school‡ Alternative/Other school75.1 Early Childhood Program or Day Care Center‡ ‡ Reporting standards not met. The names of the variables used in this table are: IEP and S0048. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhbd542 Four-category level of school based on grade levels offered by Estimated number of full-time equivalent teachers in the school. Primary(%) Middle(%) High(%) Combined(%) Total Estimates Total57.01.010.631.3100% Estimated number of full-time equivalent teachers in the school 0 to 2062.61.17.828.4100% 21 to 4049.0‡17.233.6100% 41 to 6017.0‡28.653.6100% 61 to 806.2 !!‡40.253.6100% More than 80‡‡13.481.6100% !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: NUMTCH and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhbee3 Highest tuition charged by private school 0 by Categorical measure of total K-12 enrollment. Highest tuition charged by private school(Avg) Estimates Total6,907.5 Categorical measure of total K-12 enrollment 1-496,307.1 50-996,644.4 100-1496,892.9 150-1997,106.3 200-3496,757.6 350-4997,687.3 500-7498,743.3 750-9999,886.5 1,000-1,19910,109.4 1,200-1,499‡ 1,500-1,999‡ 2,000 or more‡ ‡ Reporting standards not met. The names of the variables used in this table are: TUITIN and SCHSIZE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhbp394 Q41 12th grades enrolled 2006-07 with (percent >0) by Three-level private school typology Q41 12th grades enrolled 2006-07(%>0) Estimates Total31.7 Three-level private school typology Catholic18.7 Other religious35.6 Nonsectarian37.8 The names of the variables used in this table are: RELIG and S0112. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhb735 Estimated number of full-time equivalent teachers in the school by Percentage of enrolled students with an IEP. 0 to 20(%) 21 to 40(%) 41 to 60(%) 61 to 80(%) More than 80(%) Total Estimates Total77.315.93.91.41.4100% Percentage of enrolled students with an IEP 0% to 5%78.215.03.91.61.3100% >5% to 10%68.323.54.31.7 !2.1100% >10% to 15%71.219.73.5 !‡4.4 !100% >15% to 20%‡‡‡‡‡100% More than 20%84.911.83.0 !‡‡100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: NUMTCH and IEP. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Private School Data File 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhb671 Q43/Q56 Title I by School sector (public or private). No(%) Yes(%) Total Estimates Total55.444.6100% School sector (public or private) Public47.552.5100% Private81.019.0100% The names of the variables used in this table are: S0218 and SECTOR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhbd22 Q27c/Q43b Percent in 4 year college 0 by School sector (public or private). Q27c/Q43b Percent in 4 year college(Avg) Estimates Total47.0 School sector (public or private) Public39.5 Private66.5 The names of the variables used in this table are: SECTOR and S0114. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhb023 Q19d/Q33d Program - AP courses by Estimated number of full-time equivalent teachers in the school. No(%) Yes(%) Total Estimates Total82.617.4100% Estimated number of full-time equivalent teachers in the school 0 to 2090.89.2100% 21 to 4086.713.3100% 41 to 6080.319.7100% 61 to 8057.442.6100% More than 8023.676.4100% The names of the variables used in this table are: NUMTCH and S0084. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhb8c4 Percentage of enrolled students who are LEP 0 by School sector (public or private) and Percentage of students who are of a racial/ethnic minority. Percentage of enrolled students who are LEP(Median) Estimates Total0.4 School sector (public or private) Public1.3 Private# Percentage of students who are of a racial/ethnic minority 0% to 10%# >10% to 20%0.3 >20% to 50%1.8 >50% to 75%3.9 More than 75%7.0 # Rounds to zero The names of the variables used in this table are: SECTOR, LEP and MINENR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhb845 Four-category level of school based on grade levels offered by Categorical measure of total K-12 enrollment. Primary(%) Middle(%) High(%) Combined(%) Total Estimates Total56.411.918.713.0100% Categorical measure of total K-12 enrollment 1-4948.21.3 !!18.631.9100% 50-9948.61.8 !18.331.3100% 100-14955.83.620.120.5100% 150-19957.99.3 !15.617.2100% 200-34966.09.112.812.1100% 350-49971.712.29.86.3100% 500-74963.118.612.85.5100% 750-99946.827.320.94.9100% 1,000-1,19922.529.938.19.6100% 1,200-1,4998.722.163.95.3100% 1,500-1,999‡8.9 !87.42.9 !100% 2,000 or more‡4.5 !!92.32.0 !100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: SCHSIZE and SCHLEVE2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, Schools and Staffing Survey (SASS), Public and Private School Data Files 2007-08. Computation by NCES PowerStats on 11/3/2017. dnbhb9a1 Attendance intensity (all schools) by NPSAS institution sector (4 with multiple). Exclusively full-time(%) Exclusively part-time(%) Mixed full-time and part-time(%) Total Estimates Total43.633.123.3100% NPSAS institution sector (4 with multiple) Public 4-year53.722.523.8100% Private nonprofit 4-year63.317.619.1100% Public 2-year24.855.619.7100% Private for profit63.420.316.3100% Others or attended more than one school33.920.545.5100% The names of the variables used in this table are: SECTOR4 and ATTNPTRN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES PowerStats on 10/4/2017. embhdde2 Federal Pell grant with (percent >0) by Dependency status. Federal Pell grant(%>0) Estimates Total39.1 Dependency status Dependent student36.6 Independent student41.8 The names of the variables used in this table are: DEPEND and PELLAMT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES PowerStats on 10/4/2017. embhd963 Total aid amount 1 by Attendance intensity (all schools). Total aid amount(Avg>0) Estimates Total12,261.9 Attendance intensity (all schools) Exclusively full-time15,497.8 Exclusively part-time5,084.7 Mixed full-time and part-time12,691.6 The names of the variables used in this table are: ATTNPTRN and TOTAID. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES PowerStats on 10/4/2017. embhd2f4 Dependent students: Parents' income by NPSAS institution sector (4 with multiple). Less than $27,900(%) $27,900-62,999(%) $63,000-113,499(%) $113,500 or more(%) Total Estimates Total25.024.925.025.0100% NPSAS institution sector (4 with multiple) Public 4-year21.624.325.928.1100% Private nonprofit 4-year17.620.724.037.8100% Public 2-year31.928.425.214.5100% Private for profit46.528.016.49.1100% Others or attended more than one school23.223.725.727.4100% The names of the variables used in this table are: DEPINC and SECTOR4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES PowerStats on 10/4/2017. embhd675 Total aid amount 0 by Undergraduate degree program. Total aid amount(Avg) Estimates Total8,871.2 Undergraduate degree program Certificate5,429.5 Associate's degree4,002.6 Bachelor's degree14,303.6 Not in a degree program or others1,770.6 The names of the variables used in this table are: UGDEG and TOTAID. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES PowerStats on 10/4/2017. embhbbfe1 Direct Subsidized and Unsubsidized Loans by Graduate degree program. 0(%) $1-4,499(%) $4,500-6,199(%) $6,200-7,999(%) $8,000 or more(%) Total Estimates Total60.82.11.92.233.0100% Graduate degree program Master's degree60.52.42.22.632.3100% Post-baccalaureate or post-master's certificate72.32.2 !2.0 !2.1 !21.5100% Doctor's degree - research/scholarship72.92.01.41.422.3100% Doctor's degree - professional practice34.90.3 !0.6 !0.7 !63.5100% Doctor's degree - other57.65.5 !!0.9 !3.3 !32.6100% Not in a degree program98.1‡‡0.8 !!0.9 !!100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: STAFFAMT and GRADDEG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES PowerStats on 10/5/2017. fmbhhb02 Total assistantships amount with (percent >0.5) by Attendance intensity (all schools). Total assistantships amount(%>0.5) Estimates Total7.7 Attendance intensity (all schools) Exclusively full-time11.0 Exclusively part-time3.3 Mixed full-time and part-time8.9 The names of the variables used in this table are: GRASTAMT and ATTNPTRN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES PowerStats on 10/5/2017. fmbhhd13 Institutional tuition & fee waivers 0 by Graduate degree program. Institutional tuition & fee waivers(Avg) Estimates Total676.7 Graduate degree program Master's degree488.6 Post-baccalaureate or post-master's certificate101.4 Doctor's degree - research/scholarship2,463.5 Doctor's degree - professional practice564.3 Doctor's degree - other730.3 ! Not in a degree program223.9 ! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: INSWAIV and GRADDEG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES PowerStats on 10/5/2017. fmbhh6b4 Total loans by Total income (categorical). 0(%) $1-4,499(%) $4,500-6,499(%) $6,500-9,499(%) $9,500 or more(%) Total Estimates Total57.22.92.63.633.7100% Total income (categorical) Less than $5,00051.81.91.92.741.8100% $5,000 - $9,99948.52.6 !3.1 !2.843.0100% $10,000 - $19,99951.82.94.03.537.9100% $20,000 - $29,99951.93.82.93.937.5100% $30,000 - $49,99953.93.73.14.734.6100% $50,000 - $99,99962.43.42.14.028.1100% $100,000 or more72.02.12.33.220.4100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: INCOMEG and TOTLOAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES PowerStats on 10/5/2017. fmbhha1e5 Total aid amount by NPSAS institution type: Graduate (with multiple). 0(%) $1-3,599(%) $3,600-8,199(%) $8,200-16,599(%) $16,600 or more(%) Total Estimates Total28.49.011.814.636.1100% NPSAS institution type: Graduate (with multiple) Public 4-year non-doctorate-granting44.015.412.016.012.6100% Public 4-year doctorate-granting31.68.511.313.435.2100% Private nonprofit 4-year non-doctorate-granting30.112.616.615.425.2100% Private nonprofit 4-year doctorate-granting25.08.011.613.042.4100% Private for-profit 4-year23.09.113.321.632.9100% Attended more than one institution20.210.79.621.837.8100% The names of the variables used in this table are: AIDSECTG and TOTAID. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, 2015-16 National Postsecondary Student Aid Study (NPSAS:16). Computation by NCES PowerStats on 10/5/2017. fmbhkm581 Census region, based on ANSI state code by Four-category school level (primary/middle/high/combined). Northeast(%) Midwest(%) South(%) West(%) Total Estimates Total19.321.239.120.4100% Four-category school level (primary/middle/high/combined) Primary18.721.439.320.6100% Middle20.820.342.316.6100% High19.619.637.823.1100% Combined18.230.234.617.0100% The names of the variables used in this table are: SCHLEV_4CAT and REGION. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Teacher Data File, 2015–16 Computation by NCES PowerStats on 2/26/2018. cgbbkdcb2 Average class size for teachers of departmentalized classes 1, Average class size for teachers of self-contained classes 1 by Census region, based on ANSI state code and Four-category school level (primary/middle/high/combined). Average class size for teachers of departmentalized classes(Avg>0) Average class size for teachers of self-contained classes(Avg>0) Estimates Total26.020.5 Census region, based on ANSI state code Northeast23.419.2 Midwest25.620.7 South25.818.9 West29.223.5 Four-category school level (primary/middle/high/combined) Primary26.521.2 Middle26.814.3 High26.015.0 Combined21.815.7 The names of the variables used in this table are: SCHLEV_4CAT, CLASSZ_S, CLASSZ_D and REGION. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Teacher Data File, 2015–16 Computation by NCES PowerStats on 2/12/2018. bcbbke7b3 Program type of school by Collapsed total K-12 and ungraded enrollment in school. Regular(%) Special program emphasis(%) Special Education(%) Career/Technical/Vocational Education(%) Alternative/other(%) Total Estimates Total93.12.80.71.22.1100% Collapsed total K-12 and ungraded enrollment in school 1-4921.5 !1.0 !!13.0 !‡63.7100% 50-9945.82.0 !!25.72.3 !!24.2100% 100-14965.02.3 !8.2‡24.6100% 150-19980.52.5 !!1.5 !!1.5 !!14.0 !100% 200-34991.64.11.3 !0.9 !1.9100% 350-49994.13.00.6 !1.21.1100% 500-74994.83.60.1 !!0.70.7 !100% 750-99995.03.0‡1.60.4 !!100% 1,000-1,19995.32.8 !‡1.3 !!0.6 !!100% 1,200-1,49996.11.0 !!‡2.4 !0.5 !!100% 1,500-1,99997.0‡‡1.6 !!1.4 !!100% 2,000 or more96.31.1 !!‡1.6 !!1.1 !100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: SCHSIZE and PGMTYPE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Teacher Data File, 2015–16 Computation by NCES PowerStats on 2/26/2018. cgbbkee44 Q8-2 Number of days covered per contract year with (percent >180) by Four-category school level (primary/middle/high/combined) and Q1-3 Teaching any classes. Q8-2 Number of days covered per contract year(%>180) Estimates Total76.5 Four-category school level (primary/middle/high/combined) Primary75.6 Middle78.5 High78.3 Combined69.8 Q1-3 Teaching any classes Yes77.0 The names of the variables used in this table are: SCHLEV_4CAT, T0908 and T0102. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Teacher Data File, 2015–16 Computation by NCES PowerStats on 2/12/2018. bcbbke955 Q1-4 Time spent working as a teacher in any of grades K-12 by General field of main teaching assignment. Full time(%) 3/4 time or more, but less than full-time(%) 1/2 time or more, but less than 3/4 time(%) 1/4 time or more, but less than 1/2 time(%) Less than 1/4 time(%) Total Estimates Total4.616.934.026.018.5100% General field of main teaching assignment Elementary Education10.812.743.821.011.8100% Special Education3.1 !14.736.628.517.0100% Arts & Music1.4 !31.728.423.015.6100% English and Language Arts2.9 !9.935.633.018.5100% English as a Second Language‡16.136.327.216.7100% Foreign Languages4.2 !!27.435.520.012.8100% Health Education3.8 !!23.922.423.226.7100% Mathematics and Computer Science12.512.831.126.417.2100% Natural Sciences11.4 !21.438.316.912.1 !100% Social Sciences12.4 !16.2 !40.610.7 !20.1100% Career or Technical Education6.7 !12.730.926.823.0100% All others‡4.731.032.231.8100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: MNASGN and T0103. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Teacher Data File, 2015–16 Computation by NCES PowerStats on 2/26/2018. cgbbkg7e1 Principal's age 0 by Charter school identifier and Collapsed total K-12 and ungraded enrollment in school. Principal's age(Avg) Estimates Total47.4 Charter school identifier School is a public charter school45.8 School is not a public charter school47.5 Collapsed total K-12 and ungraded enrollment in school 1-4949.5 50-9947.4 100-14949.7 150-19946.7 200-34946.7 350-49946.9 500-74947.4 750-99947.6 1,000-1,19947.3 1,200-1,49947.1 1,500-1,99948.6 2,000 or more48.9 The names of the variables used in this table are: AGE_P, SCHSIZE and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Principal Data File, 2015–16 Computation by NCES PowerStats on 2/12/2018. bcbbkde22 Q1-4 Participated in program for aspiring principals by Q1-7 Highest degree earned. No(%) Yes(%) Total Estimates Total42.357.7100% Q1-7 Highest degree earned Associate degree‡‡100% Bachelor's degree (B.A., B.S., etc.)60.539.5100% Master's degree (M.A., M.A.T., M.B.A., M.Ed., M.S., etc.)42.457.6100% Education specialist or professional diploma (at least one year beyond master's level)41.258.8100% Doctorate or first professional degree (Ph.D., Ed.D., M.D., L.L.B., J.D., D.D.S.)40.359.7100% Do not have a degree‡‡100% ‡ Reporting standards not met. The names of the variables used in this table are: P0106 and P0103. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Principal Data File, 2015–16 Computation by NCES PowerStats on 2/15/2018. bfbbkh443 Q1-1 Number of years of teaching experience prior to becoming a principal 1 by Q1-7 Highest degree earned. Q1-1 Number of years of teaching experience prior to becoming a principal(Avg>0) Estimates Total11.4 Q1-7 Highest degree earned Associate degree‡ Bachelor's degree (B.A., B.S., etc.)10.3 Master's degree (M.A., M.A.T., M.B.A., M.Ed., M.S., etc.)11.5 Education specialist or professional diploma (at least one year beyond master's level)11.7 Doctorate or first professional degree (Ph.D., Ed.D., M.D., L.L.B., J.D., D.D.S.)10.6 Do not have a degree‡ ‡ Reporting standards not met. The names of the variables used in this table are: P0106 and P0100. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Principal Data File, 2015–16 Computation by NCES PowerStats on 2/12/2018. bcbbkeba7f4 Four-category school level (primary/middle/high/combined) by Collapsed total K-12 and ungraded enrollment in school. Primary(%) Middle(%) High(%) Combined(%) Total Estimates Total55.715.420.08.9100% Collapsed total K-12 and ungraded enrollment in school 1-4923.65.2 !!30.540.7100% 50-9936.46.1 !27.929.7100% 100-14942.06.2 !29.522.3100% 150-19948.114.123.614.1100% 200-34964.312.913.69.2100% 350-49971.711.412.34.6100% 500-74967.917.510.14.5100% 750-99946.630.319.14.1100% 1,000-1,19924.932.537.05.6100% 1,200-1,4998.724.159.18.1100% 1,500-1,999‡6.886.06.4100% 2,000 or more‡‡93.35.1 !100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: SCHSIZE and SCHLEV_4CAT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Principal Data File, 2015–16 Computation by NCES PowerStats on 2/15/2018. bfbbkhb85 Q6-5 Annual salary- before taxes and deductions with (percent >99999) by Program type of school and Four-category school level (primary/middle/high/combined). Q6-5 Annual salary- before taxes and deductions(%>99999) Estimates Total39.8 Program type of school Regular39.5 Special program emphasis51.1 Special Education49.3 Career/Technical/Vocational Education38.4 Alternative/Other35.4 Four-category school level (primary/middle/high/combined) Primary38.1 Middle43.6 High48.0 Combined25.1 The names of the variables used in this table are: P0908, PGMTYPE and SCHLEV_4CAT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Principal Data File, 2015–16 Computation by NCES PowerStats on 2/12/2018. bcbbkee571 Estimated number of students per FTE teacher in the school 0 by Collapsed school locale code. Estimated number of students per FTE teacher in the school(Avg) Estimates Total15.5 Collapsed school locale code City16.3 Suburb15.8 Town15.7 Rural14.1 The names of the variables used in this table are: URBANS12 and STU_TCH. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Data File, 2015–16 Computation by NCES PowerStats on 2/12/2018. bcbbke322 Number of students with limited English proficiency within the four year adjusted-cohort by Collapsed school locale code. Zero or one(%) 2 to 5(%) 6 to 15(%) 16 to 30(%) More than 30(%) Total Estimates Total28.631.220.19.510.5100% Collapsed school locale code City14.525.928.013.018.6100% Suburb22.733.721.011.511.1100% Town30.141.315.66.2 !6.7100% Rural51.327.812.95.2 !2.9 !100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: COHORT_LEP and URBANS12. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Data File, 2015–16 Computation by NCES PowerStats on 2/23/2018. cdbbkcad3 Percentage of teachers in the school who are of a racial/ethnic minority 1 by Q4-4a School participates in the National School Lunch Program and Charter school identifier. Percentage of teachers in the school who are of a racial/ethnic minority(Avg>0) Estimates Total26.7 Q4-4a School participates in the National School Lunch Program No27.7 Yes26.7 Charter school identifier School is a public charter school37.6 School is not a public charter school25.7 The names of the variables used in this table are: MINTCH, S0409 and CHARFLAG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Data File, 2015–16 Computation by NCES PowerStats on 2/12/2018. bcbbkee664 Percentage of teachers in the school who are of a racial/ethnic minority by School locale code. 0%(%) >0% to 5%(%) >5% to 15%(%) >15% to 30%(%) More than 30%(%) Total Estimates Total37.420.126.815.7‡100% School locale code City, Large4.613.133.449.0‡100% City, Midsize15.617.135.232.0‡100% City, Small23.224.933.618.4‡100% Suburb, Large27.023.933.615.5‡100% Suburb, Midsize30.927.327.214.5‡100% Suburb, Small41.523.720.814.0‡100% Town, Fringe43.626.520.09.9‡100% Town, Distant52.417.319.311.1‡100% Town, Remote50.119.723.17.0‡100% Rural, Fringe42.723.223.610.5‡100% Rural, Distant64.213.715.56.6‡100% Rural, Remote63.311.221.04.5‡100% ‡ Reporting standards not met. The names of the variables used in this table are: SLOCP12 and MINTCH. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Data File, 2015–16 Computation by NCES PowerStats on 2/23/2018. cdbbkee65 Number of students with limited English proficiency within the four year adjusted-cohort with (percent >10) by School locale code. Number of students with limited English proficiency within the four year adjusted-cohort(%>10) Estimates Total27.2 School locale code City, Large39.7 City, Midsize40.4 City, Small45.8 Suburb, Large33.5 Suburb, Midsize‡ Suburb, Small‡ Town, Fringe‡ Town, Distant11.9 ! Town, Remote31.5 Rural, Fringe18.1 Rural, Distant‡ Rural, Remote15.6 ! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: SLOCP12 and COHORT_LEP. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Teacher and Principal Survey (NTPS), Public School Data File, 2015–16 Computation by NCES PowerStats on 2/12/2018. bcbbke191 Total number of violent incidents recorded with (percent >0.5), Total number of serious violent incidents recorded with (percent >0.5) by School grades offered, School size categories, Urbanicity - Based on Urban-centric location of school and Percent White enrollment (categorical) Total number of violent incidents recorded(%>0.5) Total number of serious violent incidents recorded(%>0.5) Estimates Total68.9 15.5 School grades offered Primary57.2 9.2 Middle88.0 22.9 High89.8 30.5 Combined71.1 15.9 School size categories < 30052.6 7.3 300 - 49963.0 12.7 500 - 99976.0 17.1 1,000 +94.5 34.6 Urbanicity - Based on Urban-centric location of school City74.0 17.4 Suburb66.4 12.8 Town77.7 20.2 Rural62.7 14.6 Percent White enrollment (categorical) More than 95 percent58.0 11.0 More than 80 but less than or equal to 95 percent68.4 14.7 More than 50 but less than or equal to 80 percent66.8 14.5 50 percent or less72.3 17.3 The names of the variables used in this table are: PERCWHT, FR_LVEL, FR_SIZE, SVINC16, VIOINC16 and FR_URBAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 2015ñ16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES PowerStats on 3/20/2018cbcbkcn992 Q32a. Disciplinary occurrences: Racial/ethnic tensions, Q32b. Disciplinary occurrences: Student bullying, Q32c. Disciplinary occurrences: Sexual harassment of students, Q32f. Disciplinary occurrences: Widespread disorder in classrooms, Q32g. Disciplinary occurrences: Student verbal abuse of teachers, Q32h. Disciplinary occurrences: Student acts of disrespect for teachers-not verbal abuse by School grades offered, School size categories, Urbanicity and Percent White enrollment. Q32a. Disciplinary occurrences: Racial/ethnic tensions(%<2.01) Q32b. Disciplinary occurrences: Student bullying(%<2.01) Q32c. Disciplinary occurrences: Sexual harassment of students(%<2.01) Q32f. Disciplinary occurrences: Widespread disorder in classrooms(%<2.01) Q32g. Disciplinary occurrences: Student verbal abuse of teachers(%<2.01) Q32h. Disciplinary occurrences: Student acts of disrespect for teachers-not verbal abuse(%<2.01) Estimates Total1.7 11.9 1.0 2.3 4.8 10.3 School grades offered Primary1.2 ! 8.1 á 1.6 ! 3.6 8.8 Middle3.2 21.8 2.1 4.9 8.2 15.9 High2.3 14.7 2.5 2.6 7.6 12.1 Combinedá 11.0 3.5 !! á á 4.3 ! School size categories < 300á 6.4 á á 3.6 ! 6.4 300 - 4991.4 !! 9.6 0.7 ! 1.3 3.4 9.1 500 - 9992.3 14.0 1.4 3.8 6.0 12.4 1,000 +2.6 22.1 2.4 ! 3.8 7.0 14.4 Urbanicity - Based on Urban-centric location of school City1.8 ! 12.9 0.9 ! 4.9 9.6 15.3 Suburb2.3 10.3 0.9 ! 1.9 3.3 8.1 Town1.7 !! 18.3 1.2 ! 1.5 ! 5.4 14.5 Rural0.9 ! 9.7 1.2 0.6 !! 1.3 ! 5.9 Percent White enrollment (categorical) More than 95 percentá 15.6 á á á 4.6 !! More than 80 but less than or equal to 95 percent1.0 ! 10.8 1.4 ! 0.8 ! 2.1 ! 6.5 More than 50 but less than or equal to 80 percent1.4 ! 11.0 0.9 1.1 3.6 9.9 50 percent or less2.6 12.5 1.0 4.3 7.9 13.7 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.á Reporting standards not met.The names of the variables used in this table are: C0376, C0374, PERCWHT, FR_LVEL, FR_SIZE, C0378, C0382, C0380, FR_URBAN and C0384. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 2015ñ16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES PowerStats on 3/20/2018cbcbkehm793 Q2a. School practice: Written plan for active shooter, Q2b. School practice: Written plan for natural disasters, Q2c. School practice: Written crisis plan for hostages, Q2d. School practice: Written plan for bomb threats, Q2e. School practice: Written plan for chemical, biological, or radiological threats or incidents, Q2f. School practice: Written plan for suicide threat or incident, Q2g. School practice: Written plan for pandemic flu, Q2h. School practice: Written plan for post-crisis reunification of students with their families by School grades offered, School size categories, Urbanicity , and Percent White enrollment. Q2a. School practice: Written plan for active shooter(%=1) Q2b. School practice: Written plan for natural disasters(%=1) Q2c. School practice: Written crisis plan for hostages(%=1) Q2d. School practice: Written plan for bomb threats(%=1) Q2e. School practice: Written plan for chemical, biological, or radiological threats or incidents(%=1) Q2f. School practice: Written plan for suicide threat or incident(%=1) Q2g. School practice: Written plan for pandemic flu(%=1) Q2h. School practice: Written plan for post-crisis reunification of students with their families(%=1) Estimates Total92.4 96.1 60.5 94.1 73.1 84.6 51.0 86.3 School grades offered Primary91.2 96.4 57.1 92.5 71.4 80.7 50.9 87.2 Middle94.0 96.3 62.6 96.5 75.2 89.4 49.5 84.1 High95.3 95.5 67.3 97.3 77.2 91.3 50.9 87.2 Combined91.6 93.5 68.4 94.5 73.1 89.8 55.2 82.6 School size categories < 30089.0 93.1 58.7 88.9 70.4 79.2 43.8 81.7 300 - 49994.3 96.5 59.7 94.8 72.3 85.1 52.4 85.9 500 - 99991.5 97.6 60.5 95.3 73.6 84.8 53.5 87.9 1,000 +96.9 95.3 67.1 98.9 79.6 93.8 52.7 90.7 Urbanicity - Based on Urban-centric location of school City91.3 96.6 63.3 93.6 74.9 85.4 50.5 90.0 Suburb92.3 95.5 57.3 94.9 71.2 85.8 52.0 85.1 Town94.4 96.6 54.5 96.2 75.2 82.0 48.0 84.2 Rural92.6 95.9 64.7 92.8 72.7 83.6 51.6 84.9 Percent White enrollment (categorical) More than 95 percent95.3 95.1 67.8 97.7 67.7 77.1 55.8 86.5 More than 80 but less than or equal to 95 percent92.9 96.6 58.1 93.7 72.4 89.0 53.4 84.2 More than 50 but less than or equal to 80 percent93.8 96.2 56.3 92.8 72.4 82.1 50.4 86.5 50 percent or less90.7 95.8 63.6 94.7 74.8 84.7 49.1 87.3 The names of the variables used in this table are: C0170, C0166, C0173, FR_LVEL, C0158, C0169, FR_SIZE, PERCWHT, C0155, C0157, C0162 and FR_URBAN.. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 2015ñ16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES PowerStats on 3/20/2018cbcbked14 Q23a. Efforts limited by inadequate/lack of teacher training in classroom management by School grades offered Total number of full-time security guards, SROs, or sworn law enforcement officers(Avg>0) Total number of part-time security guards, SROs, or sworn law enforcement officers(Avg>0) Estimates Total2.4 1.7 School grades offered Primary1.4 1.4 Middle2.1 1.8 High3.9 2.7 Combinedá á School size categories < 3001.8 1.8 300 - 4991.6 1.4 500 - 9992.2 1.7 1,000 +3.6 2.5 Urbanicity - Based on Urban-centric location of school City2.7 2.0 Suburb2.5 1.9 Town2.1 1.7 Rural1.7 1.3 Percent White enrollment (categorical) More than 95 percent2.0 ! 1.3 More than 80 but less than or equal to 95 percent2.0 1.7 More than 50 but less than or equal to 80 percent1.9 1.7 50 percent or less2.7 1.8 ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.á Reporting standards not met.The names of the variables used in this table are: SEC_FT16, PERCWHT, FR_LVEL, FR_SIZE, SEC_PT16 and FR_URBAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 2015ñ16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES PowerStats on 3/20/2018cbcbke8a5 Q33a. How often cyberbullying among students by School grades offered, School size categories, Urbanicity - Based on Urban-centric location of school and Percent White enrollment (categorical). Happens at least once a week(%) Happens less than once a week(%) Total Estimates Total12.0 88.0 100% School grades offered Primary4.2 95.8 100% Middle25.6 74.4 100% High25.9 74.1 100% Combined10.6 ! 89.4 100% School size categories < 3007.9 92.1 100% 300 - 4998.5 91.5 100% 500 - 99912.9 87.1 100% 1,000 +27.3 72.7 100% Urbanicity - Based on Urban-centric location of school City12.2 87.8 100% Suburb10.9 89.1 100% Town14.4 85.6 100% Rural12.0 88.0 100% Percent White enrollment (categorical) More than 95 percent11.8 88.2 100% More than 80 but less than or equal to 95 percent12.6 87.4 100% More than 50 but less than or equal to 80 percent11.7 88.3 100% 50 percent or less11.9 88.1 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: C0389, PERCWHT, FR_URBAN, FR_LVEL and FR_SIZE. . The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 2015ñ16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES PowerStats on 3/20/2018cbcbkef51 Q92 Enrolled in language program by Q91 Language spoken by child at home. Yes(%) No(%) Total Estimates Total11.388.7100% Q91 Language spoken by child at home Child has not started to speak‡‡100% English‡‡100% Spanish15.984.1100% A language other than English or Spanish9.590.5100% English and Spanish equally10.090.0100% English and another language equally3.4 !96.6100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: CENGLPRG and CSPEAKX. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012. Computation by NCES PowerStats on 6/1/2018. bfbkeg682 Q22 Hours each week child receives non-relative care 0 by Child currently has disability. Q22 Hours each week child receives non-relative care(Avg) Estimates Total26.3 Child currently has disability Currently has a disability25.2 Does not currently have a disability26.4 The names of the variables used in this table are: DISABLTYX and NCHRS. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012. Computation by NCES PowerStats on 6/1/2018. bfbkee3a3 Number of siblings by Educational attainment of child's parent or guardian. Zero(%) One(%) Two(%) More than two(%) Total Estimates Total32.936.818.911.4100% Educational attainment of child's parent or guardian Less than high school credential28.827.422.321.4100% High school graduate or equivalent32.336.519.112.1100% Vocational/technical school after HS34.236.419.110.4100% College graduate34.940.617.76.7100% Graduate or professional school32.842.516.38.4100% The names of the variables used in this table are: NUMSIBSX and PAR1EDUC. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012. Computation by NCES PowerStats on 6/1/2018. bfbkem8e4 Number of household members younger than age 18 0 by Q133 Total household income. Number of household members younger than age 18(Avg) Estimates Total2.3 Q133 Total household income $0 to $10,0002.4 $10,001 to $20,0002.5 $20,001 to $30,0002.5 $30,001 to $40,0002.5 $40,001 to $50,0002.4 $50,001 to $60,0002.1 $60,001 to $75,0002.2 $75,001 to $100,0002.1 $100,001 to $150,0002.2 $150,001 or more2.2 The names of the variables used in this table are: HHUNDR18X and TTLHHINC. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012. Computation by NCES PowerStats on 6/1/2018. bfbke205 Q97 Language spoken most often at home by first parent or guardian by Work status of child's first parent or guardian and Work status of child's second parent or guardian. English(%) Spanish(%) A language other than English or Spanish(%) English and Spanish equally(%) English and another language equally(%) Total Estimates Total22.438.214.913.611.0100% Work status of child's first parent or guardian Working 35 hours or more per week28.129.718.911.711.6100% Working less than 35 hours per week20.037.211.417.813.6100% Looking for work8.853.17.8 !22.18.3 !100% Not in the labor force18.946.212.512.59.9100% Work status of child's second parent or guardian Working 35 hours or more per week26.035.214.413.211.2100% Working less than 35 hours per week13.442.311.215.617.5100% Looking for work24.041.112.4 !15.3 !7.2 !100% Not in the labor force15.838.022.611.212.4100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. The names of the variables used in this table are: PAR2EMPL, PAR1EMPL and P1SPEAK. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012. Computation by NCES PowerStats on 6/1/2018. bfbke391 Q95 Enrolled in language program by Q94 Language spoken by child at home. Yes(%) No(%) Total Estimates Total11.089.0100% Q94 Language spoken by child at home Child has not started to speak‡‡100% English‡‡100% Spanish9.190.9100% A language other than English or Spanish21.4 !78.6100% English and Spanish equally9.590.5100% English and another language equally7.3 !92.7100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: CENGLPRG and CSPEAKX. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 20126 Computation by NCES PowerStats on 6/4/2018. efbkfad32 Q22 Hours each week child receives non-relative care 0 by Child currently has disability. Q22 Hours each week child receives non-relative care(Avg) Estimates Total27.0 Child currently has disability Currently has a disability26.1 Does not currently have a disability27.1 The names of the variables used in this table are: DISABLTYX and NCHRS. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 20126 Computation by NCES PowerStats on 6/4/2018. efbkfcf73 Number of siblings by Educational attainment of child's parent or guardian. 0(%) 1(%) 2(%) 3 or more(%) Total Estimates Total29.037.820.612.7100% Educational attainment of child's parent or guardian Less than high school credential21.321.131.825.8100% High school graduate or equivalent32.437.120.69.8100% Vocational/technical school after HS29.036.420.614.0100% College graduate30.244.216.88.8100% Graduate or professional school28.644.617.39.5100% The names of the variables used in this table are: NUMSIBSX and PAR1EDUC. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 20126 Computation by NCES PowerStats on 6/4/2018. efbkff914 Number of household members younger than age 18 0 by Q138 Total household income. Number of household members younger than age 18(Avg) Estimates Total2.3 Q138 Total household income $0 to $10,0002.5 $10,001 to $20,0002.3 $20,001 to $30,0002.4 $30,001 to $40,0002.4 $40,001 to $50,0002.3 $50,001 to $60,0002.3 $60,001 to $75,0002.3 $75,001 to $100,0002.1 $100,001 to $150,0002.1 $150,001 or more2.1 The names of the variables used in this table are: HHUNDR18X and TTLHHINC. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 20126 Computation by NCES PowerStats on 6/4/2018. efbkfkfb5 Q105 Language spoken most often at home by first parent or guardian by Work status of child's first parent or guardian and Work status of child's second parent or guardian. English(%) Spanish(%) A language other than English or Spanish(%) English and Spanish equally(%) English and another language equally(%) Total Estimates Total24.035.913.913.812.4100% Work status of child's first parent or guardian Working 35 hours or more per week27.728.416.612.115.1100% Working less than 35 hours per week25.540.24.5 !21.18.7100% Looking for work44.232.210.9 !10.2 !!‡100% Not in the labor force16.046.113.314.110.5100% Work status of child's second parent or guardian Working 35 hours or more per week26.839.012.712.49.1100% Working less than 35 hours per week20.336.37.2 !9.5 !26.7 !100% Looking for work22.5 !!34.1 !27.5 !11.8 !!‡100% Not in the labor force16.429.524.412.717.1100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: PAR2EMPL, PAR1EMPL and P1SPEAK. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 20126 Computation by NCES PowerStats on 6/4/2018. efbkfn1a1 E40 Attended a religious event in the past month by E31 Time spent doing homework. Yes(%) No(%) Total Estimates Total54.345.7100% E31 Time spent doing homework Less than once a week47.752.3100% 1 to 2 days a week50.549.5100% 3 to 4 days a week57.043.0100% 5 or more days a week55.844.2100% Never36.463.6100% Child does not have homework36.963.1100% The names of the variables used in this table are: FOGROUPX and FHHOME. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012. Computation by NCES PowerStats on 8/28/2018. ckhbkfd362 Child's age 0 by E58 Enrolled in language program. Child's age(Avg) Estimates Total11.0 E58 Enrolled in language program Yes10.0 No11.2 The names of the variables used in this table are: AGE2011 and CENGLPRG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012. Computation by NCES PowerStats on 8/28/2018. ckhbkfgdf3 E40 Visited a library in the past month by E105 Total household income. Yes(%) No(%) Total Estimates Total39.560.5100% E105 Total household income $0 to $10,00045.654.4100% $10,001 to $20,00038.861.2100% $20,001 to $30,00039.061.0100% $30,001 to $40,00039.160.9100% $40,001 to $50,00038.161.9100% $50,001 to $60,00038.661.4100% $60,001 to $75,00039.660.4100% $75,001 to $100,00036.163.9100% $100,001 to $150,00041.158.9100% $150,001 or more40.459.6100% The names of the variables used in this table are: TTLHHINC and FOLIBRAYX. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012. Computation by NCES PowerStats on 8/28/2018. ckhbkf684 Number of siblings 0 by E26 Adult in child's household has attended a parent-teacher conference. Number of siblings(Avg) Estimates Total1.4 E26 Adult in child's household has attended a parent-teacher conference Yes1.4 No1.3 The names of the variables used in this table are: NUMSIBSX and FSATCNFN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012. Computation by NCES PowerStats on 8/28/2018. ckhbkf955 Child currently has disability by Census region where child lives. Currently has a disability(%) Does not currently have a disability(%) Total Estimates Total17.382.7100% Census region where child lives Northeast18.082.0100% South17.282.8100% Midwest20.279.8100% West14.485.6100% The names of the variables used in this table are: CENREG and DISABLTYX. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012. Computation by NCES PowerStats on 8/28/2018. ckhbkfc51 Percentage of students in kindergarten through grade 12 whose parents reported school-initiated communication practices, by method of communication and selected school, student, and family characteristics: 2015-16 E32 Child's family received notes or emails from teachers or school administrators(%=1) E32 Child's family received newsletters from the school(%=1) E32 Child's family received phone calls from the school(%=1) Estimates Total62.2 89.4 42.2 E3 District-assigned school Public, assigned60.6 88.3 42.1 Public, chosen64.1 90.4 45.3 Type of school Private, religious69.0 95.8 36.6 Private, nonreligious80.1 96.9 41.8 Total school enrollment of students Under 30067.1 89.7 45.6 300-59964.5 91.2 42.6 600-99962.2 89.2 41.8 1,000 or more56.9 86.9 40.6 Zip code classification by community type City58.9 88.1 45.5 Suburban65.1 92.1 39.9 Town60.5 85.9 42.8 Rural61.3 86.0 41.9 The names of the variables used in this table are: S16TYPE, S16NUMST, FSMEMO, DISTASSI, FSNOTESX, ZIPLOCL and FSPHONCHX. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES PowerStats on 8/24/2018.cehbkmg642 Hours spent doing homework for students who are assigned homework and percent of students assigned no homework, by selected school, student, and family characteristics: 2015-16 E36 Hours spent doing homework(Avg>0) E36 Hours spent doing homework(Median>0) Percent of students with no homework outside of school(%>4) Estimates Total5.5 4.0 6.1 Student's sex Male5.1 4.0 7.4 Female5.9 5.0 4.6 Race and ethnicity of child White, non-Hispanic5.1 4.0 6.1 Black, non-Hispanic6.2 5.0 6.8 Hispanic5.4 4.0 5.7 All other races and multiple races, non-Hispanic6.6 5.0 5.7 E1 Current grade or year of school Kindergarten-2nd grade4.0 3.0 6.6 3rd-5th grade4.8 4.0 3.3 6th-8th grade5.4 4.0 5.1 9th-12th grade7.6 6.0 8.6 The names of the variables used in this table are: FHWKHRS, FHHOME, CSEX, GRADE and RACEETHN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES PowerStats on 8/24/2018.cehbknea83 Percentage of students in kindergarten through grade 12 whose parents reported that students do homework outside of school, by selected school, student, and family characteristics: 2015-16 Yes(%) No(%) Total Estimates Total93.9 6.1 100% E3 District-assigned school Public, assigned93.8 6.2 100% Public, chosen94.1 5.9 100% Type of school Private, religious95.4 4.6 100% Private, nonreligious88.3 11.7 ! 100% Total school enrollment of students Under 30089.0 11.0 100% 300-59994.3 5.7 100% 600-99995.1 4.9 100% 1,000 or more94.3 5.7 100% Zip code classification by community type City93.1 6.9 100% Suburban95.4 4.6 100% Town91.4 8.6 100% Rural93.0 7.0 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: S16NUMST, DISTASSI, FHHOME, ZIPLOCL and S16TYPE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES PowerStats on 8/24/2018.cehbkmh1a4 Percentage of students in kindergarten through grade 12 whose parents reported participation in various activities, by selected school, student, and family characteristics: 2015-16 E44 Visited a library in the past month(%=1) E44 Visited a bookstore in the past month(%=1) E44 Gone to a play in the past month(%=1) Estimates Total34.2 33.6 34.1 Zip code classification by community type City36.9 35.3 32.5 Suburban34.8 35.1 36.7 Town31.0 26.3 28.8 Rural29.0 29.7 32.4 E1 Current grade or year of school Kindergarten-2nd grade41.1 34.6 32.4 3rd-5th grade42.6 38.7 35.0 6th-8th grade33.8 33.1 35.4 9th-12th grade21.4 28.8 33.8 Parent or guardian highest education including same sex partners Less than high school credential29.7 21.6 23.5 High school graduate or equivalent29.0 22.9 27.6 Vocational/technical school after HS31.7 32.3 30.8 College graduate35.8 40.0 39.1 Graduate or professional school44.4 45.4 45.1 The names of the variables used in this table are: FOCONCRTX, ZIPLOCL, FOBOOKSTX, GRADE, PARGRADEX and FOLIBRAYX. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES PowerStats on 8/24/2018.cehbknpkdc5 Percentage of students in grades 6 through 12 whose parents reported expectations of specific educational attainment levels, by selected school, student, and family characteristics: 2015-16 Complete less than a high school diploma(%) Graduate from high school(%) Attend a vocational or technical school after high school(%) Attend two or more years of college(%) Earn a Bachelor's degree(%) Earn a graduate degree or professional degree beyond a Bachelor's(%) Total Estimates Total1.1 8.6 7.6 14.8 28.9 39.0 100% E3 District-assigned school Public, assigned1.1 9.4 8.4 16.1 28.8 36.2 100% Public, chosen1.3 ! 8.1 6.8 12.3 27.0 44.6 100% Total school enrollment of students Under 3002.3 ! 11.7 9.9 17.7 25.7 32.7 100% 300-5990.6 ! 11.9 9.4 16.1 25.4 36.6 100% 600-9991.2 9.6 7.2 14.3 29.2 38.5 100% 1,000 or more1.1 6.0 6.7 13.9 30.8 41.5 100% Student's sex Male1.4 9.6 10.4 15.0 29.3 34.3 100% Female0.8 7.6 4.6 14.5 28.4 44.0 100% Student's grade level 6th-8th grade1.0 9.0 6.6 13.5 28.1 41.8 100% 9th-12th grade1.2 8.4 8.5 15.7 29.5 36.7 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: S16NUMST, DISTASSI, SEFUTUREX, CSEX and GRADE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES PowerStats on 8/24/2018.cehbkm1f1 Census region by Age category. Northeast(%) South(%) Midwest(%) West(%) Total Estimates Total17.936.322.423.5100% Age category 16 to 24 years old19.333.823.523.4100% 25 to 34 years old17.235.322.325.2100% 35 to 44 years old16.736.921.425.1100% 45 to 54 years old18.636.621.523.3100% 55 to 66 years old17.937.823.620.7100% The names of the variables used in this table are: AGECAT and CENREG. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Adult Training and Education Survey (ATES), 2016. Computation by NCES PowerStats on 7/26/2018. cggbkkd822 Q78 Age 0 by Percent of people in zip code who were Black or Hispanic. Q78 Age(Avg) Estimates Total41.8 Percent of people in zip code who were Black or Hispanic Less than 6 percent43.2 6 to 15 percent41.9 16 to 40 percent41.3 41 percent or more41.4 The names of the variables used in this table are: XXAGE and ZIPBLHI2. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Adult Training and Education Survey (ATES), 2016. Computation by NCES PowerStats on 7/27/2018. chgbkafe3 Q73 Sex by Race-ethnicity. Male(%) Female(%) Total Estimates Total46.453.6100% Race-ethnicity White, non-Hispanic47.452.6100% Black, non-Hispanic42.957.1100% Hispanic45.854.2100% All other and multiple races, non-Hispanic45.854.2100% The names of the variables used in this table are: XXSEX and RACEETHN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Adult Training and Education Survey (ATES), 2016. Computation by NCES PowerStats on 7/27/2018. chgbka9a4 Q52 Number of jobs held in last week 1 by Level of postsecondary certificate. Q52 Number of jobs held in last week(Avg>0) Estimates Total1.1 Level of postsecondary certificate Subbaccalaureate certificate1.2 Post-baccalaureate certificate1.2 No certificate1.1 The names of the variables used in this table are: EEJOB and CTLEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Adult Training and Education Survey (ATES), 2016. Computation by NCES PowerStats on 7/27/2018. chgbkad35 Q65 Type of employee in current or last job by Q50 Employed for pay last week. An employee of a private company, business, or individual, for wages, salary, or commission(%) A local (city, county, etc.), state, or federal government employee(%) Self-employed in own business, professional practice, or farm(%) Working without pay for family business or farm(%) Total Estimates Total74.216.58.70.6100% Q50 Employed for pay last week Yes74.317.28.40.2100% No74.114.99.41.6100% The names of the variables used in this table are: EEEMPLO and EEMAIN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is WTA000. Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Adult Training and Education Survey (ATES), 2016. Computation by NCES PowerStats on 7/27/2018. chgbka561Prior degree: 4-year bachelor's degree by Family status, 12 months after BA completion. No(%)Yes(%)TotalEstimatesTotal94.06.0100%Family status, 12 months after BA completion (considering only dependent children) Unmarried, no dependent children95.14.9100% Unmarried with dependent children89.510.5100% Married, no dependent children91.68.4100% Married with dependent children89.910.1100%The names of the variables used in this table are: DEGPRBA and B1MARCHA. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, B&B:17 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 6/10/2019.bafbmmc22Age, as of BA completion 0 by Most recent job, within 12 months after BA completion: Employer offered any benefits. Age, as of BA completion(Avg)EstimatesTotal25.9Most recent job, within 12 months after BA completion: Employer offered any benefits No24.8 Yes26.5The names of the variables used in this table are: B1AGEATBA and B1BENANYRCNT. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, B&B:17 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 6/10/2019.bafbmmed3Cumulative Pell amount 1 by Teacher pipeline status, as of B&B:16/17 interview. Cumulative Pell amount(Median>0)EstimatesTotal16,031.0Teacher pipeline status, as of B&B:16/17 interview Has not taught, has not prepared, and has not considered teaching15,768.0 Has not taught, has not prepared, and has considered teaching15,400.0 Has not taught, has prepared, and is not certified15,405.0 Has not taught, has prepared, and is certified19,684.0 Has taught at the pre-K-12th grade level17,150.0The names of the variables used in this table are: PELLCUM and B1PIPLN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, B&B:17 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 6/10/2019.bafbmmc74Number of institutions attended before BA completion with (percent >2) by Dependency status. Number of institutions attended before BA completion(%>2)EstimatesTotal18.7Dependency status Dependent student8.0 Independent student33.1The names of the variables used in this table are: B1NUMINST and DEPEND. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, B&B:17 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 6/10/2019.bafbmmdf5Cumulative Pell amount with (percent <10000) by Veteran status. Cumulative Pell amount(%<10000)EstimatesTotal66.0Veteran status Not a veteran66.5 Veteran54.0The names of the variables used in this table are: PELLCUM and VETERAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, B&B:17 Baccalaureate and Beyond Longitudinal Study.Computation by NCES PowerStats on 6/10/2019.bafbmmf511Gender by Accepted at first choice college. Male(%)Female(%)TotalEstimatesTotal49.750.3100%Accepted at first choice college Yes/attended44.056.0100% Yes/didn't attend41.158.9100% No/not accepted51.148.9100%The names of the variables used in this table are: SEX and SY14B1. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is BYWT.Source: U.S. Department of Education, National Center for Education Statistics, High School and Beyond (HS&B).Computation by NCES PowerStats on 5/22/2019.ccebmebc52High school academic GPA 0 by Urbanicity of high school area. High school academic GPA(Avg)EstimatesTotal2.5Urbanicity of high school area Urban2.3 Suburban2.5 Rural2.6The names of the variables used in this table are: ACADGPA and HSURBAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is PSEWT1.Source: U.S. Department of Education, National Center for Education Statistics, High School and Beyond (HS&B).Computation by NCES PowerStats on 5/22/2019.ccebme723Ever attended 4-year college as undergraduate by Senior test quartile. No(%)Yes(%)TotalEstimatesTotal40.259.8100%Senior test quartile Low69.430.6100% Low/Medium59.440.6100% Medium/High41.258.8100% High16.583.5100%The names of the variables used in this table are: SRTESTQ and ATTFOUR. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is PSEWT1.Source: U.S. Department of Education, National Center for Education Statistics, High School and Beyond (HS&B).Computation by NCES PowerStats on 5/22/2019.ccebmefecf4Socioeconomic status percentile 0 by Applied for student financial aid at first choice. Socioeconomic status percentile(Avg)EstimatesTotal50.3Applied for student financial aid at first choice Yes/offered aid51.7 Yes/didn't receive aid63.2 No/did not apply64.0The names of the variables used in this table are: SY14C1 and BYSES. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is BYWT.Source: U.S. Department of Education, National Center for Education Statistics, High School and Beyond (HS&B).Computation by NCES PowerStats on 6/12/2019.bcfbmh0e5Senior test quartile by Annual earnings in 1992. Low(%)Low/Medium(%)Medium/High(%)High(%)TotalEstimatesTotal13.721.628.436.3100%Annual earnings in 1992 Less than $1,50021.924.825.427.9100% $1,500 to $4,99914.921.931.431.8100% $5,000 to $9,99910.920.728.739.7100% $10,000 to $24,99910.520.729.739.1100% $25,000 or more8.516.527.147.9100%The names of the variables used in this table are: SRTESTQ and EARN92. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is PSEWT1.Source: U.S. Department of Education, National Center for Education Statistics, High School and Beyond (HS&B).Computation by NCES PowerStats on 5/22/2019.ccebmed71 Gender (2000) by Housing status (2000). Male(%) Female(%) Total Estimates Total48.151.9100% Housing status (2000) Own/buying living quarters42.557.5100% Rent from someone, not a relative49.650.4100% Rent from a relative52.847.2100% Live in residence without paying rent51.948.1100% The names of the variables used in this table are: F4SEX and F4HHOSE. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is F3PNLWT. Source: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88). Computation by NCES PowerStats on 3/29/2019. cmcbmdaa2 Income of spouse/partner in 1999 0 by Attended any postsecondary education since high school (2000). Income of spouse/partner in 1999(Avg) Estimates Total26,607.6 Attended any postsecondary education since high school (2000) No25,571.7 Yes27,962.2 The names of the variables used in this table are: F4HINCS and F4EANY. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is F3PNLWT. Source: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88). Computation by NCES PowerStats on 3/29/2019. cmcbmd6c3 Degree/certificate earned since high school (2000) by Marital status (2000). No(%) Yes(%) Total Estimates Total40.359.7100% Marital status (2000) Single, never married38.161.9100% Married39.760.3100% Divorced70.030.0100% Separated62.537.5100% Widowed‡‡100% In marriage-like relationship48.551.5100% ‡ Reporting standards not met. The names of the variables used in this table are: F4EDEGR and F4GMRS. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is F3PNLWT. Source: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88). Computation by NCES PowerStats on 3/29/2019. cmcbmd604 Income of respondent in 1999 (asked in 2000) 0 by Race of respondent-multiple choice (2000). Income of respondent in 1999 (asked in 2000)(Avg) Estimates Total24,658.3 Race of respondent-multiple choice (2000) American Indian or Alaska Native19,431.7 Asian or Pacific Islander26,228.0 Black, not Hispanic20,759.0 White, not Hispanic25,705.9 Hispanic or Latino22,568.5 More than one race21,320.7 The names of the variables used in this table are: F4RACEM and F4HI99. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is F3PNLWT. Source: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88). Computation by NCES PowerStats on 3/29/2019. cmcbmdee55 Work or postsecondary education status (2000) by High school completion status as of 2000. Work for pay not study(%) Study not work for pay(%) Work for pay and study(%) Neither work nor study(%) Total Estimates Total69.74.116.99.3100% High school completion status as of 2000 Had a diploma or equivalent69.74.318.08.0100% Working toward a diploma/equivalent51.27.0 !‡38.1100% Neither71.9‡0.7 !!27.2100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate. !! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate. ‡ Reporting standards not met. The names of the variables used in this table are: F4STATUS and F4HSDIPL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box. The weight variable used in this table is F3PNLWT. Source: U.S. Department of Education, National Center for Education Statistics, National Education Longitudinal Study of 1988 (NELS:88). Computation by NCES PowerStats on 3/29/2019. cmcbmd831Total number of violent incidents recorded with (percent >0), Total number of serious violent incidents recorded with (percent >0) by School grades offered - based on 03-04 SASS frame variables (School). Total number of violent incidents recorded(%>0) Total number of serious violent incidents recorded(%>0) Estimates Total81.4 18.3 School grades offered - based on 03-04 SASS frame variables (School) Primary74.2 13.3 Middle93.6 24.4 Secondary95.9 29.4 Combined84.7 23.9 The names of the variables used in this table are: SVINC04, FR_LVEL and VIOINC04. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 2003–04 School Survey on Crime and Safety (SSOCS), 2004.Computation by NCES PowerStats on 10/16/2019. bgmbmp262Q17e1i. Number of threats of attack with a weapon with (percent >0), Q17e2i. Number of threats of attack without a weapon with (percent >0) by School size categories - based on 03-04 SASS frame (School). Q17e1i. Number of threats of attack with a weapon(%>0) Q17e2i. Number of threats of attack without a weapon(%>0) Estimates Total8.6 53.0 School size categories - based on 03-04 SASS frame (School) Less than 3006.4 37.6 300 to 4996.7 52.3 500 to 9998.9 56.1 1,000 or more17.7 77.0 The names of the variables used in this table are: FR_SIZE, Q17E1_1 and Q17E2_1. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 2003–04 School Survey on Crime and Safety (SSOCS), 2004.Computation by NCES PowerStats on 10/16/2019. bgmbmpea3Q5a. Parent involvement: Parent participates in open house or back to school night by Urbanicity - from 03-04 SASS frame variable (School). 0-25%(%) 26-50%(%) 51-75%(%) 76-100%(%) School does not offer(%) Total Estimates Total5.5 16.8 31.0 44.0 2.7 100% Urbanicity - from 03-04 SASS frame variable (School) City5.8 20.4 39.0 33.4 1.4 !! 100% Urban Fringe4.4 13.1 27.6 54.3 0.6 ! 100% Town7.0 17.5 32.4 40.8 2.3 100% Rural5.7 17.7 27.6 42.6 6.3 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.The names of the variables used in this table are: Q5A and FR_LOC4. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 2003–04 School Survey on Crime and Safety (SSOCS), 2004.Computation by NCES PowerStats on 10/16/2019. bgmbmp6b4Q14a. Efforts limited by inadequate/lack of teacher training in classroom management by School size categories - based on 03-04 SASS frame (School). Limit in major way(%) Limit in minor way(%) Does not limit(%) Total Estimates Total3.2 33.3 63.5 100% School size categories - based on 03-04 SASS frame (School) Less than 3003.2 ! 28.9 67.9 100% 300 to 4992.8 ! 32.0 65.2 100% 500 to 9992.8 34.6 62.6 100% 1,000 or more5.7 42.1 52.2 100% ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: FR_SIZE and Q14A. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 2003–04 School Survey on Crime and Safety (SSOCS), 2004.Computation by NCES PowerStats on 10/16/2019. bgmbmpe75Total number of incidents recorded with (percent >0), Total number of incidents recorded 1, Total number of incidents recorded 1 by School grades offered - based on 03-04 SASS frame variables (School). Total number of incidents recorded(%>0) Total number of incidents recorded(Avg>0) Total number of incidents recorded(Median>0) Estimates Total88.5 30.0 15.0 School grades offered - based on 03-04 SASS frame variables (School) Primary83.3 18.5 10.0 Middle96.5 48.2 27.0 Secondary98.6 54.5 33.0 Combined92.5 21.1 11.0 ! ! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.The names of the variables used in this table are: INCID04 and FR_LVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 2003–04 School Survey on Crime and Safety (SSOCS), 2004.Computation by NCES PowerStats on 10/16/2019. bgmbmp5c1Number of violent incidents reported by School grade offered, based on 98-99 CCD. None(%)Any(%)TotalEstimatesTotal28.871.2100%School grade offered, based on 98-99 CCD Elementary39.061.0100% Middle12.887.2100% Secondary8.391.7100% Combined23.476.6100%The names of the variables used in this table are: VIOLINC and LEVEL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1999–2000 School Survey on Crime and Safety (SSOCS), 2000.Computation by NCES PowerStats on 12/3/2019.dpbmp2e2Q16C2_1. Number of attacks without a weapon with (percent >0), Q16D2_1. Number of threats of attack without a weapon with (percent >0) by Urbanicity, based on 98-99 CCD. Q16C2_1. Number of attacks without a weapon(%>0)Q16D2_1. Number of threats of attack without a weapon(%>0)EstimatesTotal63.752.3Urbanicity, based on 98-99 CCD City69.457.6 Urban fringe59.149.0 Town68.657.1 Rural62.149.8The names of the variables used in this table are: Q16D2_1, Q16C2_1 and URBAN. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1999–2000 School Survey on Crime and Safety (SSOCS), 2000.Computation by NCES PowerStats on 12/3/2019.dpbmp6c3Q19B. Disciplinary occurrences: Student bullying with (percent <3), Q19E. Disciplinary occurrences: Student acts of disrespect for teachers with (percent <3), Q19F. Disciplinary occurrences: undesirable gang activities with (percent <5) by Urbanicity, based on 98-99 CCD. Q19B. Disciplinary occurrences: Student bullying(%<3)Q19E. Disciplinary occurrences: Student acts of disrespect for teachers(%<3)Q19F. Disciplinary occurrences: undesirable gang activities(%<5)EstimatesTotal29.319.418.7Urbanicity, based on 98-99 CCD City32.224.831.7 Urban fringe28.919.217.4 Town31.021.515.3 Rural26.814.811.5The names of the variables used in this table are: Q19B, Q19E, URBAN and Q19F. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1999–2000 School Survey on Crime and Safety (SSOCS), 2000.Computation by NCES PowerStats on 12/3/2019.dpbmpnfb4Q4C. School program: Counseling, social work, psychological, or therapeutic activity for students with (percent =1), Q4B. School program: Behavioral modification for students with (percent =1) by Total students (categorical). Q4C. School program: Counseling, social work, psychological, or therapeutic activity for students(%=1)Q4B. School program: Behavioral modification for students(%=1)EstimatesTotal65.665.5Total students (categorical) Less Than 30057.158.7 300 To 49965.064.8 500 To 99969.768.7 1,000 Or More72.771.5 {Not applicable}‡‡‡ Reporting standards not met. The names of the variables used in this table are: Q4C, Q4B and ENROLL. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1999–2000 School Survey on Crime and Safety (SSOCS), 2000.Computation by NCES PowerStats on 12/3/2019.dpbmp6f5Q1A. School practice: Require visitor check in with (percent =1), Q1B. School practice: Control access to buildings during school hours with (percent =1), Q1G. School practice: Close campus for lunch with (percent =1) by School grade offered, based on 98-99 CCD. Q1A. School practice: Require visitor check in(%=1)Q1B. School practice: Control access to buildings during school hours(%=1)Q1G. School practice: Close campus for lunch(%=1)EstimatesTotal96.674.664.6School grade offered, based on 98-99 CCD Elementary97.177.357.5 Middle97.275.580.7 Secondary95.970.772.4 Combined90.754.166.1The names of the variables used in this table are: Q1A, Q1G, LEVEL and Q1B. The variable names are unique identifiers. To locate these variables, enter the variable name in the search box.The weight variable used in this table is WTA000.Source: U.S. Department of Education, National Center for Education Statistics, 1999–2000 School Survey on Crime and Safety (SSOCS), 2000.Computation by NCES PowerStats on 12/3/2019.dpbmpp211Average Cumulative amount borrowed for undergrad by Class level for years 1996, 2000, 2004, 2008, 2012 and 2016 Cumulative amount borrowed for undergrad(Avg)EstimatesTotal19962,856.720005,247.520045,156.120087,110.420129,767.4201610,317.7Class level1st year undergraduate19961,611.620002,647.520042,713.720083,931.520125,719.920165,812.82nd year undergraduate19962,697.920004,340.020044,076.520086,203.320128,713.120168,817.13rd year undergraduate19965,142.620007,989.820047,256.3200812,128.7201214,121.7201613,994.54th year undergraduate19966,156.6200010,052.2200411,342.3200813,607.0201218,690.7201620,045.25th year undergraduate19965,915.920009,403.0200414,457.7200819,476.3201226,832.4201622,825.5Senior/Graduated during survey year19967,447.0200012,915.820042,725.420083,936.720126,446.820167,623.0Average Cumulative amount borrowed for undergrad by Class level for years 1996, 2000, 2004, 2008, 2012 and 2016 Cumulative amount borrowed for undergrad(Avg)EstimatesTotal19962,856.720005,247.520045,156.120087,110.420129,767.4201610,317.7Class level1st year undergraduate19961,611.620002,647.520042,713.720083,931.520125,719.920165,812.82nd year undergraduate19962,697.920004,340.020044,076.520086,203.320128,713.120168,817.13rd year undergraduate19965,142.620007,989.820047,256.3200812,128.7201214,121.7201613,994.54th year undergraduate19966,156.6200010,052.2200411,342.3200813,607.0201218,690.7201620,045.25th year undergraduate19965,915.920009,403.0200414,457.7200819,476.3201226,832.4201622,825.5Senior/Graduated during survey year19967,447.0200012,915.820042,725.420083,936.720126,446.820167,623.0Standard Error (BRR)Total1996{|1996|{42.36|2000{|2000|{48.49|2004{|2004|{54.95|2008{|2008|{52.48|2012{|2012|{76.64|2016{|2016|{76.43|Class level1st year undergraduate1996{|1996|{45.48|2000{|2000|{63.74|2004{|2004|{68.90|2008{|2008|{65.99|2012{|2012|{90.39|2016{|2016|{100.66|2nd year undergraduate1996{|1996|{90.03|2000{|2000|{94.67|2004{|2004|{87.05|2008{|2008|{106.74|2012{|2012|{140.72|2016{|2016|{136.15|3rd year undergraduate1996{|1996|{119.47|2000{|2000|{179.46|2004{|2004|{121.81|2008{|2008|{168.04|2012{|2012|{212.23|2016{|2016|{224.32|4th year undergraduate1996{|1996|{200.32|2000{|2000|{192.09|2004{|2004|{199.11|2008{|2008|{184.28|2012{|2012|{281.19|2016{|2016|{290.09|5th year undergraduate1996{|1996|{725.21|2000{|2000|{747.38|2004{|2004|{526.35|2008{|2008|{561.67|2012{|2012|{877.37|2016{|2016|{1,090.53|Senior/Graduated during survey year1996{|1996|{218.19|2000{|2000|{231.17|2004{|2004|{176.52|2008{|2008|{268.16|2012{|2012|{435.74|2016{|2016|{1,066.76|Relative Standard Error (%)Total19961.4820000.9220041.0720080.7420120.7820160.74Class level1st year undergraduate19962.8220002.4120042.5420081.6820121.5820161.732nd year undergraduate19963.3420002.1820042.1420081.7220121.6220161.543rd year undergraduate19962.3220002.2520041.6820081.3920121.5020161.604th year undergraduate19963.2520001.9120041.7620081.3520121.5020161.455th year undergraduate199612.2620007.9520043.6420082.8820123.2720164.78Senior/Graduated during survey year19962.9320001.7920046.4820086.8120126.76201613.99Weighted Sample Sizes (n/1,000s)Total199616,677.9200016,579.2200419,053.8200820,762.3201223,055.4201619,532.3Class level1st year undergraduate19968,498.120005,910.620047,012.820088,738.320129,437.120167,805.02nd year undergraduate19963,646.020004,154.520044,942.220085,521.320125,958.220165,543.33rd year undergraduate19961,761.220002,126.820042,635.320082,640.420122,810.820162,416.14th year undergraduate19961,005.220001,279.720042,484.520082,653.520123,129.220163,303.25th year undergraduate199698.62000134.52004542.82008394.62012491.92016244.4Senior/Graduated during survey year1996918.920001,443.620041,436.12008814.120121,228.32016220.3Average Cumulative amount borrowed for undergrad by Class level for years 1996, 2000, 2004, 2008, 2012 and 2016 Cumulative amount borrowed for undergrad(Avg)Amt.95% CIEstimatesTotal19962,856.7[2,771.61-2,941.82]20005,247.5[5,150.11-5,344.95]20045,156.1[5,047.72-5,264.43]20087,110.4[7,006.91-7,213.88]20129,767.4[9,616.23-9,918.50]201610,317.7[10,166.95-10,468.40]Class level1st year undergraduate19961,611.6[1,520.18-1,702.93]20002,647.5[2,519.44-2,775.54]20042,713.7[2,577.87-2,849.60]20083,931.5[3,801.41-4,061.69]20125,719.9[5,541.69-5,898.18]20165,812.8[5,614.31-6,011.29]2nd year undergraduate19962,697.9[2,517.08-2,878.81]20004,340.0[4,149.78-4,530.16]20044,076.5[3,904.80-4,248.13]20086,203.3[5,992.85-6,413.84]20128,713.1[8,435.58-8,990.58]20168,817.1[8,548.64-9,085.61]3rd year undergraduate19965,142.6[4,902.60-5,382.62]20007,989.8[7,629.22-8,350.30]20047,256.3[7,016.07-7,496.49]200812,128.7[11,797.34-12,460.07]201214,121.7[13,703.15-14,540.18]201613,994.5[13,552.15-14,436.87]4th year undergraduate19966,156.6[5,754.20-6,559.07]200010,052.2[9,666.32-10,438.14]200411,342.3[10,949.65-11,734.94]200813,607.0[13,243.62-13,970.43]201218,690.7[18,136.24-19,245.25]201620,045.2[19,473.11-20,617.23]5th year undergraduate19965,915.9[4,458.93-7,372.83]20009,403.0[7,901.48-10,904.45]200414,457.7[13,419.77-15,495.71]200819,476.3[18,368.71-20,583.94]201226,832.4[25,102.26-28,562.62]201622,825.5[20,674.93-24,975.98]Senior/Graduated during survey year19967,447.0[7,008.64-7,885.32]200012,915.8[12,451.42-13,380.25]20042,725.4[2,377.33-3,073.51]20083,936.7[3,407.87-4,465.48]20126,446.8[5,587.47-7,306.04]20167,623.0[5,519.39-9,726.68]199620002004200820122016 Cumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergrad (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)EstimatesTotal2,856.75,247.55,156.17,110.49,767.410,317.7Class level1st year undergraduate1,611.62,647.52,713.73,931.55,719.95,812.82nd year undergraduate2,697.94,340.04,076.56,203.38,713.18,817.13rd year undergraduate5,142.67,989.87,256.312,128.714,121.713,994.54th year undergraduate6,156.610,052.211,342.313,607.018,690.720,045.25th year undergraduate5,915.99,403.014,457.719,476.326,832.422,825.5Senior/Graduated during survey year7,447.012,915.82,725.43,936.76,446.87,623.0199620002004200820122016 Cumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergrad (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)EstimatesTotal2,856.75,247.55,156.17,110.49,767.410,317.7Class level1st year undergraduate1,611.62,647.52,713.73,931.55,719.95,812.82nd year undergraduate2,697.94,340.04,076.56,203.38,713.18,817.13rd year undergraduate5,142.67,989.87,256.312,128.714,121.713,994.54th year undergraduate6,156.610,052.211,342.313,607.018,690.720,045.25th year undergraduate5,915.99,403.014,457.719,476.326,832.422,825.5Senior/Graduated during survey year7,447.012,915.82,725.43,936.76,446.87,623.0Standard Error (BRR)Total{|1996|{42.36|{|2000|{48.49|{|2004|{54.95|{|2008|{52.48|{|2012|{76.64|{|2016|{76.43|Class level1st year undergraduate{|1996|{45.48|{|2000|{63.74|{|2004|{68.90|{|2008|{65.99|{|2012|{90.39|{|2016|{100.66|2nd year undergraduate{|1996|{90.03|{|2000|{94.67|{|2004|{87.05|{|2008|{106.74|{|2012|{140.72|{|2016|{136.15|3rd year undergraduate{|1996|{119.47|{|2000|{179.46|{|2004|{121.81|{|2008|{168.04|{|2012|{212.23|{|2016|{224.32|4th year undergraduate{|1996|{200.32|{|2000|{192.09|{|2004|{199.11|{|2008|{184.28|{|2012|{281.19|{|2016|{290.09|5th year undergraduate{|1996|{725.21|{|2000|{747.38|{|2004|{526.35|{|2008|{561.67|{|2012|{877.37|{|2016|{1,090.53|Senior/Graduated during survey year{|1996|{218.19|{|2000|{231.17|{|2004|{176.52|{|2008|{268.16|{|2012|{435.74|{|2016|{1,066.76|Relative Standard Error (%)Total1.480.921.070.740.780.74Class level1st year undergraduate2.822.412.541.681.581.732nd year undergraduate3.342.182.141.721.621.543rd year undergraduate2.322.251.681.391.501.604th year undergraduate3.251.911.761.351.501.455th year undergraduate12.267.953.642.883.274.78Senior/Graduated during survey year2.931.796.486.816.7613.99Weighted Sample Sizes (n/1,000s)Total16,677.916,579.219,053.820,762.323,055.419,532.3Class level1st year undergraduate8,498.15,910.67,012.88,738.39,437.17,805.02nd year undergraduate3,646.04,154.54,942.25,521.35,958.25,543.33rd year undergraduate1,761.22,126.82,635.32,640.42,810.82,416.14th year undergraduate1,005.21,279.72,484.52,653.53,129.23,303.25th year undergraduate98.6134.5542.8394.6491.9244.4Senior/Graduated during survey year918.91,443.61,436.1814.11,228.3220.3199620002004200820122016 Cumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergradCumulative amount borrowed for undergrad (Avg)(Avg)(Avg)(Avg)(Avg)(Avg) Amt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIEstimatesTotal2,856.7[2,771.61-2,941.82]5,247.5[5,150.11-5,344.95]5,156.1[5,047.72-5,264.43]7,110.4[7,006.91-7,213.88]9,767.4[9,616.23-9,918.50]10,317.7[10,166.95-10,468.40]Class level1st year undergraduate1,611.6[1,520.18-1,702.93]2,647.5[2,519.44-2,775.54]2,713.7[2,577.87-2,849.60]3,931.5[3,801.41-4,061.69]5,719.9[5,541.69-5,898.18]5,812.8[5,614.31-6,011.29]2nd year undergraduate2,697.9[2,517.08-2,878.81]4,340.0[4,149.78-4,530.16]4,076.5[3,904.80-4,248.13]6,203.3[5,992.85-6,413.84]8,713.1[8,435.58-8,990.58]8,817.1[8,548.64-9,085.61]3rd year undergraduate5,142.6[4,902.60-5,382.62]7,989.8[7,629.22-8,350.30]7,256.3[7,016.07-7,496.49]12,128.7[11,797.34-12,460.07]14,121.7[13,703.15-14,540.18]13,994.5[13,552.15-14,436.87]4th year undergraduate6,156.6[5,754.20-6,559.07]10,052.2[9,666.32-10,438.14]11,342.3[10,949.65-11,734.94]13,607.0[13,243.62-13,970.43]18,690.7[18,136.24-19,245.25]20,045.2[19,473.11-20,617.23]5th year undergraduate5,915.9[4,458.93-7,372.83]9,403.0[7,901.48-10,904.45]14,457.7[13,419.77-15,495.71]19,476.3[18,368.71-20,583.94]26,832.4[25,102.26-28,562.62]22,825.5[20,674.93-24,975.98]Senior/Graduated during survey year7,447.0[7,008.64-7,885.32]12,915.8[12,451.42-13,380.25]2,725.4[2,377.33-3,073.51]3,936.7[3,407.87-4,465.48]6,446.8[5,587.47-7,306.04]7,623.0[5,519.39-9,726.68]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: UGLVL1.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: BORAMT1 and UGLVL1. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: BORAMT1 (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), UGLEVEL1 (NPSAS:1996) and UGLVL1 (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96), 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.truebfebkneebfebknee2Average Student budget minus all aid, Average Total work study, Average Total loans (excluding PLUS) and Average Student budget (attendance adjusted) by Institution sector (4 with multiple), for [Attendance pattern (Full-time/full year, 1 institution)] for years 2000, 2004, 2008, 2012 and 2016 Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)(Avg)(Avg)(Avg)(Avg)EstimatesTotal20008,606.6199.32,253.014,643.120049,607.4273.22,769.617,060.0200811,933.6317.64,361.222,456.2201213,103.6275.94,586.626,417.3201614,892.9263.14,675.630,515.3Institution sector (4 with multiple)Public 4-year20007,499.1165.92,254.512,509.720048,523.4244.52,790.415,104.3200810,025.0256.73,868.418,918.9201211,779.8201.84,514.723,202.2201613,755.1196.24,596.526,881.9Private not-for-profit 4-year200011,994.8416.43,610.823,585.6200414,018.1556.04,406.728,232.2200816,094.7673.26,591.135,381.5201218,127.2729.36,157.043,541.0201619,889.9573.46,552.847,951.6Public 2-year20006,976.969.4485.59,035.420047,649.0135.3650.010,388.320088,920.1184.11,130.112,632.620129,883.0102.81,428.915,030.9201610,382.4115.01,154.916,138.1Private for-profit200010,440.459.0 !4,344.318,081.0200410,546.5102.85,360.120,243.3200817,287.764.27,941.528,843.6201215,023.472.78,111.129,331.9201616,729.552.97,764.132,573.6Average Student budget minus all aid, Average Total work study, Average Total loans (excluding PLUS) and Average Student budget (attendance adjusted) by Institution sector (4 with multiple), for [Attendance pattern (Full-time/full year, 1 institution)] for years 2000, 2004, 2008, 2012 and 2016 Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)(Avg)(Avg)(Avg)(Avg)EstimatesTotal20008,606.6199.32,253.014,643.120049,607.4273.22,769.617,060.0200811,933.6317.64,361.222,456.2201213,103.6275.94,586.626,417.3201614,892.9263.14,675.630,515.3Institution sector (4 with multiple)Public 4-year20007,499.1165.92,254.512,509.720048,523.4244.52,790.415,104.3200810,025.0256.73,868.418,918.9201211,779.8201.84,514.723,202.2201613,755.1196.24,596.526,881.9Private not-for-profit 4-year200011,994.8416.43,610.823,585.6200414,018.1556.04,406.728,232.2200816,094.7673.26,591.135,381.5201218,127.2729.36,157.043,541.0201619,889.9573.46,552.847,951.6Public 2-year20006,976.969.4485.59,035.420047,649.0135.3650.010,388.320088,920.1184.11,130.112,632.620129,883.0102.81,428.915,030.9201610,382.4115.01,154.916,138.1Private for-profit200010,440.459.0 !4,344.318,081.0200410,546.5102.85,360.120,243.3200817,287.764.27,941.528,843.6201215,023.472.78,111.129,331.9201616,729.552.97,764.132,573.6Standard Error (BRR)Total2000{|2000|{82.32|{|2000|{8.81|{|2000|{29.93|{|2000|{110.40|2004{|2004|{98.39|{|2004|{9.45|{|2004|{39.94|{|2004|{178.06|2008{|2008|{90.97|{|2008|{7.58|{|2008|{44.39|{|2008|{111.17|2012{|2012|{132.25|{|2012|{8.68|{|2012|{54.14|{|2012|{136.26|2016{|2016|{125.60|{|2016|{7.96|{|2016|{48.01|{|2016|{156.79|Institution sector (4 with multiple)Public 4-year2000{|2000|{73.29|{|2000|{11.07|{|2000|{28.30|{|2000|{95.09|2004{|2004|{91.14|{|2004|{9.64|{|2004|{34.45|{|2004|{108.43|2008{|2008|{78.12|{|2008|{9.14|{|2008|{41.67|{|2008|{77.88|2012{|2012|{159.18|{|2012|{10.64|{|2012|{55.58|{|2012|{166.28|2016{|2016|{141.36|{|2016|{9.90|{|2016|{49.87|{|2016|{140.79|Private not-for-profit 4-year2000{|2000|{247.64|{|2000|{22.67|{|2000|{65.81|{|2000|{241.53|2004{|2004|{345.87|{|2004|{28.72|{|2004|{110.96|{|2004|{418.27|2008{|2008|{258.02|{|2008|{26.92|{|2008|{129.43|{|2008|{275.16|2012{|2012|{395.31|{|2012|{31.49|{|2012|{134.39|{|2012|{352.01|2016{|2016|{378.02|{|2016|{20.71|{|2016|{131.41|{|2016|{368.26|Public 2-year2000{|2000|{122.25|{|2000|{11.64|{|2000|{45.73|{|2000|{120.84|2004{|2004|{166.68|{|2004|{11.95|{|2004|{48.28|{|2004|{169.90|2008{|2008|{123.91|{|2008|{12.63|{|2008|{38.13|{|2008|{108.42|2012{|2012|{173.77|{|2012|{9.67|{|2012|{60.55|{|2012|{118.79|2016{|2016|{218.79|{|2016|{16.63|{|2016|{46.62|{|2016|{186.16|Private for-profit2000{|2000|{358.95|{|2000|{27.09|{|2000|{191.23|{|2000|{379.45|2004{|2004|{270.63|{|2004|{26.00|{|2004|{203.04|{|2004|{460.63|2008{|2008|{410.09|{|2008|{14.81|{|2008|{217.40|{|2008|{317.71|2012{|2012|{325.13|{|2012|{8.41|{|2012|{130.18|{|2012|{342.85|2016{|2016|{476.23|{|2016|{10.47|{|2016|{179.69|{|2016|{678.88|Relative Standard Error (%)Total20000.964.421.330.7520041.023.461.441.0420080.762.391.020.5020121.013.151.180.5220160.843.021.030.51Institution sector (4 with multiple)Public 4-year20000.986.671.260.7620041.073.941.230.7220080.783.561.080.4120121.355.271.230.7220161.035.051.080.52Private not-for-profit 4-year20002.065.441.821.0220042.475.172.521.4820081.604.001.960.7820122.184.322.180.8120161.903.612.010.77Public 2-year20001.7516.779.421.3420042.188.837.431.6420081.396.863.370.8620121.769.414.240.7920162.1114.464.041.15Private for-profit20003.4445.934.402.1020042.5725.303.792.2820082.3723.082.741.1020122.1611.571.601.1720162.8519.812.312.08Weighted Sample Sizes (n/1,000s)Total20005,870.95,886.25,886.25,870.920047,048.17,048.17,048.17,048.120086,900.46,900.46,900.46,900.420127,943.27,943.27,943.27,943.220166,487.36,487.36,487.36,487.3Institution sector (4 with multiple)Public 4-year20002,804.12,812.22,812.22,804.120043,342.03,342.03,342.03,342.020083,256.93,256.93,256.93,256.920123,466.63,466.63,466.63,466.620163,249.13,249.13,249.13,249.1Private not-for-profit 4-year20001,416.51,422.81,422.81,416.520041,480.61,480.61,480.61,480.620081,579.21,579.21,579.21,579.220121,686.11,686.11,686.11,686.120161,588.81,588.81,588.81,588.8Public 2-year20001,321.31,321.71,321.71,321.320041,702.01,702.01,702.01,702.020081,334.11,334.11,334.11,334.120121,785.41,785.41,785.41,785.420161,157.11,157.11,157.11,157.1Private for-profit2000245.9246.3246.3245.92004457.6457.6457.6457.62008688.1688.1688.1688.12012939.9939.9939.9939.92016437.8437.8437.8437.8Average Student budget minus all aid, Average Total work study, Average Total loans (excluding PLUS) and Average Student budget (attendance adjusted) by Institution sector (4 with multiple), for [Attendance pattern (Full-time/full year, 1 institution)] for years 2000, 2004, 2008, 2012 and 2016 Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)(Avg)(Avg)(Avg)(Avg)Amt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIEstimatesTotal20008,606.6[8,441.21-8,771.97]199.3[181.57-216.95]2,253.0[2,192.90-2,313.16]14,643.1[14,421.28-14,864.88]20049,607.4[9,413.39-9,801.45]273.2[254.57-291.84]2,769.6[2,690.87-2,848.39]17,060.0[16,708.88-17,411.16]200811,933.6[11,754.19-12,112.99]317.6[302.61-332.51]4,361.2[4,273.64-4,448.71]22,456.2[22,236.99-22,675.44]201213,103.6[12,842.81-13,364.39]275.9[258.80-293.02]4,586.6[4,479.84-4,693.35]26,417.3[26,148.60-26,686.01]201614,892.9[14,645.20-15,140.56]263.1[247.41-278.79]4,675.6[4,580.96-4,770.31]30,515.3[30,206.07-30,824.44]Institution sector (4 with multiple)Public 4-year20007,499.1[7,351.84-7,646.32]165.9[143.70-188.17]2,254.5[2,197.65-2,311.35]12,509.7[12,318.63-12,700.69]20048,523.4[8,343.65-8,703.12]244.5[225.47-263.51]2,790.4[2,722.47-2,858.36]15,104.3[14,890.43-15,318.09]200810,025.0[9,870.95-10,179.07]256.7[238.72-274.76]3,868.4[3,786.19-3,950.53]18,918.9[18,765.30-19,072.45]201211,779.8[11,465.86-12,093.68]201.8[180.84-222.80]4,514.7[4,405.13-4,624.35]23,202.2[22,874.27-23,530.08]201613,755.1[13,476.29-14,033.82]196.2[176.69-215.75]4,596.5[4,498.20-4,694.88]26,881.9[26,604.28-27,159.57]Private not-for-profit 4-year200011,994.8[11,497.25-12,492.26]416.4[370.89-461.97]3,610.8[3,478.61-3,743.06]23,585.6[23,100.36-24,070.81]200414,018.1[13,336.03-14,700.13]556.0[499.40-612.67]4,406.7[4,187.85-4,625.46]28,232.2[27,407.42-29,057.07]200816,094.7[15,585.90-16,603.54]673.2[620.08-726.26]6,591.1[6,335.90-6,846.36]35,381.5[34,838.88-35,924.10]201218,127.2[17,347.68-18,906.78]729.3[667.24-791.43]6,157.0[5,891.98-6,422.00]43,541.0[42,846.82-44,235.15]201619,889.9[19,144.46-20,635.38]573.4[532.54-614.21]6,552.8[6,293.63-6,811.90]47,951.6[47,225.36-48,677.79]Public 2-year20006,976.9[6,731.34-7,222.55]69.4[46.02-92.79]485.5[393.62-577.36]9,035.4[8,792.59-9,278.12]20047,649.0[7,320.35-7,977.74]135.3[111.76-158.88]650.0[554.78-745.19]10,388.3[10,053.27-10,723.34]20088,920.1[8,675.79-9,164.48]184.1[159.22-209.01]1,130.1[1,054.89-1,205.27]12,632.6[12,418.75-12,846.37]20129,883.0[9,540.31-10,225.64]102.8[83.74-121.89]1,428.9[1,309.46-1,548.28]15,030.9[14,796.69-15,265.20]201610,382.4[9,950.92-10,813.83]115.0[82.21-147.82]1,154.9[1,062.97-1,246.85]16,138.1[15,770.96-16,505.16]Private for-profit200010,440.4[9,719.23-11,161.50]59.0 ![4.56-113.40]4,344.3[3,960.09-4,728.44]18,081.0[17,318.66-18,843.31]200410,546.5[10,012.82-11,080.20]102.8[51.50-154.04]5,360.1[4,959.70-5,760.50]20,243.3[19,334.97-21,151.68]200817,287.7[16,478.99-18,096.37]64.2[34.96-93.39]7,941.5[7,512.81-8,370.24]28,843.6[28,217.08-29,470.12]201215,023.4[14,382.22-15,664.54]72.7[56.13-89.30]8,111.1[7,854.36-8,367.79]29,331.9[28,655.76-30,007.96]201616,729.5[15,790.41-17,668.67]52.9[32.23-73.53]7,764.1[7,409.79-8,118.47]32,573.6[31,234.88-33,912.36]20002004200820122016 Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted) (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)EstimatesTotal8,606.6199.32,253.014,643.19,607.4273.22,769.617,060.011,933.6317.64,361.222,456.213,103.6275.94,586.626,417.314,892.9263.14,675.630,515.3Institution sector (4 with multiple)Public 4-year7,499.1165.92,254.512,509.78,523.4244.52,790.415,104.310,025.0256.73,868.418,918.911,779.8201.84,514.723,202.213,755.1196.24,596.526,881.9Private not-for-profit 4-year11,994.8416.43,610.823,585.614,018.1556.04,406.728,232.216,094.7673.26,591.135,381.518,127.2729.36,157.043,541.019,889.9573.46,552.847,951.6Public 2-year6,976.969.4485.59,035.47,649.0135.3650.010,388.38,920.1184.11,130.112,632.69,883.0102.81,428.915,030.910,382.4115.01,154.916,138.1Private for-profit10,440.459.0 !4,344.318,081.010,546.5102.85,360.120,243.317,287.764.27,941.528,843.615,023.472.78,111.129,331.916,729.552.97,764.132,573.620002004200820122016 Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted) (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)EstimatesTotal8,606.6199.32,253.014,643.19,607.4273.22,769.617,060.011,933.6317.64,361.222,456.213,103.6275.94,586.626,417.314,892.9263.14,675.630,515.3Institution sector (4 with multiple)Public 4-year7,499.1165.92,254.512,509.78,523.4244.52,790.415,104.310,025.0256.73,868.418,918.911,779.8201.84,514.723,202.213,755.1196.24,596.526,881.9Private not-for-profit 4-year11,994.8416.43,610.823,585.614,018.1556.04,406.728,232.216,094.7673.26,591.135,381.518,127.2729.36,157.043,541.019,889.9573.46,552.847,951.6Public 2-year6,976.969.4485.59,035.47,649.0135.3650.010,388.38,920.1184.11,130.112,632.69,883.0102.81,428.915,030.910,382.4115.01,154.916,138.1Private for-profit10,440.459.0 !4,344.318,081.010,546.5102.85,360.120,243.317,287.764.27,941.528,843.615,023.472.78,111.129,331.916,729.552.97,764.132,573.6Standard Error (BRR)Total{|2000|{82.32|{|2000|{8.81|{|2000|{29.93|{|2000|{110.40|{|2004|{98.39|{|2004|{9.45|{|2004|{39.94|{|2004|{178.06|{|2008|{90.97|{|2008|{7.58|{|2008|{44.39|{|2008|{111.17|{|2012|{132.25|{|2012|{8.68|{|2012|{54.14|{|2012|{136.26|{|2016|{125.60|{|2016|{7.96|{|2016|{48.01|{|2016|{156.79|Institution sector (4 with multiple)Public 4-year{|2000|{73.29|{|2000|{11.07|{|2000|{28.30|{|2000|{95.09|{|2004|{91.14|{|2004|{9.64|{|2004|{34.45|{|2004|{108.43|{|2008|{78.12|{|2008|{9.14|{|2008|{41.67|{|2008|{77.88|{|2012|{159.18|{|2012|{10.64|{|2012|{55.58|{|2012|{166.28|{|2016|{141.36|{|2016|{9.90|{|2016|{49.87|{|2016|{140.79|Private not-for-profit 4-year{|2000|{247.64|{|2000|{22.67|{|2000|{65.81|{|2000|{241.53|{|2004|{345.87|{|2004|{28.72|{|2004|{110.96|{|2004|{418.27|{|2008|{258.02|{|2008|{26.92|{|2008|{129.43|{|2008|{275.16|{|2012|{395.31|{|2012|{31.49|{|2012|{134.39|{|2012|{352.01|{|2016|{378.02|{|2016|{20.71|{|2016|{131.41|{|2016|{368.26|Public 2-year{|2000|{122.25|{|2000|{11.64|{|2000|{45.73|{|2000|{120.84|{|2004|{166.68|{|2004|{11.95|{|2004|{48.28|{|2004|{169.90|{|2008|{123.91|{|2008|{12.63|{|2008|{38.13|{|2008|{108.42|{|2012|{173.77|{|2012|{9.67|{|2012|{60.55|{|2012|{118.79|{|2016|{218.79|{|2016|{16.63|{|2016|{46.62|{|2016|{186.16|Private for-profit{|2000|{358.95|{|2000|{27.09|{|2000|{191.23|{|2000|{379.45|{|2004|{270.63|{|2004|{26.00|{|2004|{203.04|{|2004|{460.63|{|2008|{410.09|{|2008|{14.81|{|2008|{217.40|{|2008|{317.71|{|2012|{325.13|{|2012|{8.41|{|2012|{130.18|{|2012|{342.85|{|2016|{476.23|{|2016|{10.47|{|2016|{179.69|{|2016|{678.88|Relative Standard Error (%)Total0.964.421.330.751.023.461.441.040.762.391.020.501.013.151.180.520.843.021.030.51Institution sector (4 with multiple)Public 4-year0.986.671.260.761.073.941.230.720.783.561.080.411.355.271.230.721.035.051.080.52Private not-for-profit 4-year2.065.441.821.022.475.172.521.481.604.001.960.782.184.322.180.811.903.612.010.77Public 2-year1.7516.779.421.342.188.837.431.641.396.863.370.861.769.414.240.792.1114.464.041.15Private for-profit3.4445.934.402.102.5725.303.792.282.3723.082.741.102.1611.571.601.172.8519.812.312.08Weighted Sample Sizes (n/1,000s)Total5,870.95,886.25,886.25,870.97,048.17,048.17,048.17,048.16,900.46,900.46,900.46,900.47,943.27,943.27,943.27,943.26,487.36,487.36,487.36,487.3Institution sector (4 with multiple)Public 4-year2,804.12,812.22,812.22,804.13,342.03,342.03,342.03,342.03,256.93,256.93,256.93,256.93,466.63,466.63,466.63,466.63,249.13,249.13,249.13,249.1Private not-for-profit 4-year1,416.51,422.81,422.81,416.51,480.61,480.61,480.61,480.61,579.21,579.21,579.21,579.21,686.11,686.11,686.11,686.11,588.81,588.81,588.81,588.8Public 2-year1,321.31,321.71,321.71,321.31,702.01,702.01,702.01,702.01,334.11,334.11,334.11,334.11,785.41,785.41,785.41,785.41,157.11,157.11,157.11,157.1Private for-profit245.9246.3246.3245.9457.6457.6457.6457.6688.1688.1688.1688.1939.9939.9939.9939.9437.8437.8437.8437.820002004200820122016 Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted)Student budget minus all aidTotal work studyTotal loans (excluding PLUS)Student budget (attendance adjusted) (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg) Amt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIEstimatesTotal8,606.6[8,441.21-8,771.97]199.3[181.57-216.95]2,253.0[2,192.90-2,313.16]14,643.1[14,421.28-14,864.88]9,607.4[9,413.39-9,801.45]273.2[254.57-291.84]2,769.6[2,690.87-2,848.39]17,060.0[16,708.88-17,411.16]11,933.6[11,754.19-12,112.99]317.6[302.61-332.51]4,361.2[4,273.64-4,448.71]22,456.2[22,236.99-22,675.44]13,103.6[12,842.81-13,364.39]275.9[258.80-293.02]4,586.6[4,479.84-4,693.35]26,417.3[26,148.60-26,686.01]14,892.9[14,645.20-15,140.56]263.1[247.41-278.79]4,675.6[4,580.96-4,770.31]30,515.3[30,206.07-30,824.44]Institution sector (4 with multiple)Public 4-year7,499.1[7,351.84-7,646.32]165.9[143.70-188.17]2,254.5[2,197.65-2,311.35]12,509.7[12,318.63-12,700.69]8,523.4[8,343.65-8,703.12]244.5[225.47-263.51]2,790.4[2,722.47-2,858.36]15,104.3[14,890.43-15,318.09]10,025.0[9,870.95-10,179.07]256.7[238.72-274.76]3,868.4[3,786.19-3,950.53]18,918.9[18,765.30-19,072.45]11,779.8[11,465.86-12,093.68]201.8[180.84-222.80]4,514.7[4,405.13-4,624.35]23,202.2[22,874.27-23,530.08]13,755.1[13,476.29-14,033.82]196.2[176.69-215.75]4,596.5[4,498.20-4,694.88]26,881.9[26,604.28-27,159.57]Private not-for-profit 4-year11,994.8[11,497.25-12,492.26]416.4[370.89-461.97]3,610.8[3,478.61-3,743.06]23,585.6[23,100.36-24,070.81]14,018.1[13,336.03-14,700.13]556.0[499.40-612.67]4,406.7[4,187.85-4,625.46]28,232.2[27,407.42-29,057.07]16,094.7[15,585.90-16,603.54]673.2[620.08-726.26]6,591.1[6,335.90-6,846.36]35,381.5[34,838.88-35,924.10]18,127.2[17,347.68-18,906.78]729.3[667.24-791.43]6,157.0[5,891.98-6,422.00]43,541.0[42,846.82-44,235.15]19,889.9[19,144.46-20,635.38]573.4[532.54-614.21]6,552.8[6,293.63-6,811.90]47,951.6[47,225.36-48,677.79]Public 2-year6,976.9[6,731.34-7,222.55]69.4[46.02-92.79]485.5[393.62-577.36]9,035.4[8,792.59-9,278.12]7,649.0[7,320.35-7,977.74]135.3[111.76-158.88]650.0[554.78-745.19]10,388.3[10,053.27-10,723.34]8,920.1[8,675.79-9,164.48]184.1[159.22-209.01]1,130.1[1,054.89-1,205.27]12,632.6[12,418.75-12,846.37]9,883.0[9,540.31-10,225.64]102.8[83.74-121.89]1,428.9[1,309.46-1,548.28]15,030.9[14,796.69-15,265.20]10,382.4[9,950.92-10,813.83]115.0[82.21-147.82]1,154.9[1,062.97-1,246.85]16,138.1[15,770.96-16,505.16]Private for-profit10,440.4[9,719.23-11,161.50]59.0 ![4.56-113.40]4,344.3[3,960.09-4,728.44]18,081.0[17,318.66-18,843.31]10,546.5[10,012.82-11,080.20]102.8[51.50-154.04]5,360.1[4,959.70-5,760.50]20,243.3[19,334.97-21,151.68]17,287.7[16,478.99-18,096.37]64.2[34.96-93.39]7,941.5[7,512.81-8,370.24]28,843.6[28,217.08-29,470.12]15,023.4[14,382.22-15,664.54]72.7[56.13-89.30]8,111.1[7,854.36-8,367.79]29,331.9[28,655.76-30,007.96]16,729.5[15,790.41-17,668.67]52.9[32.23-73.53]7,764.1[7,409.79-8,118.47]32,573.6[31,234.88-33,912.36]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: NETCST1, BUDGETAJ and ATTNSTAT.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: NETCST1, TOTWKST, TOTLOAN, BUDGETAJ, SECTOR4 and ATTNSTAT. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: NETCST1 (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), TOTWKST (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), TOTLOAN (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), BUDGETA2 (NPSAS:2000), SECTOR4 (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), ATTNSTAT (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and BUDGETAJ (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.truebfebkn66bfebkn663Average>0 Expected Family Contribution by Immigrant generational status, Born in the U.S. (parents) and English is the primary language for years 2000, 2004, 2008, 2012 and 2016 Expected Family Contribution(Avg>0)EstimatesTotal200010,662.2200411,889.9200813,279.9201213,044.4201616,499.0Immigrant generational statusFirst generation immigrant20008,383.520048,918.720089,761.620129,818.1201611,742.9Second gen immigrant (both parents foreign-born)20009,569.0200410,711.4200811,260.3201211,048.0201613,237.9Second gen immigrant (one parent foreign-born)200011,821.0200412,224.0200813,346.1201214,094.1201615,302.1Third generation immigrant or higher200010,760.1200412,318.7200813,833.5201213,523.7201617,611.2English is the primary languageYes2000—200412,266.1200813,727.1201213,531.4201617,441.7No2000—20048,796.020089,845.3201210,447.1201612,573.5Born in the U.S. (parents)Both parents were born in the United States200010,760.1200412,317.2200813,833.5201213,523.0201617,614.0One parent was born in the United States200011,317.9200411,879.4200813,263.6201214,026.6201615,337.3Both parents were not born in the United States20009,098.120049,824.3200810,719.0201210,625.7201613,296.6Average>0 Expected Family Contribution by Immigrant generational status, Born in the U.S. (parents) and English is the primary language for years 2000, 2004, 2008, 2012 and 2016 Expected Family Contribution(Avg>0)EstimatesTotal200010,662.2200411,889.9200813,279.9201213,044.4201616,499.0Immigrant generational statusFirst generation immigrant20008,383.520048,918.720089,761.620129,818.1201611,742.9Second gen immigrant (both parents foreign-born)20009,569.0200410,711.4200811,260.3201211,048.0201613,237.9Second gen immigrant (one parent foreign-born)200011,821.0200412,224.0200813,346.1201214,094.1201615,302.1Third generation immigrant or higher200010,760.1200412,318.7200813,833.5201213,523.7201617,611.2English is the primary languageYes2000—200412,266.1200813,727.1201213,531.4201617,441.7No2000—20048,796.020089,845.3201210,447.1201612,573.5Born in the U.S. (parents)Both parents were born in the United States200010,760.1200412,317.2200813,833.5201213,523.0201617,614.0One parent was born in the United States200011,317.9200411,879.4200813,263.6201214,026.6201615,337.3Both parents were not born in the United States20009,098.120049,824.3200810,719.0201210,625.7201613,296.6Standard Error (BRR)Total2000{|2000|{82.32|2004{|2004|{134.93|2008{|2008|{80.67|2012{|2012|{103.68|2016{|2016|{233.69|Immigrant generational statusFirst generation immigrant2000{|2000|{286.57|2004{|2004|{284.12|2008{|2008|{203.87|2012{|2012|{334.51|2016{|2016|{642.36|Second gen immigrant (both parents foreign-born)2000{|2000|{400.74|2004{|2004|{314.32|2008{|2008|{258.78|2012{|2012|{413.81|2016{|2016|{563.60|Second gen immigrant (one parent foreign-born)2000{|2000|{400.80|2004{|2004|{416.15|2008{|2008|{293.07|2012{|2012|{492.45|2016{|2016|{669.36|Third generation immigrant or higher2000{|2000|{97.63|2004{|2004|{135.52|2008{|2008|{93.95|2012{|2012|{116.57|2016{|2016|{301.56|English is the primary languageYes2000†2004{|2004|{140.83|2008{|2008|{88.03|2012{|2012|{115.78|2016{|2016|{277.19|No2000†2004{|2004|{199.31|2008{|2008|{204.46|2012{|2012|{252.00|2016{|2016|{419.57|Born in the U.S. (parents)Both parents were born in the United States2000{|2000|{97.63|2004{|2004|{135.44|2008{|2008|{93.95|2012{|2012|{116.62|2016{|2016|{301.12|One parent was born in the United States2000{|2000|{371.85|2004{|2004|{364.16|2008{|2008|{274.33|2012{|2012|{465.48|2016{|2016|{615.17|Both parents were not born in the United States2000{|2000|{228.84|2004{|2004|{232.51|2008{|2008|{178.00|2012{|2012|{242.80|2016{|2016|{408.11|Relative Standard Error (%)Total20000.7720041.1320080.6120120.7920161.42Immigrant generational statusFirst generation immigrant20003.4220043.1920082.0920123.4120165.47Second gen immigrant (both parents foreign-born)20004.1920042.9320082.3020123.7520164.26Second gen immigrant (one parent foreign-born)20003.3920043.4020082.2020123.4920164.37Third generation immigrant or higher20000.9120041.1020080.6820120.8620161.71English is the primary languageYes2000†20041.1520080.6420120.8620161.59No2000†20042.2720082.0820122.4120163.34Born in the U.S. (parents)Both parents were born in the United States20000.9120041.1020080.6820120.8620161.71One parent was born in the United States20003.2920043.0720082.0720123.3220164.01Both parents were not born in the United States20002.5220042.3720081.6620122.2920163.07Weighted Sample Sizes (n/1,000s)Total200013,651.9200415,106.2200815,501.3201214,340.3201611,900.8Immigrant generational statusFirst generation immigrant2000665.820041,390.720081,403.520121,040.32016931.0Second gen immigrant (both parents foreign-born)2000419.62004933.620081,087.420121,264.920161,457.6Second gen immigrant (one parent foreign-born)2000468.32004787.82008915.82012865.92016780.2Third generation immigrant or higher20007,257.6200411,771.1200811,915.9201210,864.720168,404.7English is the primary languageYes2000†200413,468.4200813,715.4201212,076.020169,596.2No2000†20041,637.820081,786.020122,264.320162,304.6Born in the U.S. (parents)Both parents were born in the United States20007,257.6200411,774.2200811,915.9201210,866.820168,412.7One parent was born in the United States2000567.92004900.420081,015.82012941.22016877.0Both parents were not born in the United States20001,119.520042,431.620082,569.720122,532.320162,611.1Average>0 Expected Family Contribution by Immigrant generational status, Born in the U.S. (parents) and English is the primary language for years 2000, 2004, 2008, 2012 and 2016 Expected Family Contribution(Avg>0)Amt.95% CIEstimatesTotal200010,662.2[10,496.77-10,827.54]200411,889.9[11,623.78-12,155.95]200813,279.9[13,120.83-13,438.98]201213,044.4[12,840.00-13,248.90]201616,499.0[16,038.13-16,959.80]Immigrant generational statusFirst generation immigrant20008,383.5[7,807.74-8,959.19]20048,918.7[8,358.43-9,479.00]20089,761.6[9,359.53-10,163.58]20129,818.1[9,158.42-10,477.74]201611,742.9[10,476.18-13,009.65]Second gen immigrant (both parents foreign-born)20009,569.0[8,763.96-10,374.14]200410,711.4[10,091.55-11,331.23]200811,260.3[10,749.98-11,770.61]201211,048.0[10,231.98-11,864.05]201613,237.9[12,126.51-14,349.33]Second gen immigrant (one parent foreign-born)200011,821.0[11,015.75-12,626.19]200412,224.0[11,403.37-13,044.67]200813,346.1[12,768.19-13,924.04]201214,094.1[13,123.04-15,065.25]201615,302.1[13,982.14-16,622.11]Third generation immigrant or higher200010,760.1[10,563.92-10,956.21]200412,318.7[12,051.40-12,585.91]200813,833.5[13,648.29-14,018.81]201213,523.7[13,293.88-13,753.61]201617,611.2[17,016.54-18,205.90]English is the primary languageYes2000—†200412,266.1[11,988.38-12,543.81]200813,727.1[13,553.55-13,900.73]201213,531.4[13,303.12-13,759.76]201617,441.7[16,895.07-17,988.33]No2000—†20048,796.0[8,402.91-9,188.99]20089,845.3[9,442.16-10,248.54]201210,447.1[9,950.19-10,944.10]201612,573.5[11,746.11-13,400.89]Born in the U.S. (parents)Both parents were born in the United States200010,760.1[10,563.92-10,956.21]200412,317.2[12,050.16-12,584.33]200813,833.5[13,648.29-14,018.81]201213,523.0[13,293.05-13,753.00]201617,614.0[17,020.19-18,207.79]One parent was born in the United States200011,317.9[10,570.86-12,064.95]200411,879.4[11,161.23-12,597.50]200813,263.6[12,722.64-13,804.60]201214,026.6[13,108.66-14,944.49]201615,337.3[14,124.21-16,550.45]Both parents were not born in the United States20009,098.1[8,638.37-9,557.87]20049,824.3[9,365.77-10,282.80]200810,719.0[10,368.02-11,070.04]201210,625.7[10,146.92-11,104.52]201613,296.6[12,491.82-14,101.41]20002004200820122016 Expected Family ContributionExpected Family ContributionExpected Family ContributionExpected Family ContributionExpected Family Contribution (Avg>0)(Avg>0)(Avg>0)(Avg>0)(Avg>0)EstimatesTotal10,662.211,889.913,279.913,044.416,499.0Immigrant generational statusFirst generation immigrant8,383.58,918.79,761.69,818.111,742.9Second gen immigrant (both parents foreign-born)9,569.010,711.411,260.311,048.013,237.9Second gen immigrant (one parent foreign-born)11,821.012,224.013,346.114,094.115,302.1Third generation immigrant or higher10,760.112,318.713,833.513,523.717,611.2English is the primary languageYes—12,266.113,727.113,531.417,441.7No—8,796.09,845.310,447.112,573.5Born in the U.S. (parents)Both parents were born in the United States10,760.112,317.213,833.513,523.017,614.0One parent was born in the United States11,317.911,879.413,263.614,026.615,337.3Both parents were not born in the United States9,098.19,824.310,719.010,625.713,296.620002004200820122016 Expected Family ContributionExpected Family ContributionExpected Family ContributionExpected Family ContributionExpected Family Contribution (Avg>0)(Avg>0)(Avg>0)(Avg>0)(Avg>0)EstimatesTotal10,662.211,889.913,279.913,044.416,499.0Immigrant generational statusFirst generation immigrant8,383.58,918.79,761.69,818.111,742.9Second gen immigrant (both parents foreign-born)9,569.010,711.411,260.311,048.013,237.9Second gen immigrant (one parent foreign-born)11,821.012,224.013,346.114,094.115,302.1Third generation immigrant or higher10,760.112,318.713,833.513,523.717,611.2English is the primary languageYes—12,266.113,727.113,531.417,441.7No—8,796.09,845.310,447.112,573.5Born in the U.S. (parents)Both parents were born in the United States10,760.112,317.213,833.513,523.017,614.0One parent was born in the United States11,317.911,879.413,263.614,026.615,337.3Both parents were not born in the United States9,098.19,824.310,719.010,625.713,296.6Standard Error (BRR)Total{|2000|{82.32|{|2004|{134.93|{|2008|{80.67|{|2012|{103.68|{|2016|{233.69|Immigrant generational statusFirst generation immigrant{|2000|{286.57|{|2004|{284.12|{|2008|{203.87|{|2012|{334.51|{|2016|{642.36|Second gen immigrant (both parents foreign-born){|2000|{400.74|{|2004|{314.32|{|2008|{258.78|{|2012|{413.81|{|2016|{563.60|Second gen immigrant (one parent foreign-born){|2000|{400.80|{|2004|{416.15|{|2008|{293.07|{|2012|{492.45|{|2016|{669.36|Third generation immigrant or higher{|2000|{97.63|{|2004|{135.52|{|2008|{93.95|{|2012|{116.57|{|2016|{301.56|English is the primary languageYes†{|2004|{140.83|{|2008|{88.03|{|2012|{115.78|{|2016|{277.19|No†{|2004|{199.31|{|2008|{204.46|{|2012|{252.00|{|2016|{419.57|Born in the U.S. (parents)Both parents were born in the United States{|2000|{97.63|{|2004|{135.44|{|2008|{93.95|{|2012|{116.62|{|2016|{301.12|One parent was born in the United States{|2000|{371.85|{|2004|{364.16|{|2008|{274.33|{|2012|{465.48|{|2016|{615.17|Both parents were not born in the United States{|2000|{228.84|{|2004|{232.51|{|2008|{178.00|{|2012|{242.80|{|2016|{408.11|Relative Standard Error (%)Total0.771.130.610.791.42Immigrant generational statusFirst generation immigrant3.423.192.093.415.47Second gen immigrant (both parents foreign-born)4.192.932.303.754.26Second gen immigrant (one parent foreign-born)3.393.402.203.494.37Third generation immigrant or higher0.911.100.680.861.71English is the primary languageYes†1.150.640.861.59No†2.272.082.413.34Born in the U.S. (parents)Both parents were born in the United States0.911.100.680.861.71One parent was born in the United States3.293.072.073.324.01Both parents were not born in the United States2.522.371.662.293.07Weighted Sample Sizes (n/1,000s)Total13,651.915,106.215,501.314,340.311,900.8Immigrant generational statusFirst generation immigrant665.81,390.71,403.51,040.3931.0Second gen immigrant (both parents foreign-born)419.6933.61,087.41,264.91,457.6Second gen immigrant (one parent foreign-born)468.3787.8915.8865.9780.2Third generation immigrant or higher7,257.611,771.111,915.910,864.78,404.7English is the primary languageYes†13,468.413,715.412,076.09,596.2No†1,637.81,786.02,264.32,304.6Born in the U.S. (parents)Both parents were born in the United States7,257.611,774.211,915.910,866.88,412.7One parent was born in the United States567.9900.41,015.8941.2877.0Both parents were not born in the United States1,119.52,431.62,569.72,532.32,611.120002004200820122016 Expected Family ContributionExpected Family ContributionExpected Family ContributionExpected Family ContributionExpected Family Contribution (Avg>0)(Avg>0)(Avg>0)(Avg>0)(Avg>0) Amt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIEstimatesTotal10,662.2[10,496.77-10,827.54]11,889.9[11,623.78-12,155.95]13,279.9[13,120.83-13,438.98]13,044.4[12,840.00-13,248.90]16,499.0[16,038.13-16,959.80]Immigrant generational statusFirst generation immigrant8,383.5[7,807.74-8,959.19]8,918.7[8,358.43-9,479.00]9,761.6[9,359.53-10,163.58]9,818.1[9,158.42-10,477.74]11,742.9[10,476.18-13,009.65]Second gen immigrant (both parents foreign-born)9,569.0[8,763.96-10,374.14]10,711.4[10,091.55-11,331.23]11,260.3[10,749.98-11,770.61]11,048.0[10,231.98-11,864.05]13,237.9[12,126.51-14,349.33]Second gen immigrant (one parent foreign-born)11,821.0[11,015.75-12,626.19]12,224.0[11,403.37-13,044.67]13,346.1[12,768.19-13,924.04]14,094.1[13,123.04-15,065.25]15,302.1[13,982.14-16,622.11]Third generation immigrant or higher10,760.1[10,563.92-10,956.21]12,318.7[12,051.40-12,585.91]13,833.5[13,648.29-14,018.81]13,523.7[13,293.88-13,753.61]17,611.2[17,016.54-18,205.90]English is the primary languageYes—†12,266.1[11,988.38-12,543.81]13,727.1[13,553.55-13,900.73]13,531.4[13,303.12-13,759.76]17,441.7[16,895.07-17,988.33]No—†8,796.0[8,402.91-9,188.99]9,845.3[9,442.16-10,248.54]10,447.1[9,950.19-10,944.10]12,573.5[11,746.11-13,400.89]Born in the U.S. (parents)Both parents were born in the United States10,760.1[10,563.92-10,956.21]12,317.2[12,050.16-12,584.33]13,833.5[13,648.29-14,018.81]13,523.0[13,293.05-13,753.00]17,614.0[17,020.19-18,207.79]One parent was born in the United States11,317.9[10,570.86-12,064.95]11,879.4[11,161.23-12,597.50]13,263.6[12,722.64-13,804.60]14,026.6[13,108.66-14,944.49]15,337.3[14,124.21-16,550.45]Both parents were not born in the United States9,098.1[8,638.37-9,557.87]9,824.3[9,365.77-10,282.80]10,719.0[10,368.02-11,070.04]10,625.7[10,146.92-11,104.52]13,296.6[12,491.82-14,101.41]— Not available.— Not available.† Not applicable.STDERR-SOURCE-END— Not available.† Not applicable.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: IMMIGEN, PARBORN and PRIMLANG.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: EFC, IMMIGEN, PARBORN and PRIMLANG. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: EFC4 (NPSAS:2000), IMMIGEN (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), PARBORN (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), EFC (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and PRIMLANG (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.truebfebkpm6ebfebkpm6e4Aid total amount with (Percent>9999 adjusted to 2015-2016 academic year) by Enrollment size at NPSAS institution and Institution level (with multiple) for years 1996, 2000, 2004, 2008, 2012 and 2016 Aid total amount(%>9999)EstimatesTotal199613.3200016.8200421.0200825.5201229.9201631.8Enrollment size at NPSAS institutionLess than 1,000 students199618.7200031.3200432.7200845.5201246.0201643.81,000 to 19,999 students199613.8200016.2200419.9200824.0201229.2201631.020,000 to 100,000 students199611.7200015.1200419.8200822.3201226.9201630.4More than 100,000 students1996‡2000‡2004‡200849.2201246.3201648.3Institution level (with multiple)4-year199624.9200028.8200435.5200842.1201246.7201647.92-year19962.120003.420045.420086.720129.320169.1Less than 2-year199612.8200022.8200424.3200832.8201239.5201633.3Attended more than one institution199613.8200021.4200420.2200829.3201233.8201637.2Aid total amount with (Percent>9999 adjusted to 2015-2016 academic year) by Enrollment size at NPSAS institution and Institution level (with multiple) for years 1996, 2000, 2004, 2008, 2012 and 2016 Aid total amount(%>9999)EstimatesTotal199613.3200016.8200421.0200825.5201229.9201631.8Enrollment size at NPSAS institutionLess than 1,000 students199618.7200031.3200432.7200845.5201246.0201643.81,000 to 19,999 students199613.8200016.2200419.9200824.0201229.2201631.020,000 to 100,000 students199611.7200015.1200419.8200822.3201226.9201630.4More than 100,000 students1996‡2000‡2004‡200849.2201246.3201648.3Institution level (with multiple)4-year199624.9200028.8200435.5200842.1201246.7201647.92-year19962.120003.420045.420086.720129.320169.1Less than 2-year199612.8200022.8200424.3200832.8201239.5201633.3Attended more than one institution199613.8200021.4200420.2200829.3201233.8201637.2Standard Error (BRR)Total19960.3220000.1920040.2920080.1620120.1820160.17Enrollment size at NPSAS institutionLess than 1,000 students19962.5620003.1020042.5120081.9320121.5220161.271,000 to 19,999 students19961.1220000.3420040.4620080.3220120.3920160.3520,000 to 100,000 students19961.5320000.6720040.9520080.4820120.7620160.52More than 100,000 students1996‡2000‡2004‡20083.5520121.7220160.46Institution level (with multiple)4-year19960.6820000.3320040.7220080.3420120.3220160.282-year19960.2020000.2020040.2820080.3420120.2020160.19Less than 2-year19962.9920005.1120040.6020081.3120121.6620161.35Attended more than one institution19960.6020000.7220040.8620080.6120120.9020160.73Relative Standard Error (%)Total19962.4120001.1120041.3620080.6420120.5920160.54Enrollment size at NPSAS institutionLess than 1,000 students199613.6720009.9220047.6720084.2520123.3020162.891,000 to 19,999 students19968.0920002.1020042.3220081.3520121.3520161.1320,000 to 100,000 students199613.0720004.4520044.7820082.1520122.8220161.71More than 100,000 students1996‡2000‡2004‡20087.2220123.7020160.96Institution level (with multiple)4-year19962.7120001.1420042.0320080.8120120.7020160.592-year19969.9120005.8120045.2120085.0020122.1620162.11Less than 2-year199623.44200022.4020042.4820083.9920124.2020164.04Attended more than one institution19964.3420003.3720044.2420082.0920122.6520161.96Weighted Sample Sizes (n/1,000s)Total199616,677.9200016,579.2200419,053.8200820,762.3201223,055.4201619,532.3Enrollment size at NPSAS institutionLess than 1,000 students19961,207.020001,187.620041,726.820081,685.620121,524.820161,414.21,000 to 19,999 students199610,094.1200011,612.3200413,902.0200813,150.3201214,200.0201611,874.420,000 to 100,000 students19964,919.920003,283.720043,425.020085,609.820126,787.920166,050.0More than 100,000 students1996‡2000‡2004‡2008239.02012469.2201681.4Institution level (with multiple)4-year19967,508.820007,714.820048,854.120089,595.5201211,065.020169,805.02-year19967,758.520007,457.820048,271.320089,011.220129,545.620167,410.7Less than 2-year1996703.22000449.32004597.72008563.52012542.72016415.8Attended more than one institution1996707.32000957.220041,330.720081,592.220121,902.220161,900.9Aid total amount with (Percent>9999 adjusted to 2015-2016 academic year) by Enrollment size at NPSAS institution and Institution level (with multiple) for years 1996, 2000, 2004, 2008, 2012 and 2016 Aid total amount(%>9999)Pct.95% CIEstimatesTotal199613.3[12.66-13.95]200016.8[16.41-17.16]200421.0[20.45-21.58]200825.5[25.21-25.85]201229.9[29.60-30.30]201631.8[31.50-32.18]Enrollment size at NPSAS institutionLess than 1,000 students199618.7[14.11-24.41]200031.3[25.38-37.79]200432.7[27.95-37.81]200845.5[41.75-49.36]201246.0[43.07-49.05]201643.8[41.32-46.31]1,000 to 19,999 students199613.8[11.74-16.24]200016.2[15.49-16.86]200419.9[18.97-20.79]200824.0[23.36-24.64]201229.2[28.44-29.99]201631.0[30.27-31.64]20,000 to 100,000 students199611.7[8.98-15.17]200015.1[13.83-16.55]200419.8[17.96-21.69]200822.3[21.35-23.24]201226.9[25.47-28.47]201630.4[29.41-31.47]More than 100,000 students1996‡‡2000‡‡2004‡‡200849.2[42.27-56.19]201246.3[42.96-49.71]201648.3[47.39-49.21]Institution level (with multiple)4-year199624.9[23.58-26.29]200028.8[28.11-29.43]200435.5[34.06-36.90]200842.1[41.46-42.81]201246.7[46.02-47.30]201647.9[47.33-48.44]2-year19962.1[1.68-2.51]20003.4[3.05-3.85]20045.4[4.89-6.01]20086.7[6.09-7.42]20129.3[8.87-9.66]20169.1[8.77-9.53]Less than 2-year199612.8[7.85-20.05]200022.8[14.15-34.58]200424.3[23.17-25.54]200832.8[30.28-35.44]201239.5[36.25-42.77]201633.3[30.69-36.00]Attended more than one institution199613.8[12.64-15.05]200021.4[20.03-22.93]200420.2[18.54-21.91]200829.3[28.06-30.48]201233.8[32.08-35.61]201637.2[35.81-38.69]199620002004200820122016 Aid total amountAid total amountAid total amountAid total amountAid total amountAid total amount (%>9999)(%>9999)(%>9999)(%>9999)(%>9999)(%>9999)EstimatesTotal13.316.821.025.529.931.8Enrollment size at NPSAS institutionLess than 1,000 students18.731.332.745.546.043.81,000 to 19,999 students13.816.219.924.029.231.020,000 to 100,000 students11.715.119.822.326.930.4More than 100,000 students‡‡‡49.246.348.3Institution level (with multiple)4-year24.928.835.542.146.747.92-year2.13.45.46.79.39.1Less than 2-year12.822.824.332.839.533.3Attended more than one institution13.821.420.229.333.837.2199620002004200820122016 Aid total amountAid total amountAid total amountAid total amountAid total amountAid total amount (%>9999)(%>9999)(%>9999)(%>9999)(%>9999)(%>9999)EstimatesTotal13.316.821.025.529.931.8Enrollment size at NPSAS institutionLess than 1,000 students18.731.332.745.546.043.81,000 to 19,999 students13.816.219.924.029.231.020,000 to 100,000 students11.715.119.822.326.930.4More than 100,000 students‡‡‡49.246.348.3Institution level (with multiple)4-year24.928.835.542.146.747.92-year2.13.45.46.79.39.1Less than 2-year12.822.824.332.839.533.3Attended more than one institution13.821.420.229.333.837.2Standard Error (BRR)Total0.320.190.290.160.180.17Enrollment size at NPSAS institutionLess than 1,000 students2.563.102.511.931.521.271,000 to 19,999 students1.120.340.460.320.390.3520,000 to 100,000 students1.530.670.950.480.760.52More than 100,000 students‡‡‡3.551.720.46Institution level (with multiple)4-year0.680.330.720.340.320.282-year0.200.200.280.340.200.19Less than 2-year2.995.110.601.311.661.35Attended more than one institution0.600.720.860.610.900.73Relative Standard Error (%)Total2.411.111.360.640.590.54Enrollment size at NPSAS institutionLess than 1,000 students13.679.927.674.253.302.891,000 to 19,999 students8.092.102.321.351.351.1320,000 to 100,000 students13.074.454.782.152.821.71More than 100,000 students‡‡‡7.223.700.96Institution level (with multiple)4-year2.711.142.030.810.700.592-year9.915.815.215.002.162.11Less than 2-year23.4422.402.483.994.204.04Attended more than one institution4.343.374.242.092.651.96Weighted Sample Sizes (n/1,000s)Total16,677.916,579.219,053.820,762.323,055.419,532.3Enrollment size at NPSAS institutionLess than 1,000 students1,207.01,187.61,726.81,685.61,524.81,414.21,000 to 19,999 students10,094.111,612.313,902.013,150.314,200.011,874.420,000 to 100,000 students4,919.93,283.73,425.05,609.86,787.96,050.0More than 100,000 students‡‡‡239.0469.281.4Institution level (with multiple)4-year7,508.87,714.88,854.19,595.511,065.09,805.02-year7,758.57,457.88,271.39,011.29,545.67,410.7Less than 2-year703.2449.3597.7563.5542.7415.8Attended more than one institution707.3957.21,330.71,592.21,902.21,900.9199620002004200820122016 Aid total amountAid total amountAid total amountAid total amountAid total amountAid total amount (%>9999)(%>9999)(%>9999)(%>9999)(%>9999)(%>9999) Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal13.3[12.66-13.95]16.8[16.41-17.16]21.0[20.45-21.58]25.5[25.21-25.85]29.9[29.60-30.30]31.8[31.50-32.18]Enrollment size at NPSAS institutionLess than 1,000 students18.7[14.11-24.41]31.3[25.38-37.79]32.7[27.95-37.81]45.5[41.75-49.36]46.0[43.07-49.05]43.8[41.32-46.31]1,000 to 19,999 students13.8[11.74-16.24]16.2[15.49-16.86]19.9[18.97-20.79]24.0[23.36-24.64]29.2[28.44-29.99]31.0[30.27-31.64]20,000 to 100,000 students11.7[8.98-15.17]15.1[13.83-16.55]19.8[17.96-21.69]22.3[21.35-23.24]26.9[25.47-28.47]30.4[29.41-31.47]More than 100,000 students‡‡‡‡‡‡49.2[42.27-56.19]46.3[42.96-49.71]48.3[47.39-49.21]Institution level (with multiple)4-year24.9[23.58-26.29]28.8[28.11-29.43]35.5[34.06-36.90]42.1[41.46-42.81]46.7[46.02-47.30]47.9[47.33-48.44]2-year2.1[1.68-2.51]3.4[3.05-3.85]5.4[4.89-6.01]6.7[6.09-7.42]9.3[8.87-9.66]9.1[8.77-9.53]Less than 2-year12.8[7.85-20.05]22.8[14.15-34.58]24.3[23.17-25.54]32.8[30.28-35.44]39.5[36.25-42.77]33.3[30.69-36.00]Attended more than one institution13.8[12.64-15.05]21.4[20.03-22.93]20.2[18.54-21.91]29.3[28.06-30.48]33.8[32.08-35.61]37.2[35.81-38.69]‡ Reporting standards not met.‡ Reporting standards not met.STDERR-SOURCE-END‡ Reporting standards not met.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: ENRLSIZE.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: TOTAID, ENRLSIZE and AIDLEVL. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: TOTAID (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), ENRLSIZE (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and AIDLEVL (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96), 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebkp39bfebkp395Median>0 Grade point average by Veteran status for years 2004, 2008, 2012 and 2016 Grade point average(Median>0)EstimatesTotal2004300.02008300.02012302.02016302.0Veteran statusVeteran2004330.02008311.02012320.02016306.0Not a veteran2004300.02008300.02012302.02016301.0Median>0 Grade point average by Veteran status for years 2004, 2008, 2012 and 2016 Grade point average(Median>0)EstimatesTotal2004300.02008300.02012302.02016302.0Veteran statusVeteran2004330.02008311.02012320.02016306.0Not a veteran2004300.02008300.02012302.02016301.0Standard Error (BRR)Total20041.1920080.4720121.0420161.44Veteran statusVeteran20044.1020085.4520123.9620163.63Not a veteran20040.4920080.3720121.0320161.26Relative Standard Error (%)Total20040.4020080.1620120.3420160.48Veteran statusVeteran20041.2420081.7520121.2420161.19Not a veteran20040.1620080.1220120.3420160.42Weighted Sample Sizes (n/1,000s)Total200419,044.3200820,688.3201221,880.9201619,138.6Veteran statusVeteran2004622.02008688.42012812.92016921.6Not a veteran200418,422.3200819,999.9201221,068.0201618,217.0Median>0 Grade point average by Veteran status for years 2004, 2008, 2012 and 2016 Grade point average(Median>0)Amt.95% CIEstimatesTotal2004300.0[297.65-302.35]2008300.0[299.08-300.92]2012302.0[299.95-304.05]2016302.0[299.15-304.85]Veteran statusVeteran2004330.0[321.91-338.09]2008311.0[300.26-321.74]2012320.0[312.20-327.80]2016306.0[298.84-313.16]Not a veteran2004300.0[299.03-300.97]2008300.0[299.26-300.74]2012302.0[299.96-304.04]2016301.0[298.52-303.48]2004200820122016 Grade point averageGrade point averageGrade point averageGrade point average (Median>0)(Median>0)(Median>0)(Median>0)EstimatesTotal300.0300.0302.0302.0Veteran statusVeteran330.0311.0320.0306.0Not a veteran300.0300.0302.0301.02004200820122016 Grade point averageGrade point averageGrade point averageGrade point average (Median>0)(Median>0)(Median>0)(Median>0)EstimatesTotal300.0300.0302.0302.0Veteran statusVeteran330.0311.0320.0306.0Not a veteran300.0300.0302.0301.0Standard Error (BRR)Total1.190.471.041.44Veteran statusVeteran4.105.453.963.63Not a veteran0.490.371.031.26Relative Standard Error (%)Total0.400.160.340.48Veteran statusVeteran1.241.751.241.19Not a veteran0.160.120.340.42Weighted Sample Sizes (n/1,000s)Total19,044.320,688.321,880.919,138.6Veteran statusVeteran622.0688.4812.9921.6Not a veteran18,422.319,999.921,068.018,217.02004200820122016 Grade point averageGrade point averageGrade point averageGrade point average (Median>0)(Median>0)(Median>0)(Median>0) Amt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIEstimatesTotal300.0[297.65-302.35]300.0[299.08-300.92]302.0[299.95-304.05]302.0[299.15-304.85]Veteran statusVeteran330.0[321.91-338.09]311.0[300.26-321.74]320.0[312.20-327.80]306.0[298.84-313.16]Not a veteran300.0[299.03-300.97]300.0[299.26-300.74]302.0[299.96-304.04]301.0[298.52-303.48]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: GPA and VETERAN.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: GPA and VETERAN. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: GPA (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and VETERAN (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebkp72bfebkp721Age by Institution sector (4 with multiple) for years 2000, 2004, 2008, 2012 and 2016 Age18 or younger19-2324-2930-3940 or olderTotalEstimatesTotal200010.047.716.713.711.9100%200410.147.616.913.412.1100%20089.748.617.913.010.8100%20129.047.218.414.011.4100%20169.349.618.313.49.3100%Institution sector (4 with multiple)Public 4-year200011.259.215.48.35.9100%200411.460.615.17.35.6100%200811.061.715.27.25.0100%201211.058.615.89.05.6100%201611.059.715.58.65.2100%Private not-for-profit 4-year200013.056.911.110.68.5100%200412.755.311.710.69.7100%200813.155.412.210.78.6100%201213.657.69.99.19.9100%201611.355.512.511.88.9100%Public 2-year20008.636.418.318.518.1100%20049.437.917.917.417.4100%20089.240.319.115.915.5100%20128.440.720.715.714.4100%20169.242.120.715.812.3100%Private for-profit20007.138.028.917.09.1100%20046.332.427.120.913.4100%20085.632.328.221.612.3100%20123.428.225.725.017.7100%20163.729.127.924.015.4100%Others or attended more than one school20009.053.115.512.69.8100%20048.050.217.112.911.8100%20088.053.317.711.89.2100%20127.252.717.813.19.2100%20166.854.018.213.47.5100%Age by Institution sector (4 with multiple) for years 2000, 2004, 2008, 2012 and 2016 Age18 or younger19-2324-2930-3940 or olderTotalEstimatesTotal200010.047.716.713.711.9100%200410.147.616.913.412.1100%20089.748.617.913.010.8100%20129.047.218.414.011.4100%20169.349.618.313.49.3100%Institution sector (4 with multiple)Public 4-year200011.259.215.48.35.9100%200411.460.615.17.35.6100%200811.061.715.27.25.0100%201211.058.615.89.05.6100%201611.059.715.58.65.2100%Private not-for-profit 4-year200013.056.911.110.68.5100%200412.755.311.710.69.7100%200813.155.412.210.78.6100%201213.657.69.99.19.9100%201611.355.512.511.88.9100%Public 2-year20008.636.418.318.518.1100%20049.437.917.917.417.4100%20089.240.319.115.915.5100%20128.440.720.715.714.4100%20169.242.120.715.812.3100%Private for-profit20007.138.028.917.09.1100%20046.332.427.120.913.4100%20085.632.328.221.612.3100%20123.428.225.725.017.7100%20163.729.127.924.015.4100%Others or attended more than one school20009.053.115.512.69.8100%20048.050.217.112.911.8100%20088.053.317.711.89.2100%20127.252.717.813.19.2100%20166.854.018.213.47.5100%Standard Error (BRR)Total20000.200.410.310.270.31 20040.260.520.240.250.30 20080.150.260.190.190.19 20120.130.300.220.200.26 20160.150.320.220.210.18 Institution sector (4 with multiple)Public 4-year20000.330.500.380.260.24 20040.410.800.440.350.27 20080.250.470.290.250.20 20120.210.550.380.300.27 20160.320.570.350.320.24 Private not-for-profit 4-year20000.501.050.440.620.43 20040.491.490.630.640.88 20080.420.820.450.460.53 20120.440.850.610.630.70 20160.430.880.470.460.48 Public 2-year20000.360.720.560.530.63 20040.440.560.460.400.54 20080.300.420.310.300.37 20120.240.550.420.350.40 20160.290.560.450.410.35 Private for-profit20000.521.671.040.950.89 20040.521.200.860.880.94 20080.481.120.921.050.80 20120.200.590.590.570.61 20160.280.660.630.480.59 Others or attended more than one school20000.580.930.530.610.61 20040.370.680.550.460.47 20080.300.630.460.480.42 20120.290.810.600.540.42 20160.311.440.770.740.40 Relative Standard Error (%)Total20002.000.861.831.982.63 20042.531.091.401.892.48 20081.590.531.081.451.74 20121.450.631.211.402.25 20161.610.641.231.581.89 Institution sector (4 with multiple)Public 4-year20002.960.852.453.134.07 20043.571.332.894.754.80 20082.240.771.933.504.08 20121.940.932.373.384.81 20162.930.962.283.704.65 Private not-for-profit 4-year20003.851.853.935.895.06 20043.852.705.386.029.05 20083.231.483.644.316.16 20123.231.476.156.947.09 20163.811.583.753.895.40 Public 2-year20004.201.973.072.863.48 20044.701.472.592.283.09 20083.311.041.611.862.39 20122.811.352.022.242.74 20163.151.322.162.612.87 Private for-profit20007.384.413.595.569.85 20048.363.713.194.237.02 20088.563.473.254.876.45 20125.952.112.282.293.46 20167.472.282.262.023.87 Others or attended more than one school20006.401.753.444.856.17 20044.651.363.213.613.94 20083.701.172.614.074.55 20124.101.543.384.114.53 20164.492.664.225.495.32 Weighted Sample Sizes (n/1,000s)Total200016,579.2 200419,053.8 200820,762.3 201223,055.4 201619,532.3 Institution sector (4 with multiple)Public 4-year20005,195.6 20045,705.6 20085,886.9 20126,538.6 20166,155.9 Private not-for-profit 4-year20002,333.9 20042,582.1 20082,601.1 20122,687.1 20162,706.4 Public 2-year20007,044.5 20047,777.3 20088,362.3 20128,787.7 20166,897.0 Private for-profit2000811.0 20041,468.0 20082,139.3 20122,972.1 20161,718.1 Others or attended more than one school20001,194.1 20041,520.8 20081,772.7 20122,070.0 20162,054.9 Age by Institution sector (4 with multiple) for years 2000, 2004, 2008, 2012 and 2016 Age18 or younger19-2324-2930-3940 or olderTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal200010.0[9.61-10.41]47.7[46.90-48.56]16.7[16.08-17.31]13.7[13.17-14.26]11.9[11.26-12.52]100%200410.1[9.59-10.60]47.6[46.58-48.63]16.9[16.41-17.35]13.4[12.86-13.86]12.1[11.50-12.68]100%20089.7[9.41-10.01]48.6[48.05-49.06]17.9[17.55-18.31]13.0[12.66-13.41]10.8[10.42-11.16]100%20129.0[8.73-9.24]47.2[46.63-47.80]18.4[18.01-18.89]14.0[13.62-14.39]11.4[10.86-11.87]100%20169.3[9.03-9.62]49.6[49.01-50.26]18.3[17.84-18.73]13.4[13.02-13.86]9.3[8.99-9.68]100%Institution sector (4 with multiple)Public 4-year200011.2[10.54-11.87]59.2[58.20-60.21]15.4[14.65-16.17]8.3[7.83-8.89]5.9[5.40-6.36]100%200411.4[10.63-12.24]60.6[58.96-62.13]15.1[14.30-16.02]7.3[6.65-8.02]5.6[5.09-6.15]100%200811.0[10.49-11.46]61.7[60.72-62.59]15.2[14.61-15.77]7.2[6.75-7.75]5.0[4.57-5.37]100%201211.0[10.55-11.38]58.6[57.49-59.64]15.8[15.10-16.58]9.0[8.43-9.63]5.6[5.13-6.20]100%201611.0[10.40-11.68]59.7[58.59-60.86]15.5[14.81-16.20]8.6[7.98-9.24]5.2[4.71-5.65]100%Private not-for-profit 4-year200013.0[12.04-14.06]56.9[54.73-58.96]11.1[10.22-11.97]10.6[9.38-11.88]8.5[7.67-9.40]100%200412.7[11.74-13.67]55.3[52.32-58.20]11.7[10.55-13.04]10.6[9.43-11.95]9.7[8.09-11.56]100%200813.1[12.28-13.95]55.4[53.82-57.06]12.2[11.37-13.13]10.7[9.81-11.63]8.6[7.57-9.65]100%201213.6[12.75-14.48]57.6[55.90-59.23]9.9[8.77-11.18]9.1[7.89-10.37]9.9[8.58-11.34]100%201611.3[10.49-12.19]55.5[53.80-57.26]12.5[11.59-13.44]11.8[10.90-12.70]8.9[7.99-9.89]100%Public 2-year20008.6[7.94-9.39]36.4[35.02-37.90]18.3[17.19-19.45]18.5[17.48-19.61]18.1[16.87-19.40]100%20049.4[8.55-10.30]37.9[36.83-39.03]17.9[16.98-18.81]17.4[16.61-18.17]17.4[16.39-18.52]100%20089.2[8.60-9.79]40.3[39.51-41.17]19.1[18.45-19.66]15.9[15.33-16.50]15.5[14.81-16.28]100%20128.4[7.95-8.88]40.7[39.66-41.83]20.7[19.88-21.54]15.7[15.06-16.45]14.4[13.66-15.22]100%20169.2[8.62-9.76]42.1[41.01-43.20]20.7[19.82-21.58]15.8[14.99-16.62]12.3[11.57-12.96]100%Private for-profit20007.1[6.11-8.22]38.0[34.66-41.38]28.9[26.83-30.99]17.0[15.19-18.99]9.1[7.44-11.04]100%20046.3[5.32-7.39]32.4[30.09-34.83]27.1[25.38-28.79]20.9[19.18-22.66]13.4[11.65-15.36]100%20085.6[4.73-6.63]32.3[30.13-34.54]28.2[26.42-30.03]21.6[19.58-23.73]12.3[10.84-13.98]100%20123.4[3.06-3.86]28.2[27.05-29.40]25.7[24.58-26.89]25.0[23.86-26.11]17.7[16.49-18.90]100%20163.7[3.18-4.27]29.1[27.85-30.47]27.9[26.64-29.13]24.0[23.01-24.92]15.4[14.22-16.56]100%Others or attended more than one school20009.0[7.90-10.22]53.1[51.26-54.98]15.5[14.47-16.61]12.6[11.39-13.84]9.8[8.66-11.09]100%20048.0[7.30-8.76]50.2[48.82-51.52]17.1[16.08-18.25]12.9[11.97-13.80]11.8[10.94-12.78]100%20088.0[7.42-8.58]53.3[52.09-54.55]17.7[16.82-18.64]11.8[10.92-12.82]9.2[8.37-10.01]100%20127.2[6.64-7.80]52.7[51.06-54.26]17.8[16.67-19.05]13.1[12.04-14.16]9.2[8.45-10.10]100%20166.8[6.23-7.44]54.0[51.20-56.86]18.2[16.70-19.72]13.4[12.05-14.96]7.5[6.79-8.38]100%20002004200820122016 AgeAgeAgeAgeAge 18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or olderEstimatesTotal10.047.716.713.711.910.147.616.913.412.19.748.617.913.010.89.047.218.414.011.49.349.618.313.49.3Institution sector (4 with multiple)Public 4-year11.259.215.48.35.911.460.615.17.35.611.061.715.27.25.011.058.615.89.05.611.059.715.58.65.2Private not-for-profit 4-year13.056.911.110.68.512.755.311.710.69.713.155.412.210.78.613.657.69.99.19.911.355.512.511.88.9Public 2-year8.636.418.318.518.19.437.917.917.417.49.240.319.115.915.58.440.720.715.714.49.242.120.715.812.3Private for-profit7.138.028.917.09.16.332.427.120.913.45.632.328.221.612.33.428.225.725.017.73.729.127.924.015.4Others or attended more than one school9.053.115.512.69.88.050.217.112.911.88.053.317.711.89.27.252.717.813.19.26.854.018.213.47.520002004200820122016 AgeAgeAgeAgeAge 18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or olderEstimatesTotal10.047.716.713.711.910.147.616.913.412.19.748.617.913.010.89.047.218.414.011.49.349.618.313.49.3Institution sector (4 with multiple)Public 4-year11.259.215.48.35.911.460.615.17.35.611.061.715.27.25.011.058.615.89.05.611.059.715.58.65.2Private not-for-profit 4-year13.056.911.110.68.512.755.311.710.69.713.155.412.210.78.613.657.69.99.19.911.355.512.511.88.9Public 2-year8.636.418.318.518.19.437.917.917.417.49.240.319.115.915.58.440.720.715.714.49.242.120.715.812.3Private for-profit7.138.028.917.09.16.332.427.120.913.45.632.328.221.612.33.428.225.725.017.73.729.127.924.015.4Others or attended more than one school9.053.115.512.69.88.050.217.112.911.88.053.317.711.89.27.252.717.813.19.26.854.018.213.47.5Standard Error (BRR)Total0.200.410.310.270.310.260.520.240.250.300.150.260.190.190.190.130.300.220.200.260.150.320.220.210.18Institution sector (4 with multiple)Public 4-year0.330.500.380.260.240.410.800.440.350.270.250.470.290.250.200.210.550.380.300.270.320.570.350.320.24Private not-for-profit 4-year0.501.050.440.620.430.491.490.630.640.880.420.820.450.460.530.440.850.610.630.700.430.880.470.460.48Public 2-year0.360.720.560.530.630.440.560.460.400.540.300.420.310.300.370.240.550.420.350.400.290.560.450.410.35Private for-profit0.521.671.040.950.890.521.200.860.880.940.481.120.921.050.800.200.590.590.570.610.280.660.630.480.59Others or attended more than one school0.580.930.530.610.610.370.680.550.460.470.300.630.460.480.420.290.810.600.540.420.311.440.770.740.40Relative Standard Error (%)Total2.000.861.831.982.632.531.091.401.892.481.590.531.081.451.741.450.631.211.402.251.610.641.231.581.89Institution sector (4 with multiple)Public 4-year2.960.852.453.134.073.571.332.894.754.802.240.771.933.504.081.940.932.373.384.812.930.962.283.704.65Private not-for-profit 4-year3.851.853.935.895.063.852.705.386.029.053.231.483.644.316.163.231.476.156.947.093.811.583.753.895.40Public 2-year4.201.973.072.863.484.701.472.592.283.093.311.041.611.862.392.811.352.022.242.743.151.322.162.612.87Private for-profit7.384.413.595.569.858.363.713.194.237.028.563.473.254.876.455.952.112.282.293.467.472.282.262.023.87Others or attended more than one school6.401.753.444.856.174.651.363.213.613.943.701.172.614.074.554.101.543.384.114.534.492.664.225.495.32Weighted Sample Sizes (n/1,000s)Total16,579.2 19,053.8 20,762.3 23,055.4 19,532.3 Institution sector (4 with multiple)Public 4-year5,195.6 5,705.6 5,886.9 6,538.6 6,155.9 Private not-for-profit 4-year2,333.9 2,582.1 2,601.1 2,687.1 2,706.4 Public 2-year7,044.5 7,777.3 8,362.3 8,787.7 6,897.0 Private for-profit811.0 1,468.0 2,139.3 2,972.1 1,718.1 Others or attended more than one school1,194.1 1,520.8 1,772.7 2,070.0 2,054.9 20002004200820122016 AgeAgeAgeAgeAge 18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or older18 or younger19-2324-2930-3940 or older Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal10.0[9.61-10.41]47.7[46.90-48.56]16.7[16.08-17.31]13.7[13.17-14.26]11.9[11.26-12.52]10.1[9.59-10.60]47.6[46.58-48.63]16.9[16.41-17.35]13.4[12.86-13.86]12.1[11.50-12.68]9.7[9.41-10.01]48.6[48.05-49.06]17.9[17.55-18.31]13.0[12.66-13.41]10.8[10.42-11.16]9.0[8.73-9.24]47.2[46.63-47.80]18.4[18.01-18.89]14.0[13.62-14.39]11.4[10.86-11.87]9.3[9.03-9.62]49.6[49.01-50.26]18.3[17.84-18.73]13.4[13.02-13.86]9.3[8.99-9.68]Institution sector (4 with multiple)Public 4-year11.2[10.54-11.87]59.2[58.20-60.21]15.4[14.65-16.17]8.3[7.83-8.89]5.9[5.40-6.36]11.4[10.63-12.24]60.6[58.96-62.13]15.1[14.30-16.02]7.3[6.65-8.02]5.6[5.09-6.15]11.0[10.49-11.46]61.7[60.72-62.59]15.2[14.61-15.77]7.2[6.75-7.75]5.0[4.57-5.37]11.0[10.55-11.38]58.6[57.49-59.64]15.8[15.10-16.58]9.0[8.43-9.63]5.6[5.13-6.20]11.0[10.40-11.68]59.7[58.59-60.86]15.5[14.81-16.20]8.6[7.98-9.24]5.2[4.71-5.65]Private not-for-profit 4-year13.0[12.04-14.06]56.9[54.73-58.96]11.1[10.22-11.97]10.6[9.38-11.88]8.5[7.67-9.40]12.7[11.74-13.67]55.3[52.32-58.20]11.7[10.55-13.04]10.6[9.43-11.95]9.7[8.09-11.56]13.1[12.28-13.95]55.4[53.82-57.06]12.2[11.37-13.13]10.7[9.81-11.63]8.6[7.57-9.65]13.6[12.75-14.48]57.6[55.90-59.23]9.9[8.77-11.18]9.1[7.89-10.37]9.9[8.58-11.34]11.3[10.49-12.19]55.5[53.80-57.26]12.5[11.59-13.44]11.8[10.90-12.70]8.9[7.99-9.89]Public 2-year8.6[7.94-9.39]36.4[35.02-37.90]18.3[17.19-19.45]18.5[17.48-19.61]18.1[16.87-19.40]9.4[8.55-10.30]37.9[36.83-39.03]17.9[16.98-18.81]17.4[16.61-18.17]17.4[16.39-18.52]9.2[8.60-9.79]40.3[39.51-41.17]19.1[18.45-19.66]15.9[15.33-16.50]15.5[14.81-16.28]8.4[7.95-8.88]40.7[39.66-41.83]20.7[19.88-21.54]15.7[15.06-16.45]14.4[13.66-15.22]9.2[8.62-9.76]42.1[41.01-43.20]20.7[19.82-21.58]15.8[14.99-16.62]12.3[11.57-12.96]Private for-profit7.1[6.11-8.22]38.0[34.66-41.38]28.9[26.83-30.99]17.0[15.19-18.99]9.1[7.44-11.04]6.3[5.32-7.39]32.4[30.09-34.83]27.1[25.38-28.79]20.9[19.18-22.66]13.4[11.65-15.36]5.6[4.73-6.63]32.3[30.13-34.54]28.2[26.42-30.03]21.6[19.58-23.73]12.3[10.84-13.98]3.4[3.06-3.86]28.2[27.05-29.40]25.7[24.58-26.89]25.0[23.86-26.11]17.7[16.49-18.90]3.7[3.18-4.27]29.1[27.85-30.47]27.9[26.64-29.13]24.0[23.01-24.92]15.4[14.22-16.56]Others or attended more than one school9.0[7.90-10.22]53.1[51.26-54.98]15.5[14.47-16.61]12.6[11.39-13.84]9.8[8.66-11.09]8.0[7.30-8.76]50.2[48.82-51.52]17.1[16.08-18.25]12.9[11.97-13.80]11.8[10.94-12.78]8.0[7.42-8.58]53.3[52.09-54.55]17.7[16.82-18.64]11.8[10.92-12.82]9.2[8.37-10.01]7.2[6.64-7.80]52.7[51.06-54.26]17.8[16.67-19.05]13.1[12.04-14.16]9.2[8.45-10.10]6.8[6.23-7.44]54.0[51.20-56.86]18.2[16.70-19.72]13.4[12.05-14.96]7.5[6.79-8.38]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: AGE and SECTOR4. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: AGE (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and SECTOR4 (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebknhp3dbfebknhp3d2Institution sector (4 with multiple) by Race/ethnicity (with multiple) for years 2000, 2004, 2008, 2012 and 2016 Institution sector (4 with multiple)Public 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolTotalEstimatesTotal200031.314.142.54.97.2100%200429.913.640.87.78.0100%200828.412.540.310.38.5100%201228.411.738.112.99.0100%201631.513.935.38.810.5100%Race/ethnicity (with multiple)White200032.914.741.53.97.0100%200433.414.438.85.77.7100%200830.613.739.48.08.2100%201230.513.136.710.88.9100%201633.716.033.66.810.0100%Black or African American200028.111.444.77.97.9100%200422.112.744.313.87.0100%200823.610.741.217.07.5100%201222.59.738.820.58.5100%201626.712.334.115.411.5100%Hispanic or Latino200025.714.045.68.16.7100%200420.812.946.311.88.2100%200823.610.341.315.29.6100%201224.57.444.114.99.2100%201627.99.841.110.710.5100%Asian200036.111.740.43.97.9100%200432.310.440.64.911.7100%200829.711.642.25.710.9100%201234.914.433.86.710.1100%201636.014.232.65.112.2100%American Indian or Alaska Native200022.713.353.53.2 !7.3100%200431.65.746.04.911.8100%200828.24.445.511.0 !10.9100%201230.75.033.619.611.1100%201629.35.245.58.012.0100%Native Hawaiian / other Pacific Islander200024.88.153.85.9 !7.3100%200423.96.051.68.010.4100%200817.810.348.610.6 !12.8100%201226.510.938.714.19.7100%201627.88.939.09.914.5100%Institution sector (4 with multiple) by Race/ethnicity (with multiple) for years 2000, 2004, 2008, 2012 and 2016 Institution sector (4 with multiple)Public 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolTotalEstimatesTotal200031.314.142.54.97.2100%200429.913.640.87.78.0100%200828.412.540.310.38.5100%201228.411.738.112.99.0100%201631.513.935.38.810.5100%Race/ethnicity (with multiple)White200032.914.741.53.97.0100%200433.414.438.85.77.7100%200830.613.739.48.08.2100%201230.513.136.710.88.9100%201633.716.033.66.810.0100%Black or African American200028.111.444.77.97.9100%200422.112.744.313.87.0100%200823.610.741.217.07.5100%201222.59.738.820.58.5100%201626.712.334.115.411.5100%Hispanic or Latino200025.714.045.68.16.7100%200420.812.946.311.88.2100%200823.610.341.315.29.6100%201224.57.444.114.99.2100%201627.99.841.110.710.5100%Asian200036.111.740.43.97.9100%200432.310.440.64.911.7100%200829.711.642.25.710.9100%201234.914.433.86.710.1100%201636.014.232.65.112.2100%American Indian or Alaska Native200022.713.353.53.2 !7.3100%200431.65.746.04.911.8100%200828.24.445.511.0 !10.9100%201230.75.033.619.611.1100%201629.35.245.58.012.0100%Native Hawaiian / other Pacific Islander200024.88.153.85.9 !7.3100%200423.96.051.68.010.4100%200817.810.348.610.6 !12.8100%201226.510.938.714.19.7100%201627.88.939.09.914.5100%Standard Error (BRR)Total20000.100.110.140.080.14 20040.500.270.820.131.65 20080.050.040.080.060.14 20120.100.080.140.030.28 20160.070.050.110.290.33 Race/ethnicity (with multiple)White20000.450.320.640.180.19 20040.750.371.300.211.69 20080.250.180.360.290.18 20120.350.250.410.240.28 20160.340.220.430.300.32 Black or African American20001.691.171.970.790.68 20041.841.261.970.991.40 20080.620.601.000.860.33 20120.710.521.010.720.42 20160.880.580.910.810.69 Hispanic or Latino20002.411.293.100.660.57 20040.881.941.350.721.56 20080.750.621.080.700.37 20121.110.481.360.690.56 20160.750.450.860.450.42 Asian20002.070.701.950.780.59 20041.890.681.960.552.29 20081.190.671.360.800.61 20121.210.921.490.740.79 20161.060.741.180.570.51 American Indian or Alaska Native20003.403.365.271.111.20 20046.481.305.891.122.93 20083.420.904.923.591.71 20123.301.304.082.702.50 20163.661.233.561.111.84 Native Hawaiian / other Pacific Islander20004.031.715.002.001.93 20043.181.324.681.712.75 20082.262.583.843.293.08 20123.762.363.922.481.88 20163.832.184.731.472.58 Relative Standard Error (%)Total20000.320.790.321.622.01 20041.681.982.011.6620.63 20080.190.350.200.601.70 20120.360.660.360.233.13 20160.220.350.303.343.10 Race/ethnicity (with multiple)White20001.352.151.554.642.66 20042.252.563.363.6021.90 20080.811.280.913.572.15 20121.161.871.112.193.17 20161.001.401.274.373.17 Black or African American20006.0210.204.4210.038.67 20048.339.924.457.1420.05 20082.655.652.435.064.32 20123.145.342.603.504.97 20163.314.712.675.245.98 Hispanic or Latino20009.419.236.798.098.56 20044.2115.012.916.0919.06 20083.156.012.604.633.91 20124.536.543.094.676.06 20162.694.582.084.194.03 Asian20005.746.004.8320.137.42 20045.866.554.8311.1619.48 20084.005.813.2314.085.55 20123.486.414.4111.107.85 20162.955.183.6311.274.20 American Indian or Alaska Native200014.9625.229.8534.7916.53 200420.4922.8512.8222.9024.74 200812.1220.5910.8232.5415.64 201210.7526.0312.1313.7522.62 201612.5023.457.8213.9415.41 Native Hawaiian / other Pacific Islander200016.2421.109.2833.9426.33 200413.2921.749.0621.4426.42 200812.7025.187.9131.2024.10 201214.1721.6510.1317.6419.25 201613.7824.6212.1314.8717.82 Weighted Sample Sizes (n/1,000s)Total200016,579.2 200419,053.8 200820,762.3 201223,055.4 201619,532.3 Race/ethnicity (with multiple)White200011,017.9 200411,982.4 200812,708.8 201213,345.5 201610,277.0 Black or African American20002,024.8 20042,674.5 20082,991.8 20123,708.8 20163,007.1 Hispanic or Latino20001,914.1 20042,456.4 20082,966.4 20123,696.0 20163,944.8 Asian2000866.0 20041,029.2 20081,219.8 20121,291.6 20161,399.3 American Indian or Alaska Native2000155.9 2004175.2 2008173.0 2012208.8 2016159.9 Native Hawaiian / other Pacific Islander2000127.4 200499.9 2008149.0 2012118.5 201682.9 Institution sector (4 with multiple) by Race/ethnicity (with multiple) for years 2000, 2004, 2008, 2012 and 2016 Institution sector (4 with multiple)Public 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal200031.3[31.14-31.54]14.1[13.86-14.30]42.5[42.22-42.77]4.9[4.74-5.05]7.2[6.92-7.50]100%200429.9[28.96-30.95]13.6[13.03-14.09]40.8[39.21-42.45]7.7[7.46-7.96]8.0[5.28-11.89]100%200828.4[28.25-28.46]12.5[12.44-12.62]40.3[40.12-40.44]10.3[10.18-10.43]8.5[8.26-8.83]100%201228.4[28.16-28.56]11.7[11.50-11.81]38.1[37.85-38.39]12.9[12.83-12.95]9.0[8.44-9.55]100%201631.5[31.38-31.66]13.9[13.76-13.95]35.3[35.10-35.52]8.8[8.23-9.39]10.5[9.89-11.18]100%Race/ethnicity (with multiple)White200032.9[32.05-33.83]14.7[14.08-15.35]41.5[40.21-42.79]3.9[3.52-4.24]7.0[6.64-7.39]100%200433.4[31.94-34.90]14.4[13.68-15.14]38.8[36.24-41.37]5.7[5.31-6.12]7.7[4.98-11.79]100%200830.6[30.14-31.12]13.7[13.40-14.10]39.4[38.74-40.15]8.0[7.44-8.57]8.2[7.85-8.55]100%201230.5[29.76-31.16]13.1[12.64-13.61]36.7[35.94-37.54]10.8[10.34-11.27]8.9[8.35-9.47]100%201633.7[33.01-34.35]16.0[15.59-16.47]33.6[32.76-34.43]6.8[6.20-7.37]10.0[9.35-10.59]100%Black or African American200028.1[24.82-31.61]11.4[9.30-14.00]44.7[40.78-48.69]7.9[6.44-9.64]7.9[6.60-9.35]100%200422.1[18.69-25.95]12.7[10.45-15.44]44.3[40.48-48.24]13.8[11.99-15.89]7.0[4.69-10.33]100%200823.6[22.36-24.82]10.7[9.57-11.96]41.2[39.27-43.22]17.0[15.34-18.73]7.5[6.91-8.20]100%201222.5[21.12-23.91]9.7[8.72-10.77]38.8[36.87-40.84]20.5[19.13-21.96]8.5[7.67-9.34]100%201626.7[25.02-28.51]12.3[11.20-13.49]34.1[32.31-35.90]15.4[13.87-17.05]11.5[10.21-12.92]100%Hispanic or Latino200025.7[21.11-30.79]14.0[11.56-16.75]45.6[39.50-51.89]8.1[6.87-9.52]6.7[5.61-7.91]100%200420.8[19.13-22.58]12.9[9.55-17.24]46.3[43.63-48.94]11.8[10.47-13.31]8.2[5.59-11.84]100%200823.6[22.19-25.13]10.3[9.11-11.55]41.3[39.24-43.48]15.2[13.87-16.65]9.6[8.84-10.32]100%201224.5[22.34-26.71]7.4[6.47-8.37]44.1[41.45-46.83]14.9[13.57-16.31]9.2[8.13-10.33]100%201627.9[26.40-29.36]9.8[8.97-10.75]41.1[39.46-42.84]10.7[9.81-11.57]10.5[9.71-11.39]100%Asian200036.1[32.04-40.34]11.7[10.35-13.17]40.4[36.58-44.41]3.9[2.57-5.77]7.9[6.83-9.20]100%200432.3[28.67-36.12]10.4[9.14-11.84]40.6[36.82-44.55]4.9[3.96-6.15]11.7[7.92-17.04]100%200829.7[27.38-32.06]11.6[10.32-12.97]42.2[39.54-44.92]5.7[4.27-7.44]10.9[9.76-12.15]100%201234.9[32.59-37.38]14.4[12.70-16.35]33.8[30.95-36.83]6.7[5.38-8.33]10.1[8.63-11.76]100%201636.0[33.90-38.09]14.2[12.82-15.73]32.6[30.30-34.97]5.1[4.06-6.33]12.2[11.18-13.19]100%American Indian or Alaska Native200022.7[16.61-30.24]13.3[7.89-21.61]53.5[42.91-63.77]3.2 ![1.58-6.38]7.3[5.19-10.08]100%200431.6[20.38-45.50]5.7[3.60-8.86]46.0[34.75-57.59]4.9[3.11-7.66]11.8[7.17-18.94]100%200828.2[21.96-35.39]4.4[2.89-6.51]45.5[36.06-55.22]11.0 ![5.69-20.33]10.9[7.99-14.80]100%201230.7[24.62-37.60]5.0[2.97-8.27]33.6[26.09-42.06]19.6[14.83-25.48]11.1[7.00-17.03]100%201629.3[22.65-37.03]5.2[3.29-8.27]45.5[38.62-52.56]8.0[6.04-10.46]12.0[8.77-16.09]100%Native Hawaiian / other Pacific Islander200024.8[17.61-33.74]8.1[5.28-12.30]53.8[43.78-63.60]5.9 ![2.95-11.45]7.3[4.28-12.27]100%200423.9[18.23-30.74]6.0[3.92-9.23]51.6[42.46-60.72]8.0[5.19-12.06]10.4[6.10-17.20]100%200817.8[13.79-22.74]10.3[6.16-16.57]48.6[41.09-56.12]10.6 ![5.60-19.01]12.8[7.84-20.19]100%201226.5[19.80-34.56]10.9[7.06-16.53]38.7[31.33-46.69]14.1[9.85-19.72]9.7[6.62-14.11]100%201627.8[20.88-35.89]8.9[5.40-14.22]39.0[30.14-48.59]9.9[7.35-13.20]14.5[10.10-20.36]100%20002004200820122016 Institution sector (4 with multiple)Institution sector (4 with multiple)Institution sector (4 with multiple)Institution sector (4 with multiple)Institution sector (4 with multiple) Public 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolEstimatesTotal31.314.142.54.97.229.913.640.87.78.028.412.540.310.38.528.411.738.112.99.031.513.935.38.810.5Race/ethnicity (with multiple)White32.914.741.53.97.033.414.438.85.77.730.613.739.48.08.230.513.136.710.88.933.716.033.66.810.0Black or African American28.111.444.77.97.922.112.744.313.87.023.610.741.217.07.522.59.738.820.58.526.712.334.115.411.5Hispanic or Latino25.714.045.68.16.720.812.946.311.88.223.610.341.315.29.624.57.444.114.99.227.99.841.110.710.5Asian36.111.740.43.97.932.310.440.64.911.729.711.642.25.710.934.914.433.86.710.136.014.232.65.112.2American Indian or Alaska Native22.713.353.53.27.331.65.746.04.911.828.24.445.511.010.930.75.033.619.611.129.35.245.58.012.0Native Hawaiian / other Pacific Islander24.88.153.85.97.323.96.051.68.010.417.810.348.610.612.826.510.938.714.19.727.88.939.09.914.520002004200820122016 Institution sector (4 with multiple)Institution sector (4 with multiple)Institution sector (4 with multiple)Institution sector (4 with multiple)Institution sector (4 with multiple) Public 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolEstimatesTotal31.314.142.54.97.229.913.640.87.78.028.412.540.310.38.528.411.738.112.99.031.513.935.38.810.5Race/ethnicity (with multiple)White32.914.741.53.97.033.414.438.85.77.730.613.739.48.08.230.513.136.710.88.933.716.033.66.810.0Black or African American28.111.444.77.97.922.112.744.313.87.023.610.741.217.07.522.59.738.820.58.526.712.334.115.411.5Hispanic or Latino25.714.045.68.16.720.812.946.311.88.223.610.341.315.29.624.57.444.114.99.227.99.841.110.710.5Asian36.111.740.43.97.932.310.440.64.911.729.711.642.25.710.934.914.433.86.710.136.014.232.65.112.2American Indian or Alaska Native22.713.353.53.27.331.65.746.04.911.828.24.445.511.010.930.75.033.619.611.129.35.245.58.012.0Native Hawaiian / other Pacific Islander24.88.153.85.97.323.96.051.68.010.417.810.348.610.612.826.510.938.714.19.727.88.939.09.914.5Standard Error (BRR)Total0.100.110.140.080.140.500.270.820.131.650.050.040.080.060.140.100.080.140.030.280.070.050.110.290.33Race/ethnicity (with multiple)White0.450.320.640.180.190.750.371.300.211.690.250.180.360.290.180.350.250.410.240.280.340.220.430.300.32Black or African American1.691.171.970.790.681.841.261.970.991.400.620.601.000.860.330.710.521.010.720.420.880.580.910.810.69Hispanic or Latino2.411.293.100.660.570.881.941.350.721.560.750.621.080.700.371.110.481.360.690.560.750.450.860.450.42Asian2.070.701.950.780.591.890.681.960.552.291.190.671.360.800.611.210.921.490.740.791.060.741.180.570.51American Indian or Alaska Native3.403.365.271.111.206.481.305.891.122.933.420.904.923.591.713.301.304.082.702.503.661.233.561.111.84Native Hawaiian / other Pacific Islander4.031.715.002.001.933.181.324.681.712.752.262.583.843.293.083.762.363.922.481.883.832.184.731.472.58Relative Standard Error (%)Total0.320.790.321.622.011.681.982.011.6620.630.190.350.200.601.700.360.660.360.233.130.220.350.303.343.10Race/ethnicity (with multiple)White1.352.151.554.642.662.252.563.363.6021.900.811.280.913.572.151.161.871.112.193.171.001.401.274.373.17Black or African American6.0210.204.4210.038.678.339.924.457.1420.052.655.652.435.064.323.145.342.603.504.973.314.712.675.245.98Hispanic or Latino9.419.236.798.098.564.2115.012.916.0919.063.156.012.604.633.914.536.543.094.676.062.694.582.084.194.03Asian5.746.004.8320.137.425.866.554.8311.1619.484.005.813.2314.085.553.486.414.4111.107.852.955.183.6311.274.20American Indian or Alaska Native14.9625.229.8534.7916.5320.4922.8512.8222.9024.7412.1220.5910.8232.5415.6410.7526.0312.1313.7522.6212.5023.457.8213.9415.41Native Hawaiian / other Pacific Islander16.2421.109.2833.9426.3313.2921.749.0621.4426.4212.7025.187.9131.2024.1014.1721.6510.1317.6419.2513.7824.6212.1314.8717.82Weighted Sample Sizes (n/1,000s)Total16,579.2 19,053.8 20,762.3 23,055.4 19,532.3 Race/ethnicity (with multiple)White11,017.9 11,982.4 12,708.8 13,345.5 10,277.0 Black or African American2,024.8 2,674.5 2,991.8 3,708.8 3,007.1 Hispanic or Latino1,914.1 2,456.4 2,966.4 3,696.0 3,944.8 Asian866.0 1,029.2 1,219.8 1,291.6 1,399.3 American Indian or Alaska Native155.9 175.2 173.0 208.8 159.9 Native Hawaiian / other Pacific Islander127.4 99.9 149.0 118.5 82.9 20002004200820122016 Institution sector (4 with multiple)Institution sector (4 with multiple)Institution sector (4 with multiple)Institution sector (4 with multiple)Institution sector (4 with multiple) Public 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one schoolPublic 4-yearPrivate not-for-profit 4-yearPublic 2-yearPrivate for-profitOthers or attended more than one school Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal31.3[31.14-31.54]14.1[13.86-14.30]42.5[42.22-42.77]4.9[4.74-5.05]7.2[6.92-7.50]29.9[28.96-30.95]13.6[13.03-14.09]40.8[39.21-42.45]7.7[7.46-7.96]8.0[5.28-11.89]28.4[28.25-28.46]12.5[12.44-12.62]40.3[40.12-40.44]10.3[10.18-10.43]8.5[8.26-8.83]28.4[28.16-28.56]11.7[11.50-11.81]38.1[37.85-38.39]12.9[12.83-12.95]9.0[8.44-9.55]31.5[31.38-31.66]13.9[13.76-13.95]35.3[35.10-35.52]8.8[8.23-9.39]10.5[9.89-11.18]Race/ethnicity (with multiple)White32.9[32.05-33.83]14.7[14.08-15.35]41.5[40.21-42.79]3.9[3.52-4.24]7.0[6.64-7.39]33.4[31.94-34.90]14.4[13.68-15.14]38.8[36.24-41.37]5.7[5.31-6.12]7.7[4.98-11.79]30.6[30.14-31.12]13.7[13.40-14.10]39.4[38.74-40.15]8.0[7.44-8.57]8.2[7.85-8.55]30.5[29.76-31.16]13.1[12.64-13.61]36.7[35.94-37.54]10.8[10.34-11.27]8.9[8.35-9.47]33.7[33.01-34.35]16.0[15.59-16.47]33.6[32.76-34.43]6.8[6.20-7.37]10.0[9.35-10.59]Black or African American28.1[24.82-31.61]11.4[9.30-14.00]44.7[40.78-48.69]7.9[6.44-9.64]7.9[6.60-9.35]22.1[18.69-25.95]12.7[10.45-15.44]44.3[40.48-48.24]13.8[11.99-15.89]7.0[4.69-10.33]23.6[22.36-24.82]10.7[9.57-11.96]41.2[39.27-43.22]17.0[15.34-18.73]7.5[6.91-8.20]22.5[21.12-23.91]9.7[8.72-10.77]38.8[36.87-40.84]20.5[19.13-21.96]8.5[7.67-9.34]26.7[25.02-28.51]12.3[11.20-13.49]34.1[32.31-35.90]15.4[13.87-17.05]11.5[10.21-12.92]Hispanic or Latino25.7[21.11-30.79]14.0[11.56-16.75]45.6[39.50-51.89]8.1[6.87-9.52]6.7[5.61-7.91]20.8[19.13-22.58]12.9[9.55-17.24]46.3[43.63-48.94]11.8[10.47-13.31]8.2[5.59-11.84]23.6[22.19-25.13]10.3[9.11-11.55]41.3[39.24-43.48]15.2[13.87-16.65]9.6[8.84-10.32]24.5[22.34-26.71]7.4[6.47-8.37]44.1[41.45-46.83]14.9[13.57-16.31]9.2[8.13-10.33]27.9[26.40-29.36]9.8[8.97-10.75]41.1[39.46-42.84]10.7[9.81-11.57]10.5[9.71-11.39]Asian36.1[32.04-40.34]11.7[10.35-13.17]40.4[36.58-44.41]3.9[2.57-5.77]7.9[6.83-9.20]32.3[28.67-36.12]10.4[9.14-11.84]40.6[36.82-44.55]4.9[3.96-6.15]11.7[7.92-17.04]29.7[27.38-32.06]11.6[10.32-12.97]42.2[39.54-44.92]5.7[4.27-7.44]10.9[9.76-12.15]34.9[32.59-37.38]14.4[12.70-16.35]33.8[30.95-36.83]6.7[5.38-8.33]10.1[8.63-11.76]36.0[33.90-38.09]14.2[12.82-15.73]32.6[30.30-34.97]5.1[4.06-6.33]12.2[11.18-13.19]American Indian or Alaska Native22.7[16.61-30.24]13.3[7.89-21.61]53.5[42.91-63.77]3.2 ![1.58-6.38]7.3[5.19-10.08]31.6[20.38-45.50]5.7[3.60-8.86]46.0[34.75-57.59]4.9[3.11-7.66]11.8[7.17-18.94]28.2[21.96-35.39]4.4[2.89-6.51]45.5[36.06-55.22]11.0 ![5.69-20.33]10.9[7.99-14.80]30.7[24.62-37.60]5.0[2.97-8.27]33.6[26.09-42.06]19.6[14.83-25.48]11.1[7.00-17.03]29.3[22.65-37.03]5.2[3.29-8.27]45.5[38.62-52.56]8.0[6.04-10.46]12.0[8.77-16.09]Native Hawaiian / other Pacific Islander24.8[17.61-33.74]8.1[5.28-12.30]53.8[43.78-63.60]5.9 ![2.95-11.45]7.3[4.28-12.27]23.9[18.23-30.74]6.0[3.92-9.23]51.6[42.46-60.72]8.0[5.19-12.06]10.4[6.10-17.20]17.8[13.79-22.74]10.3[6.16-16.57]48.6[41.09-56.12]10.6 ![5.60-19.01]12.8[7.84-20.19]26.5[19.80-34.56]10.9[7.06-16.53]38.7[31.33-46.69]14.1[9.85-19.72]9.7[6.62-14.11]27.8[20.88-35.89]8.9[5.40-14.22]39.0[30.14-48.59]9.9[7.35-13.20]14.5[10.10-20.36]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: RACE.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: SECTOR4 and RACE. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: SECTOR4 (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), RACE2 (NPSAS:2000) and RACE (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebknm0dbfebknm0d3Field of study: Undergraduate by Gender for years 1996, 2000, 2004, 2008, 2012 and 2016 Field of study: UndergraduateHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredTotalEstimatesTotal199611.97.54.20.80.52.86.06.215.89.82.111.021.4100%200015.78.14.61.00.77.75.07.216.59.04.58.911.2100%200410.37.03.90.60.54.84.26.715.412.82.59.721.7100%200816.06.65.50.90.53.75.36.117.415.62.611.28.7100%201217.17.06.50.90.54.24.95.315.619.23.213.42.1100%201616.37.07.51.10.64.66.14.515.918.43.212.32.4100%GenderMale199610.96.44.51.10.74.011.54.114.84.24.410.523.0100%200014.96.74.71.40.811.19.63.816.33.68.37.211.6100%200410.36.14.30.80.68.28.33.216.45.15.08.822.9100%200816.26.15.71.20.76.510.33.119.06.05.310.39.5100%201217.56.46.81.30.77.79.92.717.78.26.711.92.5100%201616.55.67.41.60.88.411.52.218.37.46.411.32.4100%Female199612.68.33.90.60.41.91.87.916.614.00.311.320.2100%200016.39.24.50.80.65.01.49.816.613.11.610.210.8100%200410.37.63.50.50.42.21.19.314.718.60.610.420.9100%200815.97.05.40.70.41.61.58.316.222.70.611.88.0100%201216.87.56.20.60.31.61.17.214.127.50.614.61.8100%201616.28.17.60.80.51.62.06.214.027.00.713.02.3100%Field of study: Undergraduate by Gender for years 1996, 2000, 2004, 2008, 2012 and 2016 Field of study: UndergraduateHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredTotalEstimatesTotal199611.97.54.20.80.52.86.06.215.89.82.111.021.4100%200015.78.14.61.00.77.75.07.216.59.04.58.911.2100%200410.37.03.90.60.54.84.26.715.412.82.59.721.7100%200816.06.65.50.90.53.75.36.117.415.62.611.28.7100%201217.17.06.50.90.54.24.95.315.619.23.213.42.1100%201616.37.07.51.10.64.66.14.515.918.43.212.32.4100%GenderMale199610.96.44.51.10.74.011.54.114.84.24.410.523.0100%200014.96.74.71.40.811.19.63.816.33.68.37.211.6100%200410.36.14.30.80.68.28.33.216.45.15.08.822.9100%200816.26.15.71.20.76.510.33.119.06.05.310.39.5100%201217.56.46.81.30.77.79.92.717.78.26.711.92.5100%201616.55.67.41.60.88.411.52.218.37.46.411.32.4100%Female199612.68.33.90.60.41.91.87.916.614.00.311.320.2100%200016.39.24.50.80.65.01.49.816.613.11.610.210.8100%200410.37.63.50.50.42.21.19.314.718.60.610.420.9100%200815.97.05.40.70.41.61.58.316.222.70.611.88.0100%201216.87.56.20.60.31.61.17.214.127.50.614.61.8100%201616.28.17.60.80.51.62.06.214.027.00.713.02.3100%Standard Error (BRR)Total19960.650.380.180.060.090.170.470.270.490.460.260.501.13 20000.430.180.170.060.060.340.180.190.310.250.220.330.46 20040.240.150.110.040.030.150.170.200.260.310.150.250.49 20080.280.120.110.040.030.110.150.140.280.290.190.230.28 20120.310.170.140.060.030.130.130.140.200.340.150.250.10 20160.270.130.150.070.040.110.150.110.230.270.160.250.10 GenderMale19960.520.420.240.110.140.290.900.280.680.390.570.511.41 20000.500.250.200.120.100.580.350.170.410.190.460.340.55 20040.340.210.150.060.050.290.320.180.350.200.320.290.62 20080.360.190.150.070.050.210.290.130.360.210.410.270.35 20120.410.230.220.100.070.260.280.120.350.290.340.310.14 20160.400.180.210.100.080.230.300.140.370.240.340.270.13 Female19960.890.460.230.060.080.170.220.380.590.610.080.611.03 20000.510.210.190.070.070.230.090.270.400.360.120.410.48 20040.240.180.120.040.040.110.090.280.300.430.060.300.49 20080.320.160.140.040.040.090.070.210.350.380.050.290.29 20120.350.190.180.050.040.090.070.220.280.500.050.340.11 20160.320.210.210.090.050.090.090.160.270.430.060.380.13 Relative Standard Error (%)Total19965.475.134.407.4116.026.047.734.393.094.7412.574.585.30 20002.722.173.615.938.904.393.692.671.882.804.943.694.08 20042.362.172.805.767.003.234.063.061.702.425.912.622.28 20081.751.841.924.495.472.842.802.361.631.847.302.023.19 20121.832.352.165.986.843.152.702.731.281.774.791.844.96 20161.641.852.066.156.562.512.382.521.471.464.962.014.01 GenderMale19964.736.545.409.9420.857.247.816.824.639.2912.864.866.15 20003.343.664.358.8713.065.243.604.542.515.435.524.714.79 20043.343.373.537.159.143.523.815.772.164.046.323.372.71 20082.213.142.636.337.283.142.844.311.923.497.732.593.72 20122.353.543.177.879.573.382.804.611.963.515.032.585.83 20162.403.222.846.669.292.692.646.061.993.285.302.425.45 Female19967.085.565.829.8219.158.8812.154.833.574.3727.895.415.09 20003.132.294.268.5311.614.666.162.752.422.747.254.074.41 20042.342.363.438.9310.015.128.513.002.062.3110.112.892.36 20082.022.252.526.619.505.924.972.512.181.688.822.493.66 20122.092.582.938.3311.635.506.163.081.971.808.442.326.11 20161.972.602.7110.8110.175.844.642.651.951.588.552.945.66 Weighted Sample Sizes (n/1,000s)Total199616,301.5 200015,742.8 200419,053.8 200819,477.2 201222,287.8 201618,939.6 GenderMale19967,051.6 20006,867.7 20048,082.4 20088,361.3 20129,617.4 20168,238.4 Female19969,249.9 20008,875.2 200410,971.4 200811,115.8 201212,670.4 201610,701.1 Field of study: Undergraduate by Gender for years 1996, 2000, 2004, 2008, 2012 and 2016 Field of study: UndergraduateHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal199611.9[10.65-13.27]7.5[6.73-8.27]4.2[3.84-4.58]0.8[0.71-0.96]0.5[0.39-0.74]2.8[2.50-3.19]6.0[5.15-7.03]6.2[5.70-6.80]15.8[14.85-16.82]9.8[8.88-10.74]2.1[1.61-2.66]11.0[10.01-12.04]21.4[19.19-23.74]100%200015.7[14.86-16.58]8.1[7.78-8.49]4.6[4.27-4.94]1.0[0.92-1.16]0.7[0.58-0.82]7.7[7.05-8.41]5.0[4.64-5.38]7.2[6.80-7.56]16.5[15.87-17.11]9.0[8.46-9.47]4.5[4.12-5.02]8.9[8.22-9.53]11.2[10.28-12.11]100%200410.3[9.86-10.82]7.0[6.67-7.26]3.9[3.65-4.08]0.6[0.55-0.70]0.5[0.42-0.55]4.8[4.49-5.10]4.2[3.84-4.50]6.7[6.29-7.09]15.4[14.93-15.96]12.8[12.24-13.46]2.5[2.19-2.76]9.7[9.19-10.19]21.7[20.76-22.71]100%200816.0[15.47-16.57]6.6[6.41-6.89]5.5[5.32-5.73]0.9[0.81-0.96]0.5[0.47-0.58]3.7[3.50-3.92]5.3[4.97-5.55]6.1[5.79-6.35]17.4[16.81-17.92]15.6[15.00-16.13]2.6[2.28-3.04]11.2[10.75-11.64]8.7[8.14-9.23]100%201217.1[16.52-17.75]7.0[6.72-7.37]6.5[6.23-6.78]0.9[0.83-1.05]0.5[0.43-0.57]4.2[3.97-4.50]4.9[4.62-5.14]5.3[4.99-5.56]15.6[15.26-16.05]19.2[18.51-19.84]3.2[2.93-3.54]13.4[12.94-13.92]2.1[1.89-2.30]100%201616.3[15.83-16.88]7.0[6.79-7.30]7.5[7.24-7.85]1.1[1.01-1.28]0.6[0.55-0.71]4.6[4.34-4.80]6.1[5.84-6.41]4.5[4.28-4.73]15.9[15.42-16.33]18.4[17.92-18.98]3.2[2.87-3.49]12.3[11.79-12.77]2.4[2.19-2.57]100%GenderMale199610.9[9.94-12.03]6.4[5.58-7.26]4.5[4.05-5.04]1.1[0.88-1.31]0.7[0.45-1.05]4.0[3.44-4.60]11.5[9.84-13.46]4.1[3.53-4.64]14.8[13.43-16.18]4.2[3.47-5.05]4.4[3.41-5.71]10.5[9.52-11.57]23.0[20.27-25.94]100%200014.9[13.90-15.90]6.7[6.25-7.24]4.7[4.32-5.15]1.4[1.16-1.65]0.8[0.59-1.00]11.1[10.01-12.35]9.6[8.93-10.32]3.8[3.47-4.17]16.3[15.52-17.17]3.6[3.21-3.99]8.3[7.47-9.32]7.2[6.53-7.89]11.6[10.50-12.73]100%200410.3[9.65-11.02]6.1[5.71-6.52]4.3[4.04-4.65]0.8[0.73-0.96]0.6[0.49-0.70]8.2[7.68-8.82]8.3[7.71-8.96]3.2[2.83-3.55]16.4[15.69-17.09]5.1[4.67-5.48]5.0[4.43-5.68]8.8[8.19-9.36]22.9[21.70-24.14]100%200816.2[15.52-16.93]6.1[5.78-6.54]5.7[5.40-5.99]1.2[1.02-1.31]0.7[0.63-0.83]6.5[6.15-6.96]10.3[9.75-10.90]3.1[2.83-3.35]19.0[18.24-19.68]6.0[5.62-6.45]5.3[4.58-6.21]10.3[9.82-10.87]9.5[8.83-10.23]100%201217.5[16.74-18.36]6.4[5.99-6.88]6.8[6.41-7.27]1.3[1.12-1.53]0.7[0.57-0.84]7.7[7.18-8.20]9.9[9.36-10.45]2.7[2.45-2.94]17.7[16.99-18.35]8.2[7.67-8.80]6.7[6.05-7.38]11.9[11.32-12.53]2.5[2.22-2.79]100%201616.5[15.73-17.30]5.6[5.29-6.00]7.4[7.00-7.84]1.6[1.37-1.78]0.8[0.70-1.01]8.4[8.01-8.91]11.5[10.90-12.10]2.2[1.99-2.53]18.3[17.63-19.07]7.4[6.89-7.84]6.4[5.76-7.10]11.3[10.82-11.90]2.4[2.19-2.72]100%Female199612.6[10.93-14.53]8.3[7.41-9.27]3.9[3.51-4.43]0.6[0.52-0.78]0.4[0.29-0.62]1.9[1.63-2.33]1.8[1.43-2.33]7.9[7.16-8.69]16.6[15.46-17.85]14.0[12.83-15.30]0.3[0.16-0.49]11.3[10.17-12.64]20.2[18.17-22.29]100%200016.3[15.34-17.40]9.2[8.79-9.63]4.5[4.13-4.90]0.8[0.64-0.91]0.6[0.50-0.79]5.0[4.59-5.54]1.4[1.27-1.62]9.8[9.25-10.33]16.6[15.80-17.42]13.1[12.40-13.85]1.6[1.39-1.86]10.2[9.35-11.01]10.8[9.92-11.84]100%200410.3[9.87-10.82]7.6[7.25-7.95]3.5[3.28-3.76]0.5[0.39-0.55]0.4[0.33-0.49]2.2[2.03-2.48]1.1[0.92-1.29]9.3[8.73-9.82]14.7[14.15-15.35]18.6[17.74-19.43]0.6[0.47-0.70]10.4[9.79-10.97]20.9[19.90-21.84]100%200815.9[15.24-16.50]7.0[6.71-7.33]5.4[5.14-5.67]0.7[0.59-0.77]0.4[0.31-0.45]1.6[1.39-1.76]1.5[1.32-1.60]8.3[7.91-8.73]16.2[15.48-16.86]22.7[21.99-23.49]0.6[0.50-0.71]11.8[11.26-12.42]8.0[7.48-8.64]100%201216.8[16.13-17.52]7.5[7.13-7.89]6.2[5.90-6.62]0.6[0.54-0.75]0.3[0.28-0.44]1.6[1.44-1.79]1.1[0.95-1.21]7.2[6.81-7.68]14.1[13.58-14.68]27.5[26.51-28.46]0.6[0.50-0.70]14.6[13.92-15.25]1.8[1.58-2.01]100%201616.2[15.61-16.87]8.1[7.72-8.55]7.6[7.24-8.05]0.8[0.66-1.00]0.5[0.37-0.56]1.6[1.40-1.77]2.0[1.81-2.18]6.2[5.91-6.56]14.0[13.44-14.52]27.0[26.16-27.83]0.7[0.57-0.81]13.0[12.25-13.76]2.3[2.08-2.60]100%199620002004200820122016 Field of study: UndergraduateField of study: UndergraduateField of study: UndergraduateField of study: UndergraduateField of study: UndergraduateField of study: Undergraduate HumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredEstimatesTotal11.97.54.20.80.52.86.06.215.89.82.111.021.415.78.14.61.00.77.75.07.216.59.04.58.911.210.37.03.90.60.54.84.26.715.412.82.59.721.716.06.65.50.90.53.75.36.117.415.62.611.28.717.17.06.50.90.54.24.95.315.619.23.213.42.116.37.07.51.10.64.66.14.515.918.43.212.32.4GenderMale10.96.44.51.10.74.011.54.114.84.24.410.523.014.96.74.71.40.811.19.63.816.33.68.37.211.610.36.14.30.80.68.28.33.216.45.15.08.822.916.26.15.71.20.76.510.33.119.06.05.310.39.517.56.46.81.30.77.79.92.717.78.26.711.92.516.55.67.41.60.88.411.52.218.37.46.411.32.4Female12.68.33.90.60.41.91.87.916.614.00.311.320.216.39.24.50.80.65.01.49.816.613.11.610.210.810.37.63.50.50.42.21.19.314.718.60.610.420.915.97.05.40.70.41.61.58.316.222.70.611.88.016.87.56.20.60.31.61.17.214.127.50.614.61.816.28.17.60.80.51.62.06.214.027.00.713.02.3199620002004200820122016 Field of study: UndergraduateField of study: UndergraduateField of study: UndergraduateField of study: UndergraduateField of study: UndergraduateField of study: Undergraduate HumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredEstimatesTotal11.97.54.20.80.52.86.06.215.89.82.111.021.415.78.14.61.00.77.75.07.216.59.04.58.911.210.37.03.90.60.54.84.26.715.412.82.59.721.716.06.65.50.90.53.75.36.117.415.62.611.28.717.17.06.50.90.54.24.95.315.619.23.213.42.116.37.07.51.10.64.66.14.515.918.43.212.32.4GenderMale10.96.44.51.10.74.011.54.114.84.24.410.523.014.96.74.71.40.811.19.63.816.33.68.37.211.610.36.14.30.80.68.28.33.216.45.15.08.822.916.26.15.71.20.76.510.33.119.06.05.310.39.517.56.46.81.30.77.79.92.717.78.26.711.92.516.55.67.41.60.88.411.52.218.37.46.411.32.4Female12.68.33.90.60.41.91.87.916.614.00.311.320.216.39.24.50.80.65.01.49.816.613.11.610.210.810.37.63.50.50.42.21.19.314.718.60.610.420.915.97.05.40.70.41.61.58.316.222.70.611.88.016.87.56.20.60.31.61.17.214.127.50.614.61.816.28.17.60.80.51.62.06.214.027.00.713.02.3Standard Error (BRR)Total0.650.380.180.060.090.170.470.270.490.460.260.501.130.430.180.170.060.060.340.180.190.310.250.220.330.460.240.150.110.040.030.150.170.200.260.310.150.250.490.280.120.110.040.030.110.150.140.280.290.190.230.280.310.170.140.060.030.130.130.140.200.340.150.250.100.270.130.150.070.040.110.150.110.230.270.160.250.10GenderMale0.520.420.240.110.140.290.900.280.680.390.570.511.410.500.250.200.120.100.580.350.170.410.190.460.340.550.340.210.150.060.050.290.320.180.350.200.320.290.620.360.190.150.070.050.210.290.130.360.210.410.270.350.410.230.220.100.070.260.280.120.350.290.340.310.140.400.180.210.100.080.230.300.140.370.240.340.270.13Female0.890.460.230.060.080.170.220.380.590.610.080.611.030.510.210.190.070.070.230.090.270.400.360.120.410.480.240.180.120.040.040.110.090.280.300.430.060.300.490.320.160.140.040.040.090.070.210.350.380.050.290.290.350.190.180.050.040.090.070.220.280.500.050.340.110.320.210.210.090.050.090.090.160.270.430.060.380.13Relative Standard Error (%)Total5.475.134.407.4116.026.047.734.393.094.7412.574.585.302.722.173.615.938.904.393.692.671.882.804.943.694.082.362.172.805.767.003.234.063.061.702.425.912.622.281.751.841.924.495.472.842.802.361.631.847.302.023.191.832.352.165.986.843.152.702.731.281.774.791.844.961.641.852.066.156.562.512.382.521.471.464.962.014.01GenderMale4.736.545.409.9420.857.247.816.824.639.2912.864.866.153.343.664.358.8713.065.243.604.542.515.435.524.714.793.343.373.537.159.143.523.815.772.164.046.323.372.712.213.142.636.337.283.142.844.311.923.497.732.593.722.353.543.177.879.573.382.804.611.963.515.032.585.832.403.222.846.669.292.692.646.061.993.285.302.425.45Female7.085.565.829.8219.158.8812.154.833.574.3727.895.415.093.132.294.268.5311.614.666.162.752.422.747.254.074.412.342.363.438.9310.015.128.513.002.062.3110.112.892.362.022.252.526.619.505.924.972.512.181.688.822.493.662.092.582.938.3311.635.506.163.081.971.808.442.326.111.972.602.7110.8110.175.844.642.651.951.588.552.945.66Weighted Sample Sizes (n/1,000s)Total16,301.5 15,742.8 19,053.8 19,477.2 22,287.8 18,939.6 GenderMale7,051.6 6,867.7 8,082.4 8,361.3 9,617.4 8,238.4 Female9,249.9 8,875.2 10,971.4 11,115.8 12,670.4 10,701.1 199620002004200820122016 Field of study: UndergraduateField of study: UndergraduateField of study: UndergraduateField of study: UndergraduateField of study: UndergraduateField of study: Undergraduate HumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclaredHumanitiesSocial / behavioral sciencesLife sciencesPhysical sciencesMathComputer / information scienceEngineeringEducationBusiness / managementHealthVocational / technicalOther technical/ professionalUndeclared Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal11.9[10.65-13.27]7.5[6.73-8.27]4.2[3.84-4.58]0.8[0.71-0.96]0.5[0.39-0.74]2.8[2.50-3.19]6.0[5.15-7.03]6.2[5.70-6.80]15.8[14.85-16.82]9.8[8.88-10.74]2.1[1.61-2.66]11.0[10.01-12.04]21.4[19.19-23.74]15.7[14.86-16.58]8.1[7.78-8.49]4.6[4.27-4.94]1.0[0.92-1.16]0.7[0.58-0.82]7.7[7.05-8.41]5.0[4.64-5.38]7.2[6.80-7.56]16.5[15.87-17.11]9.0[8.46-9.47]4.5[4.12-5.02]8.9[8.22-9.53]11.2[10.28-12.11]10.3[9.86-10.82]7.0[6.67-7.26]3.9[3.65-4.08]0.6[0.55-0.70]0.5[0.42-0.55]4.8[4.49-5.10]4.2[3.84-4.50]6.7[6.29-7.09]15.4[14.93-15.96]12.8[12.24-13.46]2.5[2.19-2.76]9.7[9.19-10.19]21.7[20.76-22.71]16.0[15.47-16.57]6.6[6.41-6.89]5.5[5.32-5.73]0.9[0.81-0.96]0.5[0.47-0.58]3.7[3.50-3.92]5.3[4.97-5.55]6.1[5.79-6.35]17.4[16.81-17.92]15.6[15.00-16.13]2.6[2.28-3.04]11.2[10.75-11.64]8.7[8.14-9.23]17.1[16.52-17.75]7.0[6.72-7.37]6.5[6.23-6.78]0.9[0.83-1.05]0.5[0.43-0.57]4.2[3.97-4.50]4.9[4.62-5.14]5.3[4.99-5.56]15.6[15.26-16.05]19.2[18.51-19.84]3.2[2.93-3.54]13.4[12.94-13.92]2.1[1.89-2.30]16.3[15.83-16.88]7.0[6.79-7.30]7.5[7.24-7.85]1.1[1.01-1.28]0.6[0.55-0.71]4.6[4.34-4.80]6.1[5.84-6.41]4.5[4.28-4.73]15.9[15.42-16.33]18.4[17.92-18.98]3.2[2.87-3.49]12.3[11.79-12.77]2.4[2.19-2.57]GenderMale10.9[9.94-12.03]6.4[5.58-7.26]4.5[4.05-5.04]1.1[0.88-1.31]0.7[0.45-1.05]4.0[3.44-4.60]11.5[9.84-13.46]4.1[3.53-4.64]14.8[13.43-16.18]4.2[3.47-5.05]4.4[3.41-5.71]10.5[9.52-11.57]23.0[20.27-25.94]14.9[13.90-15.90]6.7[6.25-7.24]4.7[4.32-5.15]1.4[1.16-1.65]0.8[0.59-1.00]11.1[10.01-12.35]9.6[8.93-10.32]3.8[3.47-4.17]16.3[15.52-17.17]3.6[3.21-3.99]8.3[7.47-9.32]7.2[6.53-7.89]11.6[10.50-12.73]10.3[9.65-11.02]6.1[5.71-6.52]4.3[4.04-4.65]0.8[0.73-0.96]0.6[0.49-0.70]8.2[7.68-8.82]8.3[7.71-8.96]3.2[2.83-3.55]16.4[15.69-17.09]5.1[4.67-5.48]5.0[4.43-5.68]8.8[8.19-9.36]22.9[21.70-24.14]16.2[15.52-16.93]6.1[5.78-6.54]5.7[5.40-5.99]1.2[1.02-1.31]0.7[0.63-0.83]6.5[6.15-6.96]10.3[9.75-10.90]3.1[2.83-3.35]19.0[18.24-19.68]6.0[5.62-6.45]5.3[4.58-6.21]10.3[9.82-10.87]9.5[8.83-10.23]17.5[16.74-18.36]6.4[5.99-6.88]6.8[6.41-7.27]1.3[1.12-1.53]0.7[0.57-0.84]7.7[7.18-8.20]9.9[9.36-10.45]2.7[2.45-2.94]17.7[16.99-18.35]8.2[7.67-8.80]6.7[6.05-7.38]11.9[11.32-12.53]2.5[2.22-2.79]16.5[15.73-17.30]5.6[5.29-6.00]7.4[7.00-7.84]1.6[1.37-1.78]0.8[0.70-1.01]8.4[8.01-8.91]11.5[10.90-12.10]2.2[1.99-2.53]18.3[17.63-19.07]7.4[6.89-7.84]6.4[5.76-7.10]11.3[10.82-11.90]2.4[2.19-2.72]Female12.6[10.93-14.53]8.3[7.41-9.27]3.9[3.51-4.43]0.6[0.52-0.78]0.4[0.29-0.62]1.9[1.63-2.33]1.8[1.43-2.33]7.9[7.16-8.69]16.6[15.46-17.85]14.0[12.83-15.30]0.3[0.16-0.49]11.3[10.17-12.64]20.2[18.17-22.29]16.3[15.34-17.40]9.2[8.79-9.63]4.5[4.13-4.90]0.8[0.64-0.91]0.6[0.50-0.79]5.0[4.59-5.54]1.4[1.27-1.62]9.8[9.25-10.33]16.6[15.80-17.42]13.1[12.40-13.85]1.6[1.39-1.86]10.2[9.35-11.01]10.8[9.92-11.84]10.3[9.87-10.82]7.6[7.25-7.95]3.5[3.28-3.76]0.5[0.39-0.55]0.4[0.33-0.49]2.2[2.03-2.48]1.1[0.92-1.29]9.3[8.73-9.82]14.7[14.15-15.35]18.6[17.74-19.43]0.6[0.47-0.70]10.4[9.79-10.97]20.9[19.90-21.84]15.9[15.24-16.50]7.0[6.71-7.33]5.4[5.14-5.67]0.7[0.59-0.77]0.4[0.31-0.45]1.6[1.39-1.76]1.5[1.32-1.60]8.3[7.91-8.73]16.2[15.48-16.86]22.7[21.99-23.49]0.6[0.50-0.71]11.8[11.26-12.42]8.0[7.48-8.64]16.8[16.13-17.52]7.5[7.13-7.89]6.2[5.90-6.62]0.6[0.54-0.75]0.3[0.28-0.44]1.6[1.44-1.79]1.1[0.95-1.21]7.2[6.81-7.68]14.1[13.58-14.68]27.5[26.51-28.46]0.6[0.50-0.70]14.6[13.92-15.25]1.8[1.58-2.01]16.2[15.61-16.87]8.1[7.72-8.55]7.6[7.24-8.05]0.8[0.66-1.00]0.5[0.37-0.56]1.6[1.40-1.77]2.0[1.81-2.18]6.2[5.91-6.56]14.0[13.44-14.52]27.0[26.16-27.83]0.7[0.57-0.81]13.0[12.25-13.76]2.3[2.08-2.60]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: MAJORS12.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: MAJORS12 and GENDER. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: MAJORS3 (NPSAS:1996, NPSAS:2000), GENDER (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and MAJORS12 (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96), 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebkn50bfebkn504Remedial courses: Ever taken by Age at start of postsecondary education, Gender and Race/ethnicity (with multiple) for years 2000, 2004, 2008, 2012 and 2016 Remedial courses: Ever takenYesNoTotalEstimatesTotal200031.368.7100%200434.365.7100%200835.964.1100%201230.769.3100%201639.160.9100%Age at start of postsecondary education18 or younger200025.574.5100%200431.168.9100%200832.567.5100%201226.773.3100%201632.967.1100%19-23200035.564.5100%200435.464.6100%200837.762.3100%201232.367.7100%201642.257.8100%24-29200041.358.7100%200438.561.5100%200840.159.9100%201236.663.4100%201650.749.3100%30-39200041.258.8100%200438.961.1100%200844.355.7100%201236.363.7100%201650.849.2100%40 or older200033.466.6100%200440.060.0100%200843.256.8100%201236.963.1100%201645.254.8100%GenderMale200029.970.1100%200432.068.0100%200832.567.5100%201227.572.5100%201636.263.8100%Female200032.367.7100%200436.064.0100%200838.561.5100%201233.166.9100%201641.458.6100%Race/ethnicity (with multiple)White200027.272.8100%200431.069.0100%200831.268.8100%201226.573.5100%201633.766.3100%Black or African American200042.757.3100%200442.058.0100%200846.253.8100%201237.762.3100%201647.552.5100%Hispanic or Latino200041.458.6100%200440.659.4100%200845.154.9100%201237.462.6100%201646.953.1100%Asian200034.965.1100%200436.463.6100%200837.262.8100%201235.464.6100%201639.160.9100%American Indian or Alaska Native200033.966.1100%200441.358.7100%200841.158.9100%201235.164.9100%201649.550.5100%Native Hawaiian / other Pacific Islander200034.965.1100%200437.162.9100%200838.062.0100%201233.866.2100%201641.458.6100%Other200033.266.8100%200434.965.1100%200834.165.9100%2012——100%2016——100%More than one race200034.265.8100%200432.867.2100%200835.364.7100%201228.271.8100%201637.362.7100%Remedial courses: Ever taken by Age at start of postsecondary education, Gender and Race/ethnicity (with multiple) for years 2000, 2004, 2008, 2012 and 2016 Remedial courses: Ever takenYesNoTotalEstimatesTotal200031.368.7100%200434.365.7100%200835.964.1100%201230.769.3100%201639.160.9100%Age at start of postsecondary education18 or younger200025.574.5100%200431.168.9100%200832.567.5100%201226.773.3100%201632.967.1100%19-23200035.564.5100%200435.464.6100%200837.762.3100%201232.367.7100%201642.257.8100%24-29200041.358.7100%200438.561.5100%200840.159.9100%201236.663.4100%201650.749.3100%30-39200041.258.8100%200438.961.1100%200844.355.7100%201236.363.7100%201650.849.2100%40 or older200033.466.6100%200440.060.0100%200843.256.8100%201236.963.1100%201645.254.8100%GenderMale200029.970.1100%200432.068.0100%200832.567.5100%201227.572.5100%201636.263.8100%Female200032.367.7100%200436.064.0100%200838.561.5100%201233.166.9100%201641.458.6100%Race/ethnicity (with multiple)White200027.272.8100%200431.069.0100%200831.268.8100%201226.573.5100%201633.766.3100%Black or African American200042.757.3100%200442.058.0100%200846.253.8100%201237.762.3100%201647.552.5100%Hispanic or Latino200041.458.6100%200440.659.4100%200845.154.9100%201237.462.6100%201646.953.1100%Asian200034.965.1100%200436.463.6100%200837.262.8100%201235.464.6100%201639.160.9100%American Indian or Alaska Native200033.966.1100%200441.358.7100%200841.158.9100%201235.164.9100%201649.550.5100%Native Hawaiian / other Pacific Islander200034.965.1100%200437.162.9100%200838.062.0100%201233.866.2100%201641.458.6100%Other200033.266.8100%200434.965.1100%200834.165.9100%2012——100%2016——100%More than one race200034.265.8100%200432.867.2100%200835.364.7100%201228.271.8100%201637.362.7100%Standard Error (BRR)Total20000.590.59 20040.290.29 20080.240.24 20120.270.27 20160.330.33 Age at start of postsecondary education18 or younger20000.650.65 20040.430.43 20080.300.30 20120.430.43 20160.410.41 19-2320000.820.82 20040.400.40 20080.380.38 20120.420.42 20160.440.44 24-2920001.841.84 20041.081.08 20081.061.06 20120.880.88 20161.161.16 30-3920002.032.03 20040.870.87 20081.241.24 20121.051.05 20161.311.31 40 or older20003.113.11 20041.151.15 20081.821.82 20121.491.49 20161.461.46 GenderMale20000.690.69 20040.380.38 20080.330.33 20120.380.38 20160.450.45 Female20000.690.69 20040.370.37 20080.330.33 20120.350.35 20160.390.39 Race/ethnicity (with multiple)White20000.570.57 20040.310.31 20080.280.28 20120.310.31 20160.420.42 Black or African American20001.411.41 20040.780.78 20080.760.76 20120.660.66 20160.770.77 Hispanic or Latino20001.441.44 20040.850.85 20080.670.67 20120.800.80 20160.730.73 Asian20001.531.53 20040.990.99 20081.041.04 20121.301.30 20160.980.98 American Indian or Alaska Native20003.763.76 20043.073.07 20083.043.04 20122.622.62 20163.773.77 Native Hawaiian / other Pacific Islander20005.945.94 20043.333.33 20083.043.04 20123.743.74 20163.703.70 Other20003.253.25 20042.102.10 20083.913.91 2012†† 2016†† More than one race20003.163.16 20041.351.35 20081.471.47 20121.401.40 20161.291.29 Relative Standard Error (%)Total20001.890.86 20040.850.44 20080.680.38 20120.870.39 20160.840.54 Age at start of postsecondary education18 or younger20002.570.88 20041.380.62 20080.940.45 20121.620.59 20161.260.62 19-2320002.301.27 20041.120.62 20081.010.61 20121.310.62 20161.050.77 24-2920004.453.13 20042.811.76 20082.651.77 20122.421.39 20162.282.35 30-3920004.933.45 20042.241.43 20082.792.22 20122.911.65 20162.582.66 40 or older20009.314.67 20042.871.91 20084.213.20 20124.042.37 20163.222.66 GenderMale20002.320.99 20041.200.57 20081.010.49 20121.370.52 20161.250.71 Female20002.131.02 20041.040.58 20080.860.54 20121.050.52 20160.940.67 Race/ethnicity (with multiple)White20002.080.78 20041.000.45 20080.910.41 20121.190.43 20161.260.64 Black or African American20003.312.46 20041.851.34 20081.651.42 20121.741.06 20161.621.47 Hispanic or Latino20003.472.45 20042.101.44 20081.491.22 20122.151.28 20161.551.37 Asian20004.392.36 20042.731.56 20082.791.65 20123.672.01 20162.521.62 American Indian or Alaska Native200011.095.69 20047.445.24 20087.425.16 20127.464.04 20167.627.47 Native Hawaiian / other Pacific Islander200017.049.13 20048.965.29 20087.994.91 201211.095.65 20168.946.31 Other20009.794.86 20046.023.22 200811.485.94 2012†† 2016†† More than one race20009.244.80 20044.102.01 20084.172.28 20124.971.95 20163.462.06 Weighted Sample Sizes (n/1,000s)Total200010,567.4 200419,053.8 200820,762.3 201223,055.4 201619,532.3 Age at start of postsecondary education18 or younger20005,081.2 20047,889.7 20089,513.8 20129,591.6 20168,719.8 19-2320003,988.1 20047,713.7 20088,335.0 20129,363.4 20167,903.0 24-292000669.7 20041,570.4 20081,417.9 20121,939.6 20161,236.8 30-392000525.4 20041,149.8 2008963.4 20121,362.0 2016920.7 40 or older2000276.2 2004730.2 2008532.2 2012798.8 2016752.0 GenderMale20004,439.8 20048,082.4 20088,922.0 20129,920.7 20168,498.8 Female20006,127.6 200410,971.4 200811,840.3 201213,134.7 201611,033.5 Race/ethnicity (with multiple)White20007,182.7 200411,982.4 200812,708.8 201213,345.5 201610,277.0 Black or African American20001,305.7 20042,674.5 20082,991.8 20123,708.8 20163,007.1 Hispanic or Latino20001,088.7 20042,456.4 20082,966.4 20123,696.0 20163,944.8 Asian2000466.4 20041,029.2 20081,219.8 20121,291.6 20161,399.3 American Indian or Alaska Native200089.1 2004175.2 2008173.0 2012208.8 2016159.9 Native Hawaiian / other Pacific Islander200084.0 200499.9 2008149.0 2012118.5 201682.9 Other2000158.2 2004247.1 200860.8 2012‡ 2016‡ More than one race2000192.6 2004389.0 2008492.6 2012686.1 2016661.3 Remedial courses: Ever taken by Age at start of postsecondary education, Gender and Race/ethnicity (with multiple) for years 2000, 2004, 2008, 2012 and 2016 Remedial courses: Ever takenYesNoTotalPct.95% CIPct.95% CI EstimatesTotal200031.3[30.10-32.48]68.7[67.52-69.90]100%200434.3[33.72-34.86]65.7[65.14-66.28]100%200835.9[35.45-36.41]64.1[63.59-64.55]100%201230.7[30.18-31.23]69.3[68.77-69.82]100%201639.1[38.50-39.79]60.9[60.21-61.50]100%Age at start of postsecondary education18 or younger200025.5[24.17-26.80]74.5[73.20-75.83]100%200431.1[30.28-31.97]68.9[68.03-69.72]100%200832.5[31.92-33.12]67.5[66.88-68.08]100%201226.7[25.83-27.54]73.3[72.46-74.17]100%201632.9[32.13-33.76]67.1[66.24-67.87]100%19-23200035.5[33.87-37.15]64.5[62.85-66.13]100%200435.4[34.66-36.22]64.6[63.78-65.34]100%200837.7[36.92-38.42]62.3[61.58-63.08]100%201232.3[31.45-33.11]67.7[66.89-68.55]100%201642.2[41.37-43.12]57.8[56.88-58.63]100%24-29200041.3[37.66-45.03]58.7[54.97-62.34]100%200438.5[36.39-40.65]61.5[59.35-63.61]100%200840.1[38.01-42.20]59.9[57.80-61.99]100%201236.6[34.83-38.32]63.4[61.68-65.17]100%201650.7[48.42-52.98]49.3[47.02-51.58]100%30-39200041.2[37.16-45.30]58.8[54.70-62.84]100%200438.9[37.21-40.65]61.1[59.35-62.79]100%200844.3[41.93-46.80]55.7[53.20-58.07]100%201236.3[34.22-38.37]63.7[61.63-65.78]100%201650.8[48.22-53.38]49.2[46.62-51.78]100%40 or older200033.4[27.49-39.94]66.6[60.06-72.51]100%200440.0[37.73-42.26]60.0[57.74-62.27]100%200843.2[39.67-46.83]56.8[53.17-60.33]100%201236.9[34.04-39.92]63.1[60.08-65.96]100%201645.2[42.38-48.12]54.8[51.88-57.62]100%GenderMale200029.9[28.50-31.29]70.1[68.71-71.50]100%200432.0[31.23-32.74]68.0[67.26-68.77]100%200832.5[31.90-33.19]67.5[66.81-68.10]100%201227.5[26.79-28.28]72.5[71.72-73.21]100%201636.2[35.36-37.14]63.8[62.86-64.64]100%Female200032.3[30.93-33.69]67.7[66.31-69.07]100%200436.0[35.25-36.72]64.0[63.28-64.75]100%200838.5[37.82-39.13]61.5[60.87-62.18]100%201233.1[32.42-33.79]66.9[66.21-67.58]100%201641.4[40.61-42.15]58.6[57.85-59.39]100%Race/ethnicity (with multiple)White200027.2[26.11-28.39]72.8[71.61-73.89]100%200431.0[30.39-31.61]69.0[68.39-69.61]100%200831.2[30.61-31.73]68.8[68.27-69.39]100%201226.5[25.86-27.11]73.5[72.89-74.14]100%201633.7[32.84-34.52]66.3[65.48-67.16]100%Black or African American200042.7[39.86-45.53]57.3[54.47-60.14]100%200442.0[40.47-43.54]58.0[56.46-59.53]100%200846.2[44.73-47.74]53.8[52.26-55.27]100%201237.7[36.41-39.01]62.3[60.99-63.59]100%201647.5[45.99-49.03]52.5[50.97-54.01]100%Hispanic or Latino200041.4[38.57-44.33]58.6[55.67-61.43]100%200440.6[38.93-42.30]59.4[57.70-61.07]100%200845.1[43.81-46.45]54.9[53.55-56.19]100%201237.4[35.84-39.00]62.6[61.00-64.16]100%201646.9[45.42-48.30]53.1[51.70-54.58]100%Asian200034.9[31.91-38.07]65.1[61.93-68.09]100%200436.4[34.44-38.36]63.6[61.64-65.56]100%200837.2[35.14-39.23]62.8[60.77-64.86]100%201235.4[32.87-37.98]64.6[62.02-67.13]100%201639.1[37.19-41.07]60.9[58.93-62.81]100%American Indian or Alaska Native200033.9[26.79-41.80]66.1[58.20-73.21]100%200441.3[35.41-47.48]58.7[52.52-64.59]100%200841.1[35.21-47.16]58.9[52.84-64.79]100%201235.1[30.15-40.46]64.9[59.54-69.85]100%201649.5[42.14-56.91]50.5[43.09-57.86]100%Native Hawaiian / other Pacific Islander200034.9[24.05-47.55]65.1[52.45-75.95]100%200437.1[30.83-43.88]62.9[56.12-69.17]100%200838.0[32.26-44.19]62.0[55.81-67.74]100%201233.8[26.82-41.49]66.2[58.51-73.18]100%201641.4[34.33-48.82]58.6[51.18-65.67]100%Other200033.2[27.00-39.98]66.8[60.02-73.00]100%200434.9[30.84-39.10]65.1[60.90-69.16]100%200834.1[26.84-42.17]65.9[57.83-73.16]100%2012—†—†100%2016—†—†100%More than one race200034.2[28.13-40.76]65.8[59.24-71.87]100%200432.8[30.24-35.55]67.2[64.45-69.76]100%200835.3[32.45-38.25]64.7[61.75-67.55]100%201228.2[25.51-31.04]71.8[68.96-74.49]100%201637.3[34.79-39.88]62.7[60.12-65.21]100%20002004200820122016 Remedial courses: Ever takenRemedial courses: Ever takenRemedial courses: Ever takenRemedial courses: Ever takenRemedial courses: Ever taken YesNoYesNoYesNoYesNoYesNoEstimatesTotal31.368.734.365.735.964.130.769.339.160.9Age at start of postsecondary education18 or younger25.574.531.168.932.567.526.773.332.967.119-2335.564.535.464.637.762.332.367.742.257.824-2941.358.738.561.540.159.936.663.450.749.330-3941.258.838.961.144.355.736.363.750.849.240 or older33.466.640.060.043.256.836.963.145.254.8GenderMale29.970.132.068.032.567.527.572.536.263.8Female32.367.736.064.038.561.533.166.941.458.6Race/ethnicity (with multiple)White27.272.831.069.031.268.826.573.533.766.3Black or African American42.757.342.058.046.253.837.762.347.552.5Hispanic or Latino41.458.640.659.445.154.937.462.646.953.1Asian34.965.136.463.637.262.835.464.639.160.9American Indian or Alaska Native33.966.141.358.741.158.935.164.949.550.5Native Hawaiian / other Pacific Islander34.965.137.162.938.062.033.866.241.458.6Other33.266.834.965.134.165.9————More than one race34.265.832.867.235.364.728.271.837.362.720002004200820122016 Remedial courses: Ever takenRemedial courses: Ever takenRemedial courses: Ever takenRemedial courses: Ever takenRemedial courses: Ever taken YesNoYesNoYesNoYesNoYesNoEstimatesTotal31.368.734.365.735.964.130.769.339.160.9Age at start of postsecondary education18 or younger25.574.531.168.932.567.526.773.332.967.119-2335.564.535.464.637.762.332.367.742.257.824-2941.358.738.561.540.159.936.663.450.749.330-3941.258.838.961.144.355.736.363.750.849.240 or older33.466.640.060.043.256.836.963.145.254.8GenderMale29.970.132.068.032.567.527.572.536.263.8Female32.367.736.064.038.561.533.166.941.458.6Race/ethnicity (with multiple)White27.272.831.069.031.268.826.573.533.766.3Black or African American42.757.342.058.046.253.837.762.347.552.5Hispanic or Latino41.458.640.659.445.154.937.462.646.953.1Asian34.965.136.463.637.262.835.464.639.160.9American Indian or Alaska Native33.966.141.358.741.158.935.164.949.550.5Native Hawaiian / other Pacific Islander34.965.137.162.938.062.033.866.241.458.6Other33.266.834.965.134.165.9————More than one race34.265.832.867.235.364.728.271.837.362.7Standard Error (BRR)Total0.590.590.290.290.240.240.270.270.330.33Age at start of postsecondary education18 or younger0.650.650.430.430.300.300.430.430.410.4119-230.820.820.400.400.380.380.420.420.440.4424-291.841.841.081.081.061.060.880.881.161.1630-392.032.030.870.871.241.241.051.051.311.3140 or older3.113.111.151.151.821.821.491.491.461.46GenderMale0.690.690.380.380.330.330.380.380.450.45Female0.690.690.370.370.330.330.350.350.390.39Race/ethnicity (with multiple)White0.570.570.310.310.280.280.310.310.420.42Black or African American1.411.410.780.780.760.760.660.660.770.77Hispanic or Latino1.441.440.850.850.670.670.800.800.730.73Asian1.531.530.990.991.041.041.301.300.980.98American Indian or Alaska Native3.763.763.073.073.043.042.622.623.773.77Native Hawaiian / other Pacific Islander5.945.943.333.333.043.043.743.743.703.70Other3.253.252.102.103.913.91††††More than one race3.163.161.351.351.471.471.401.401.291.29Relative Standard Error (%)Total1.890.860.850.440.680.380.870.390.840.54Age at start of postsecondary education18 or younger2.570.881.380.620.940.451.620.591.260.6219-232.301.271.120.621.010.611.310.621.050.7724-294.453.132.811.762.651.772.421.392.282.3530-394.933.452.241.432.792.222.911.652.582.6640 or older9.314.672.871.914.213.204.042.373.222.66GenderMale2.320.991.200.571.010.491.370.521.250.71Female2.131.021.040.580.860.541.050.520.940.67Race/ethnicity (with multiple)White2.080.781.000.450.910.411.190.431.260.64Black or African American3.312.461.851.341.651.421.741.061.621.47Hispanic or Latino3.472.452.101.441.491.222.151.281.551.37Asian4.392.362.731.562.791.653.672.012.521.62American Indian or Alaska Native11.095.697.445.247.425.167.464.047.627.47Native Hawaiian / other Pacific Islander17.049.138.965.297.994.9111.095.658.946.31Other9.794.866.023.2211.485.94††††More than one race9.244.804.102.014.172.284.971.953.462.06Weighted Sample Sizes (n/1,000s)Total10,567.4 19,053.8 20,762.3 23,055.4 19,532.3 Age at start of postsecondary education18 or younger5,081.2 7,889.7 9,513.8 9,591.6 8,719.8 19-233,988.1 7,713.7 8,335.0 9,363.4 7,903.0 24-29669.7 1,570.4 1,417.9 1,939.6 1,236.8 30-39525.4 1,149.8 963.4 1,362.0 920.7 40 or older276.2 730.2 532.2 798.8 752.0 GenderMale4,439.8 8,082.4 8,922.0 9,920.7 8,498.8 Female6,127.6 10,971.4 11,840.3 13,134.7 11,033.5 Race/ethnicity (with multiple)White7,182.7 11,982.4 12,708.8 13,345.5 10,277.0 Black or African American1,305.7 2,674.5 2,991.8 3,708.8 3,007.1 Hispanic or Latino1,088.7 2,456.4 2,966.4 3,696.0 3,944.8 Asian466.4 1,029.2 1,219.8 1,291.6 1,399.3 American Indian or Alaska Native89.1 175.2 173.0 208.8 159.9 Native Hawaiian / other Pacific Islander84.0 99.9 149.0 118.5 82.9 Other158.2 247.1 60.8 ‡ ‡ More than one race192.6 389.0 492.6 686.1 661.3 20002004200820122016 Remedial courses: Ever takenRemedial courses: Ever takenRemedial courses: Ever takenRemedial courses: Ever takenRemedial courses: Ever taken YesNoYesNoYesNoYesNoYesNo Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal31.3[30.10-32.48]68.7[67.52-69.90]34.3[33.72-34.86]65.7[65.14-66.28]35.9[35.45-36.41]64.1[63.59-64.55]30.7[30.18-31.23]69.3[68.77-69.82]39.1[38.50-39.79]60.9[60.21-61.50]Age at start of postsecondary education18 or younger25.5[24.17-26.80]74.5[73.20-75.83]31.1[30.28-31.97]68.9[68.03-69.72]32.5[31.92-33.12]67.5[66.88-68.08]26.7[25.83-27.54]73.3[72.46-74.17]32.9[32.13-33.76]67.1[66.24-67.87]19-2335.5[33.87-37.15]64.5[62.85-66.13]35.4[34.66-36.22]64.6[63.78-65.34]37.7[36.92-38.42]62.3[61.58-63.08]32.3[31.45-33.11]67.7[66.89-68.55]42.2[41.37-43.12]57.8[56.88-58.63]24-2941.3[37.66-45.03]58.7[54.97-62.34]38.5[36.39-40.65]61.5[59.35-63.61]40.1[38.01-42.20]59.9[57.80-61.99]36.6[34.83-38.32]63.4[61.68-65.17]50.7[48.42-52.98]49.3[47.02-51.58]30-3941.2[37.16-45.30]58.8[54.70-62.84]38.9[37.21-40.65]61.1[59.35-62.79]44.3[41.93-46.80]55.7[53.20-58.07]36.3[34.22-38.37]63.7[61.63-65.78]50.8[48.22-53.38]49.2[46.62-51.78]40 or older33.4[27.49-39.94]66.6[60.06-72.51]40.0[37.73-42.26]60.0[57.74-62.27]43.2[39.67-46.83]56.8[53.17-60.33]36.9[34.04-39.92]63.1[60.08-65.96]45.2[42.38-48.12]54.8[51.88-57.62]GenderMale29.9[28.50-31.29]70.1[68.71-71.50]32.0[31.23-32.74]68.0[67.26-68.77]32.5[31.90-33.19]67.5[66.81-68.10]27.5[26.79-28.28]72.5[71.72-73.21]36.2[35.36-37.14]63.8[62.86-64.64]Female32.3[30.93-33.69]67.7[66.31-69.07]36.0[35.25-36.72]64.0[63.28-64.75]38.5[37.82-39.13]61.5[60.87-62.18]33.1[32.42-33.79]66.9[66.21-67.58]41.4[40.61-42.15]58.6[57.85-59.39]Race/ethnicity (with multiple)White27.2[26.11-28.39]72.8[71.61-73.89]31.0[30.39-31.61]69.0[68.39-69.61]31.2[30.61-31.73]68.8[68.27-69.39]26.5[25.86-27.11]73.5[72.89-74.14]33.7[32.84-34.52]66.3[65.48-67.16]Black or African American42.7[39.86-45.53]57.3[54.47-60.14]42.0[40.47-43.54]58.0[56.46-59.53]46.2[44.73-47.74]53.8[52.26-55.27]37.7[36.41-39.01]62.3[60.99-63.59]47.5[45.99-49.03]52.5[50.97-54.01]Hispanic or Latino41.4[38.57-44.33]58.6[55.67-61.43]40.6[38.93-42.30]59.4[57.70-61.07]45.1[43.81-46.45]54.9[53.55-56.19]37.4[35.84-39.00]62.6[61.00-64.16]46.9[45.42-48.30]53.1[51.70-54.58]Asian34.9[31.91-38.07]65.1[61.93-68.09]36.4[34.44-38.36]63.6[61.64-65.56]37.2[35.14-39.23]62.8[60.77-64.86]35.4[32.87-37.98]64.6[62.02-67.13]39.1[37.19-41.07]60.9[58.93-62.81]American Indian or Alaska Native33.9[26.79-41.80]66.1[58.20-73.21]41.3[35.41-47.48]58.7[52.52-64.59]41.1[35.21-47.16]58.9[52.84-64.79]35.1[30.15-40.46]64.9[59.54-69.85]49.5[42.14-56.91]50.5[43.09-57.86]Native Hawaiian / other Pacific Islander34.9[24.05-47.55]65.1[52.45-75.95]37.1[30.83-43.88]62.9[56.12-69.17]38.0[32.26-44.19]62.0[55.81-67.74]33.8[26.82-41.49]66.2[58.51-73.18]41.4[34.33-48.82]58.6[51.18-65.67]Other33.2[27.00-39.98]66.8[60.02-73.00]34.9[30.84-39.10]65.1[60.90-69.16]34.1[26.84-42.17]65.9[57.83-73.16]—†—†—†—†More than one race34.2[28.13-40.76]65.8[59.24-71.87]32.8[30.24-35.55]67.2[64.45-69.76]35.3[32.45-38.25]64.7[61.75-67.55]28.2[25.51-31.04]71.8[68.96-74.49]37.3[34.79-39.88]62.7[60.12-65.21]— Not available.— Not available.† Not applicable.‡ Reporting standards not met.STDERR-SOURCE-END— Not available.† Not applicable.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: REMEVER, AGEPSE and RACE.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: REMEVER, AGEPSE, GENDER and RACE. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: NEREMEVR (NPSAS:2000), AGEPSE (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), GENDER (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), RACE2 (NPSAS:2000), REMEVER (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and RACE (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebknh55bfebknh555Work: Hours per week by Type of job student had (includes work study or assistantship), for [Job: Primary role as student or employee (excludes work study or assistant) (A student working to meet expenses)] for years 2004, 2008, 2012 and 2016 Work: Hours per week1-15 hours16-25 hours26-39 hours40 or more hoursTotalEstimatesTotal200423.932.323.420.5100%200823.330.223.523.0100%201223.230.323.523.1100%201622.531.722.023.7100%Type of job student had (includes work study or assistantship)Regular job only200424.832.922.819.5100%200824.731.122.921.4100%201223.930.923.321.9100%201622.531.722.023.7100%Work-study job only2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Both200412.324.730.132.9100%20088.821.029.740.5100%201210.618.826.743.8100%2016‡‡‡‡100%No job2004‡‡‡‡100%2008————100%2012————100%2016————100%Work: Hours per week by Type of job student had (includes work study or assistantship), for [Job: Primary role as student or employee (excludes work study or assistant) (A student working to meet expenses)] for years 2004, 2008, 2012 and 2016 Work: Hours per week1-15 hours16-25 hours26-39 hours40 or more hoursTotalEstimatesTotal200423.932.323.420.5100%200823.330.223.523.0100%201223.230.323.523.1100%201622.531.722.023.7100%Type of job student had (includes work study or assistantship)Regular job only200424.832.922.819.5100%200824.731.122.921.4100%201223.930.923.321.9100%201622.531.722.023.7100%Work-study job only2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Both200412.324.730.132.9100%20088.821.029.740.5100%201210.618.826.743.8100%2016‡‡‡‡100%No job2004‡‡‡‡100%2008————100%2012————100%2016————100%Standard Error (BRR)Total20040.320.340.290.32 20080.260.300.290.33 20120.340.380.360.38 20160.290.350.360.35 Type of job student had (includes work study or assistantship)Regular job only20040.350.340.310.32 20080.280.300.320.36 20120.350.390.370.38 20160.290.350.360.35 Work-study job only2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Both20040.731.151.031.29 20080.600.930.991.09 20120.971.251.601.88 2016‡‡‡‡ No job2004‡‡‡‡ 2008†††† 2012†††† 2016†††† Relative Standard Error (%)Total20041.331.051.261.57 20081.110.991.251.42 20121.451.261.551.63 20161.281.111.641.49 Type of job student had (includes work study or assistantship)Regular job only20041.401.031.371.65 20081.140.981.391.67 20121.451.261.571.71 20161.281.111.641.49 Work-study job only2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Both20045.954.673.433.92 20086.824.443.342.70 20129.096.626.004.29 2016‡‡‡‡ No job2004‡‡‡‡ 2008†††† 2012†††† 2016†††† Weighted Sample Sizes (n/1,000s)Total20049,096.3 200810,395.1 20129,457.6 20168,491.0 Type of job student had (includes work study or assistantship)Regular job only20048,413.4 20089,516.0 20128,968.0 20168,491.0 Work-study job only2004‡ 2008‡ 2012‡ 2016‡ Both2004682.9 2008879.2 2012489.6 2016‡ No job2004‡ 2008‡ 2012‡ 2016‡ Work: Hours per week by Type of job student had (includes work study or assistantship), for [Job: Primary role as student or employee (excludes work study or assistant) (A student working to meet expenses)] for years 2004, 2008, 2012 and 2016 Work: Hours per week1-15 hours16-25 hours26-39 hours40 or more hoursTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal200423.9[23.27-24.52]32.3[31.61-32.95]23.4[22.79-23.94]20.5[19.85-21.12]100%200823.3[22.80-23.82]30.2[29.63-30.81]23.5[22.92-24.08]23.0[22.35-23.64]100%201223.2[22.52-23.85]30.3[29.53-31.03]23.5[22.77-24.21]23.1[22.33-23.82]100%201622.5[21.96-23.10]31.7[31.03-32.42]22.0[21.31-22.74]23.7[23.05-24.45]100%Type of job student had (includes work study or assistantship)Regular job only200424.8[24.15-25.52]32.9[32.22-33.56]22.8[22.20-23.44]19.5[18.85-20.11]100%200824.7[24.10-25.21]31.1[30.47-31.66]22.9[22.30-23.56]21.4[20.67-22.07]100%201223.9[23.19-24.55]30.9[30.13-31.67]23.3[22.59-24.03]21.9[21.20-22.69]100%201622.5[21.96-23.10]31.7[31.03-32.42]22.0[21.31-22.74]23.7[23.05-24.45]100%Work-study job only2004—†—†—†—†100%2008—†—†—†—†100%2012—†—†—†—†100%2016—†—†—†—†100%Both200412.3[10.95-13.85]24.7[22.51-27.06]30.1[28.10-32.17]32.9[30.38-35.46]100%20088.8[7.67-10.04]21.0[19.25-22.93]29.7[27.75-31.65]40.5[38.39-42.70]100%201210.6[8.87-12.68]18.8[16.50-21.42]26.7[23.69-30.01]43.8[40.14-47.53]100%2016—†—†—†—†100%No job2004—†—†—†—†100%2008—†—†—†—†100%2012—†—†—†—†100%2016—†—†—†—†100%2004200820122016 Work: Hours per weekWork: Hours per weekWork: Hours per weekWork: Hours per week 1-15 hours16-25 hours26-39 hours40 or more hours1-15 hours16-25 hours26-39 hours40 or more hours1-15 hours16-25 hours26-39 hours40 or more hours1-15 hours16-25 hours26-39 hours40 or more hoursEstimatesTotal23.932.323.420.523.330.223.523.023.230.323.523.122.531.722.023.7Type of job student had (includes work study or assistantship)Regular job only24.832.922.819.524.731.122.921.423.930.923.321.922.531.722.023.7Work-study job only‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Both12.324.730.132.98.821.029.740.510.618.826.743.8‡‡‡‡No job‡‡‡‡————————————2004200820122016 Work: Hours per weekWork: Hours per weekWork: Hours per weekWork: Hours per week 1-15 hours16-25 hours26-39 hours40 or more hours1-15 hours16-25 hours26-39 hours40 or more hours1-15 hours16-25 hours26-39 hours40 or more hours1-15 hours16-25 hours26-39 hours40 or more hoursEstimatesTotal23.932.323.420.523.330.223.523.023.230.323.523.122.531.722.023.7Type of job student had (includes work study or assistantship)Regular job only24.832.922.819.524.731.122.921.423.930.923.321.922.531.722.023.7Work-study job only‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Both12.324.730.132.98.821.029.740.510.618.826.743.8‡‡‡‡No job‡‡‡‡————————————Standard Error (BRR)Total0.320.340.290.320.260.300.290.330.340.380.360.380.290.350.360.35Type of job student had (includes work study or assistantship)Regular job only0.350.340.310.320.280.300.320.360.350.390.370.380.290.350.360.35Work-study job only‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Both0.731.151.031.290.600.930.991.090.971.251.601.88‡‡‡‡No job‡‡‡‡††††††††††††Relative Standard Error (%)Total1.331.051.261.571.110.991.251.421.451.261.551.631.281.111.641.49Type of job student had (includes work study or assistantship)Regular job only1.401.031.371.651.140.981.391.671.451.261.571.711.281.111.641.49Work-study job only‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Both5.954.673.433.926.824.443.342.709.096.626.004.29‡‡‡‡No job‡‡‡‡††††††††††††Weighted Sample Sizes (n/1,000s)Total9,096.3 10,395.1 9,457.6 8,491.0 Type of job student had (includes work study or assistantship)Regular job only8,413.4 9,516.0 8,968.0 8,491.0 Work-study job only‡ ‡ ‡ ‡ Both682.9 879.2 489.6 ‡ No job‡ ‡ ‡ ‡ 2004200820122016 Work: Hours per weekWork: Hours per weekWork: Hours per weekWork: Hours per week 1-15 hours16-25 hours26-39 hours40 or more hours1-15 hours16-25 hours26-39 hours40 or more hours1-15 hours16-25 hours26-39 hours40 or more hours1-15 hours16-25 hours26-39 hours40 or more hours Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal23.9[23.27-24.52]32.3[31.61-32.95]23.4[22.79-23.94]20.5[19.85-21.12]23.3[22.80-23.82]30.2[29.63-30.81]23.5[22.92-24.08]23.0[22.35-23.64]23.2[22.52-23.85]30.3[29.53-31.03]23.5[22.77-24.21]23.1[22.33-23.82]22.5[21.96-23.10]31.7[31.03-32.42]22.0[21.31-22.74]23.7[23.05-24.45]Type of job student had (includes work study or assistantship)Regular job only24.8[24.15-25.52]32.9[32.22-33.56]22.8[22.20-23.44]19.5[18.85-20.11]24.7[24.10-25.21]31.1[30.47-31.66]22.9[22.30-23.56]21.4[20.67-22.07]23.9[23.19-24.55]30.9[30.13-31.67]23.3[22.59-24.03]21.9[21.20-22.69]22.5[21.96-23.10]31.7[31.03-32.42]22.0[21.31-22.74]23.7[23.05-24.45]Work-study job only—†—†—†—†—†—†—†—†—†—†—†—†—†—†—†—†Both12.3[10.95-13.85]24.7[22.51-27.06]30.1[28.10-32.17]32.9[30.38-35.46]8.8[7.67-10.04]21.0[19.25-22.93]29.7[27.75-31.65]40.5[38.39-42.70]10.6[8.87-12.68]18.8[16.50-21.42]26.7[23.69-30.01]43.8[40.14-47.53]—†—†—†—†No job—†—†—†—†—†—†—†—†—†—†—†—†—†—†—†—†— Not available.‡ Reporting standards not met.— Not available.† Not applicable.‡ Reporting standards not met.STDERR-SOURCE-END— Not available.† Not applicable.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: JOBHOUR2, JOBTYPE2 and JOBROLE.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: JOBHOUR2, JOBTYPE2 and JOBROLE. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: JOBHOUR2 (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), JOBTYPE (NPSAS:2004), JOBROLE (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and JOBTYPE2 (NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebknk25bfebknk251Total loans with (Percent>0.5) and Average>0 Total loans by Graduate programs for years 2000, 2004, 2008, 2012 and 2016 Total loansTotal loans(%>0.5)(Avg>0)EstimatesTotal200030.413,528.5200440.015,741.4200842.418,435.8201245.121,425.6201644.223,366.5Graduate programsBusiness administration (MBA)200024.613,853.9200439.114,987.9200841.716,673.5201237.519,149.5201638.720,919.0Education (any master's)200024.49,399.7200434.811,917.3200844.113,053.9201249.014,252.3201645.015,251.7Other master of arts (MA)200033.211,664.7200441.312,260.4200848.915,724.1201245.717,954.4201643.717,671.3Other master of science (MS)200024.411,769.9200431.813,212.0200835.716,129.2201239.517,739.2201640.618,285.2Other master's degree200038.812,527.0200449.312,998.1200846.317,895.0201256.019,370.8201649.720,440.8PhD except in education200021.811,479.8200419.912,259.9200825.816,750.3201217.318,337.0201623.318,446.1Education (any doctorate)200021.112,010.5200427.113,579.2200842.215,373.4201249.517,788.1201644.716,804.5Other doctoral degree200025.014,890.2200449.520,476.0200853.526,612.8201248.426,115.8201655.828,357.3Medicine (MD)200068.918,393.9200477.328,441.2200876.634,440.7201280.741,215.9201674.451,296.6Other health science degree200076.117,310.9200481.724,994.5200882.129,734.7201287.336,425.7201677.549,062.4Law (LLB or JD)200081.820,348.5200481.022,599.1200882.131,942.2201281.640,424.4201672.141,588.2Theology (MDiv, MHL, BD)200023.0‡200430.010,588.5200850.512,404.02012‡‡201669.357,844.5Post-baccalaureate certificate2000——200430.18,273.7200829.611,748.4201228.914,707.9201632.718,612.0Other professional practice doctoral degree2000——2004——2008——201267.530,003.9201660.131,208.1Not in a degree program200014.49,457.4200428.013,937.7200817.512,693.8201210.912,585.820168.8 !‡Total loans with (Percent>0.5) and Average>0 Total loans by Graduate programs for years 2000, 2004, 2008, 2012 and 2016 Total loansTotal loans(%>0.5)(Avg>0)EstimatesTotal200030.413,528.5200440.015,741.4200842.418,435.8201245.121,425.6201644.223,366.5Graduate programsBusiness administration (MBA)200024.613,853.9200439.114,987.9200841.716,673.5201237.519,149.5201638.720,919.0Education (any master's)200024.49,399.7200434.811,917.3200844.113,053.9201249.014,252.3201645.015,251.7Other master of arts (MA)200033.211,664.7200441.312,260.4200848.915,724.1201245.717,954.4201643.717,671.3Other master of science (MS)200024.411,769.9200431.813,212.0200835.716,129.2201239.517,739.2201640.618,285.2Other master's degree200038.812,527.0200449.312,998.1200846.317,895.0201256.019,370.8201649.720,440.8PhD except in education200021.811,479.8200419.912,259.9200825.816,750.3201217.318,337.0201623.318,446.1Education (any doctorate)200021.112,010.5200427.113,579.2200842.215,373.4201249.517,788.1201644.716,804.5Other doctoral degree200025.014,890.2200449.520,476.0200853.526,612.8201248.426,115.8201655.828,357.3Medicine (MD)200068.918,393.9200477.328,441.2200876.634,440.7201280.741,215.9201674.451,296.6Other health science degree200076.117,310.9200481.724,994.5200882.129,734.7201287.336,425.7201677.549,062.4Law (LLB or JD)200081.820,348.5200481.022,599.1200882.131,942.2201281.640,424.4201672.141,588.2Theology (MDiv, MHL, BD)200023.0‡200430.010,588.5200850.512,404.02012‡‡201669.357,844.5Post-baccalaureate certificate2000——200430.18,273.7200829.611,748.4201228.914,707.9201632.718,612.0Other professional practice doctoral degree2000——2004——2008——201267.530,003.9201660.131,208.1Not in a degree program200014.49,457.4200428.013,937.7200817.512,693.8201210.912,585.820168.8 !‡Standard Error (BRR)Total20000.35{|2000|{164.31|20041.16{|2004|{277.00|20080.34{|2008|{154.93|20120.24{|2012|{107.61|20160.27{|2016|{128.27|Graduate programsBusiness administration (MBA)20001.55{|2000|{926.53|20043.49{|2004|{924.34|20082.38{|2008|{776.01|20122.50{|2012|{1,011.83|20161.78{|2016|{1,315.82|Education (any master's)20001.28{|2000|{307.11|20041.81{|2004|{401.66|20081.98{|2008|{413.83|20121.82{|2012|{434.00|20161.91{|2016|{601.25|Other master of arts (MA)20002.13{|2000|{520.54|20043.58{|2004|{552.07|20082.45{|2008|{596.53|20123.09{|2012|{1,075.40|20162.64{|2016|{957.85|Other master of science (MS)20001.39{|2000|{658.07|20042.05{|2004|{721.06|20081.70{|2008|{546.40|20121.61{|2012|{578.95|20161.24{|2016|{576.30|Other master's degree20001.71{|2000|{396.36|20043.12{|2004|{502.25|20082.00{|2008|{524.15|20121.78{|2012|{565.32|20161.54{|2016|{666.25|PhD except in education20001.76{|2000|{948.97|20041.19{|2004|{647.97|20083.74{|2008|{1,140.16|20120.85{|2012|{707.63|20161.74{|2016|{981.68|Education (any doctorate)20002.05{|2000|{683.50|20042.46{|2004|{692.85|20087.64{|2008|{2,059.29|20122.24{|2012|{731.81|20163.49{|2016|{681.11|Other doctoral degree20004.52{|2000|{2,037.37|20044.17{|2004|{1,266.07|20083.27{|2008|{1,033.36|20122.90{|2012|{1,105.68|20163.64{|2016|{2,071.62|Medicine (MD)20003.06{|2000|{753.59|20042.59{|2004|{1,103.80|20082.65{|2008|{1,187.84|20122.13{|2012|{1,046.86|20163.06{|2016|{1,909.80|Other health science degree20002.71{|2000|{701.44|20042.59{|2004|{1,150.04|20082.63{|2008|{1,172.31|20122.21{|2012|{1,103.84|20163.38{|2016|{2,968.41|Law (LLB or JD)20001.89{|2000|{449.30|20041.90{|2004|{901.97|20081.75{|2008|{840.88|20121.67{|2012|{1,010.00|20163.14{|2016|{1,674.10|Theology (MDiv, MHL, BD)20005.52‡20045.06{|2004|{1,447.74|20087.84{|2008|{1,132.92|2012‡‡20166.64{|2016|{2,941.09|Post-baccalaureate certificate2000††20044.71{|2004|{686.41|20082.81{|2008|{773.62|20122.53{|2012|{808.85|20162.80{|2016|{1,582.49|Other professional practice doctoral degree2000††2004††2008††20123.64{|2012|{1,427.06|20165.62{|2016|{1,473.35|Not in a degree program20001.12{|2000|{457.75|20044.18{|2004|{788.75|20082.98{|2008|{1,290.84|20122.50{|2012|{1,730.38|20163.08‡Relative Standard Error (%)Total20001.141.2120042.901.7620080.800.8420120.540.5020160.620.55Graduate programsBusiness administration (MBA)20006.326.6920048.926.1720085.714.6520126.655.2820164.596.29Education (any master's)20005.273.2720045.203.3720084.493.1720123.723.0520164.253.94Other master of arts (MA)20006.424.4620048.674.5020085.023.7920126.765.9920166.045.42Other master of science (MS)20005.715.5920046.435.4620084.763.3920124.093.2620163.063.15Other master's degree20004.393.1620046.343.8620084.322.9320123.182.9220163.093.26PhD except in education20008.088.2720045.975.29200814.496.8120124.913.8620167.455.32Education (any doctorate)20009.705.6920049.065.10200818.1113.4020124.524.1120167.814.05Other doctoral degree200018.0913.6820048.446.1820086.123.8820125.984.2320166.527.31Medicine (MD)20004.444.1020043.353.8820083.463.4520122.632.5420164.123.72Other health science degree20003.564.0520043.184.6020083.213.9420122.533.0320164.366.05Law (LLB or JD)20002.312.2120042.343.9920082.132.6320122.052.5020164.354.03Theology (MDiv, MHL, BD)200023.93‡200416.8813.67200815.549.132012‡‡20169.575.08Post-baccalaureate certificate2000††200415.638.3020089.466.5820128.775.5020168.578.50Other professional practice doctoral degree2000††2004††2008††20125.404.7620169.354.72Not in a degree program20007.784.84200414.935.66200816.9810.17201222.8913.75201635.11‡Weighted Sample Sizes (n/1,000s)Total20002,616.9794.620042,824.31,130.720083,492.01,479.320123,682.21,659.620163,572.91,577.6Graduate programsBusiness administration (MBA)2000309.476.12004319.4124.92008419.7174.92012403.0151.22016369.2143.0Education (any master's)2000440.6107.32004513.9179.02008709.8313.12012595.0291.32016490.9220.9Other master of arts (MA)2000166.455.32004173.871.72008238.9116.82012284.2130.02016253.7110.9Other master of science (MS)2000305.174.32004367.1116.82008475.2169.72012666.3263.02016712.5289.3Other master's degree2000327.0127.02004306.6151.22008406.6188.12012543.4304.02016622.7309.5PhD except in education2000213.846.62004230.945.92008325.283.92012324.356.32016245.057.1Education (any doctorate)200060.912.9200460.716.4200888.537.3201295.147.0201684.037.6Other doctoral degree200070.217.5200495.347.12008138.073.9201275.636.62016129.272.0Medicine (MD)200073.850.9200476.559.1200869.052.8201291.573.9201677.357.5Other health science degree200081.762.2200480.665.8200860.249.42012107.593.8201691.771.1Law (LLB or JD)2000120.398.52004153.2124.12008149.2122.52012129.0105.3201690.865.5Theology (MDiv, MHL, BD)200020.0‡200439.511.9200816.48.32012‡‡201635.524.6Post-baccalaureate certificate2000‡‡2004134.340.52008160.047.42012212.661.42016217.571.1Other professional practice doctoral degree2000‡‡2004‡‡2008‡‡201251.034.4201666.239.8Not in a degree program2000427.561.42004272.476.32008235.241.32012102.911.2201686.7‡Total loans with (Percent>0.5) and Average>0 Total loans by Graduate programs for years 2000, 2004, 2008, 2012 and 2016 Total loansTotal loans(%>0.5)(Avg>0)Pct.95% CIAmt.95% CIEstimatesTotal200030.4[29.67-31.06]13,528.5[13,199.84-13,857.07]200440.0[37.77-42.34]15,741.4[15,195.16-16,287.66]200842.4[41.70-43.03]18,435.8[18,130.31-18,741.36]201245.1[44.60-45.55]21,425.6[21,213.35-21,637.77]201644.2[43.61-44.70]23,366.5[23,113.50-23,619.41]Graduate programsBusiness administration (MBA)200024.6[21.61-27.82]13,853.9[12,000.81-15,706.91]200439.1[32.46-46.14]14,987.9[13,165.10-16,810.69]200841.7[37.06-46.42]16,673.5[15,143.18-18,203.76]201237.5[32.75-42.56]19,149.5[17,154.23-21,144.87]201638.7[35.29-42.29]20,919.0[18,324.22-23,513.83]Education (any master's)200024.4[21.88-27.01]9,399.7[8,785.42-10,013.88]200434.8[31.34-38.48]11,917.3[11,125.26-12,709.42]200844.1[40.25-48.04]13,053.9[12,237.83-13,869.96]201249.0[45.38-52.56]14,252.3[13,396.42-15,108.12]201645.0[41.26-48.79]15,251.7[14,066.00-16,437.32]Other master of arts (MA)200033.2[29.08-37.60]11,664.7[10,623.59-12,705.77]200441.3[34.43-48.46]12,260.4[11,171.68-13,349.05]200848.9[44.07-53.71]15,724.1[14,547.76-16,900.47]201245.7[39.74-51.87]17,954.4[15,833.74-20,075.10]201643.7[38.61-48.99]17,671.3[15,782.41-19,560.19]Other master of science (MS)200024.4[21.69-27.25]11,769.9[10,453.76-13,086.06]200431.8[27.93-35.99]13,212.0[11,790.02-14,633.89]200835.7[32.43-39.12]16,129.2[15,051.67-17,206.69]201239.5[36.35-42.70]17,739.2[16,597.46-18,880.84]201640.6[38.18-43.08]18,285.2[17,148.78-19,421.70]Other master's degree200038.8[35.49-42.31]12,527.0[11,734.30-13,319.75]200449.3[43.19-55.45]12,998.1[12,007.71-13,988.57]200846.3[42.34-50.21]17,895.0[16,861.42-18,928.66]201256.0[52.43-59.43]19,370.8[18,255.98-20,485.61]201649.7[46.68-52.74]20,440.8[19,126.99-21,754.70]PhD except in education200021.8[18.48-25.53]11,479.8[9,581.87-13,377.77]200419.9[17.63-22.31]12,259.9[10,982.13-13,537.72]200825.8[19.13-33.82]16,750.3[14,501.87-18,998.67]201217.3[15.73-19.09]18,337.0[16,941.57-19,732.48]201623.3[20.06-26.91]18,446.1[16,510.18-20,381.94]Education (any doctorate)200021.1[17.33-25.53]12,010.5[10,643.53-13,377.54]200427.1[22.54-32.21]13,579.2[12,212.87-14,945.46]200842.2[28.22-57.48]15,373.4[11,312.49-19,434.34]201249.5[45.08-53.88]17,788.1[16,345.02-19,231.27]201644.7[37.98-51.67]16,804.5[15,461.35-18,147.66]Other doctoral degree200025.0[17.07-35.07]14,890.2[10,815.41-18,964.90]200449.5[41.31-57.63]20,476.0[17,979.36-22,972.73]200853.5[47.04-59.88]26,612.8[24,575.06-28,650.62]201248.4[42.73-54.10]26,115.8[23,935.44-28,296.24]201655.8[48.51-62.76]28,357.3[24,272.11-32,442.57]Medicine (MD)200068.9[62.48-74.68]18,393.9[16,886.69-19,901.04]200477.3[71.80-82.01]28,441.2[26,264.51-30,617.89]200876.6[70.97-81.42]34,440.7[32,098.32-36,783.15]201280.7[76.17-84.56]41,215.9[39,151.49-43,280.30]201674.4[67.93-79.99]51,296.6[47,530.50-55,062.77]Other health science degree200076.1[70.27-81.09]17,310.9[15,908.04-18,713.80]200481.7[76.03-86.28]24,994.5[22,726.62-27,262.39]200882.1[76.34-86.75]29,734.7[27,422.86-32,046.44]201287.3[82.23-91.02]36,425.7[34,248.96-38,602.50]201677.5[70.18-83.50]49,062.4[43,208.71-54,916.12]Law (LLB or JD)200081.8[77.75-85.33]20,348.5[19,449.89-21,247.11]200481.0[76.98-84.47]22,599.1[20,820.37-24,377.73]200882.1[78.39-85.29]31,942.2[30,284.02-33,600.46]201281.6[78.10-84.70]40,424.4[38,432.63-42,416.07]201672.1[65.54-77.88]41,588.2[38,286.86-44,889.52]Theology (MDiv, MHL, BD)200023.0[13.85-35.81]‡‡200430.0[21.03-40.80]10,588.5[7,733.61-13,443.48]200850.5[35.43-65.41]12,404.0[10,169.90-14,638.13]2012‡‡‡‡201669.3[54.99-80.72]57,844.5[52,044.69-63,644.34]Post-baccalaureate certificate2000††††200430.1[21.71-40.12]8,273.7[6,920.10-9,627.31]200829.6[24.42-35.45]11,748.4[10,222.85-13,274.01]201228.9[24.16-34.13]14,707.9[13,112.85-16,302.95]201632.7[27.41-38.41]18,612.0[15,491.32-21,732.66]Other professional practice doctoral degree2000††††2004††††2008††††201267.5[59.98-74.27]30,003.9[27,189.71-32,818.05]201660.1[48.70-70.51]31,208.1[28,302.65-34,113.54]Not in a degree program200014.4[12.26-16.73]9,457.4[8,541.87-10,372.87]200428.0[20.54-36.93]13,937.7[12,382.24-15,493.07]200817.5[12.42-24.21]12,693.8[10,148.32-15,239.37]201210.9[6.87-16.89]12,585.8[9,173.47-15,998.07]20168.8 ![4.30-17.01]‡‡20002004200820122016 Total loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loans (%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)EstimatesTotal30.413,528.540.015,741.442.418,435.845.121,425.644.223,366.5Graduate programsBusiness administration (MBA)24.613,853.939.114,987.941.716,673.537.519,149.538.720,919.0Education (any master's)24.49,399.734.811,917.344.113,053.949.014,252.345.015,251.7Other master of arts (MA)33.211,664.741.312,260.448.915,724.145.717,954.443.717,671.3Other master of science (MS)24.411,769.931.813,212.035.716,129.239.517,739.240.618,285.2Other master's degree38.812,527.049.312,998.146.317,895.056.019,370.849.720,440.8PhD except in education21.811,479.819.912,259.925.816,750.317.318,337.023.318,446.1Education (any doctorate)21.112,010.527.113,579.242.215,373.449.517,788.144.716,804.5Other doctoral degree25.014,890.249.520,476.053.526,612.848.426,115.855.828,357.3Medicine (MD)68.918,393.977.328,441.276.634,440.780.741,215.974.451,296.6Other health science degree76.117,310.981.724,994.582.129,734.787.336,425.777.549,062.4Law (LLB or JD)81.820,348.581.022,599.182.131,942.281.640,424.472.141,588.2Theology (MDiv, MHL, BD)23.0‡30.010,588.550.512,404.0‡‡69.357,844.5Post-baccalaureate certificate——30.18,273.729.611,748.428.914,707.932.718,612.0Other professional practice doctoral degree——————67.530,003.960.131,208.1Not in a degree program14.49,457.428.013,937.717.512,693.810.912,585.88.8 !‡20002004200820122016 Total loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loans (%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)EstimatesTotal30.413,528.540.015,741.442.418,435.845.121,425.644.223,366.5Graduate programsBusiness administration (MBA)24.613,853.939.114,987.941.716,673.537.519,149.538.720,919.0Education (any master's)24.49,399.734.811,917.344.113,053.949.014,252.345.015,251.7Other master of arts (MA)33.211,664.741.312,260.448.915,724.145.717,954.443.717,671.3Other master of science (MS)24.411,769.931.813,212.035.716,129.239.517,739.240.618,285.2Other master's degree38.812,527.049.312,998.146.317,895.056.019,370.849.720,440.8PhD except in education21.811,479.819.912,259.925.816,750.317.318,337.023.318,446.1Education (any doctorate)21.112,010.527.113,579.242.215,373.449.517,788.144.716,804.5Other doctoral degree25.014,890.249.520,476.053.526,612.848.426,115.855.828,357.3Medicine (MD)68.918,393.977.328,441.276.634,440.780.741,215.974.451,296.6Other health science degree76.117,310.981.724,994.582.129,734.787.336,425.777.549,062.4Law (LLB or JD)81.820,348.581.022,599.182.131,942.281.640,424.472.141,588.2Theology (MDiv, MHL, BD)23.0‡30.010,588.550.512,404.0‡‡69.357,844.5Post-baccalaureate certificate——30.18,273.729.611,748.428.914,707.932.718,612.0Other professional practice doctoral degree——————67.530,003.960.131,208.1Not in a degree program14.49,457.428.013,937.717.512,693.810.912,585.88.8 !‡Standard Error (BRR)Total0.35{|2000|{164.31|1.16{|2004|{277.00|0.34{|2008|{154.93|0.24{|2012|{107.61|0.27{|2016|{128.27|Graduate programsBusiness administration (MBA)1.55{|2000|{926.53|3.49{|2004|{924.34|2.38{|2008|{776.01|2.50{|2012|{1,011.83|1.78{|2016|{1,315.82|Education (any master's)1.28{|2000|{307.11|1.81{|2004|{401.66|1.98{|2008|{413.83|1.82{|2012|{434.00|1.91{|2016|{601.25|Other master of arts (MA)2.13{|2000|{520.54|3.58{|2004|{552.07|2.45{|2008|{596.53|3.09{|2012|{1,075.40|2.64{|2016|{957.85|Other master of science (MS)1.39{|2000|{658.07|2.05{|2004|{721.06|1.70{|2008|{546.40|1.61{|2012|{578.95|1.24{|2016|{576.30|Other master's degree1.71{|2000|{396.36|3.12{|2004|{502.25|2.00{|2008|{524.15|1.78{|2012|{565.32|1.54{|2016|{666.25|PhD except in education1.76{|2000|{948.97|1.19{|2004|{647.97|3.74{|2008|{1,140.16|0.85{|2012|{707.63|1.74{|2016|{981.68|Education (any doctorate)2.05{|2000|{683.50|2.46{|2004|{692.85|7.64{|2008|{2,059.29|2.24{|2012|{731.81|3.49{|2016|{681.11|Other doctoral degree4.52{|2000|{2,037.37|4.17{|2004|{1,266.07|3.27{|2008|{1,033.36|2.90{|2012|{1,105.68|3.64{|2016|{2,071.62|Medicine (MD)3.06{|2000|{753.59|2.59{|2004|{1,103.80|2.65{|2008|{1,187.84|2.13{|2012|{1,046.86|3.06{|2016|{1,909.80|Other health science degree2.71{|2000|{701.44|2.59{|2004|{1,150.04|2.63{|2008|{1,172.31|2.21{|2012|{1,103.84|3.38{|2016|{2,968.41|Law (LLB or JD)1.89{|2000|{449.30|1.90{|2004|{901.97|1.75{|2008|{840.88|1.67{|2012|{1,010.00|3.14{|2016|{1,674.10|Theology (MDiv, MHL, BD)5.52‡5.06{|2004|{1,447.74|7.84{|2008|{1,132.92|‡‡6.64{|2016|{2,941.09|Post-baccalaureate certificate††4.71{|2004|{686.41|2.81{|2008|{773.62|2.53{|2012|{808.85|2.80{|2016|{1,582.49|Other professional practice doctoral degree††††††3.64{|2012|{1,427.06|5.62{|2016|{1,473.35|Not in a degree program1.12{|2000|{457.75|4.18{|2004|{788.75|2.98{|2008|{1,290.84|2.50{|2012|{1,730.38|3.08‡Relative Standard Error (%)Total1.141.212.901.760.800.840.540.500.620.55Graduate programsBusiness administration (MBA)6.326.698.926.175.714.656.655.284.596.29Education (any master's)5.273.275.203.374.493.173.723.054.253.94Other master of arts (MA)6.424.468.674.505.023.796.765.996.045.42Other master of science (MS)5.715.596.435.464.763.394.093.263.063.15Other master's degree4.393.166.343.864.322.933.182.923.093.26PhD except in education8.088.275.975.2914.496.814.913.867.455.32Education (any doctorate)9.705.699.065.1018.1113.404.524.117.814.05Other doctoral degree18.0913.688.446.186.123.885.984.236.527.31Medicine (MD)4.444.103.353.883.463.452.632.544.123.72Other health science degree3.564.053.184.603.213.942.533.034.366.05Law (LLB or JD)2.312.212.343.992.132.632.052.504.354.03Theology (MDiv, MHL, BD)23.93‡16.8813.6715.549.13‡‡9.575.08Post-baccalaureate certificate††15.638.309.466.588.775.508.578.50Other professional practice doctoral degree††††††5.404.769.354.72Not in a degree program7.784.8414.935.6616.9810.1722.8913.7535.11‡Weighted Sample Sizes (n/1,000s)Total2,616.9794.62,824.31,130.73,492.01,479.33,682.21,659.63,572.91,577.6Graduate programsBusiness administration (MBA)309.476.1319.4124.9419.7174.9403.0151.2369.2143.0Education (any master's)440.6107.3513.9179.0709.8313.1595.0291.3490.9220.9Other master of arts (MA)166.455.3173.871.7238.9116.8284.2130.0253.7110.9Other master of science (MS)305.174.3367.1116.8475.2169.7666.3263.0712.5289.3Other master's degree327.0127.0306.6151.2406.6188.1543.4304.0622.7309.5PhD except in education213.846.6230.945.9325.283.9324.356.3245.057.1Education (any doctorate)60.912.960.716.488.537.395.147.084.037.6Other doctoral degree70.217.595.347.1138.073.975.636.6129.272.0Medicine (MD)73.850.976.559.169.052.891.573.977.357.5Other health science degree81.762.280.665.860.249.4107.593.891.771.1Law (LLB or JD)120.398.5153.2124.1149.2122.5129.0105.390.865.5Theology (MDiv, MHL, BD)20.0‡39.511.916.48.3‡‡35.524.6Post-baccalaureate certificate‡‡134.340.5160.047.4212.661.4217.571.1Other professional practice doctoral degree‡‡‡‡‡‡51.034.466.239.8Not in a degree program427.561.4272.476.3235.241.3102.911.286.7‡20002004200820122016 Total loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loansTotal loans (%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0) Pct.95% CIAmt.95% CIPct.95% CIAmt.95% CIPct.95% CIAmt.95% CIPct.95% CIAmt.95% CIPct.95% CIAmt.95% CIEstimatesTotal30.4[29.67-31.06]13,528.5[13,199.84-13,857.07]40.0[37.77-42.34]15,741.4[15,195.16-16,287.66]42.4[41.70-43.03]18,435.8[18,130.31-18,741.36]45.1[44.60-45.55]21,425.6[21,213.35-21,637.77]44.2[43.61-44.70]23,366.5[23,113.50-23,619.41]Graduate programsBusiness administration (MBA)24.6[21.61-27.82]13,853.9[12,000.81-15,706.91]39.1[32.46-46.14]14,987.9[13,165.10-16,810.69]41.7[37.06-46.42]16,673.5[15,143.18-18,203.76]37.5[32.75-42.56]19,149.5[17,154.23-21,144.87]38.7[35.29-42.29]20,919.0[18,324.22-23,513.83]Education (any master's)24.4[21.88-27.01]9,399.7[8,785.42-10,013.88]34.8[31.34-38.48]11,917.3[11,125.26-12,709.42]44.1[40.25-48.04]13,053.9[12,237.83-13,869.96]49.0[45.38-52.56]14,252.3[13,396.42-15,108.12]45.0[41.26-48.79]15,251.7[14,066.00-16,437.32]Other master of arts (MA)33.2[29.08-37.60]11,664.7[10,623.59-12,705.77]41.3[34.43-48.46]12,260.4[11,171.68-13,349.05]48.9[44.07-53.71]15,724.1[14,547.76-16,900.47]45.7[39.74-51.87]17,954.4[15,833.74-20,075.10]43.7[38.61-48.99]17,671.3[15,782.41-19,560.19]Other master of science (MS)24.4[21.69-27.25]11,769.9[10,453.76-13,086.06]31.8[27.93-35.99]13,212.0[11,790.02-14,633.89]35.7[32.43-39.12]16,129.2[15,051.67-17,206.69]39.5[36.35-42.70]17,739.2[16,597.46-18,880.84]40.6[38.18-43.08]18,285.2[17,148.78-19,421.70]Other master's degree38.8[35.49-42.31]12,527.0[11,734.30-13,319.75]49.3[43.19-55.45]12,998.1[12,007.71-13,988.57]46.3[42.34-50.21]17,895.0[16,861.42-18,928.66]56.0[52.43-59.43]19,370.8[18,255.98-20,485.61]49.7[46.68-52.74]20,440.8[19,126.99-21,754.70]PhD except in education21.8[18.48-25.53]11,479.8[9,581.87-13,377.77]19.9[17.63-22.31]12,259.9[10,982.13-13,537.72]25.8[19.13-33.82]16,750.3[14,501.87-18,998.67]17.3[15.73-19.09]18,337.0[16,941.57-19,732.48]23.3[20.06-26.91]18,446.1[16,510.18-20,381.94]Education (any doctorate)21.1[17.33-25.53]12,010.5[10,643.53-13,377.54]27.1[22.54-32.21]13,579.2[12,212.87-14,945.46]42.2[28.22-57.48]15,373.4[11,312.49-19,434.34]49.5[45.08-53.88]17,788.1[16,345.02-19,231.27]44.7[37.98-51.67]16,804.5[15,461.35-18,147.66]Other doctoral degree25.0[17.07-35.07]14,890.2[10,815.41-18,964.90]49.5[41.31-57.63]20,476.0[17,979.36-22,972.73]53.5[47.04-59.88]26,612.8[24,575.06-28,650.62]48.4[42.73-54.10]26,115.8[23,935.44-28,296.24]55.8[48.51-62.76]28,357.3[24,272.11-32,442.57]Medicine (MD)68.9[62.48-74.68]18,393.9[16,886.69-19,901.04]77.3[71.80-82.01]28,441.2[26,264.51-30,617.89]76.6[70.97-81.42]34,440.7[32,098.32-36,783.15]80.7[76.17-84.56]41,215.9[39,151.49-43,280.30]74.4[67.93-79.99]51,296.6[47,530.50-55,062.77]Other health science degree76.1[70.27-81.09]17,310.9[15,908.04-18,713.80]81.7[76.03-86.28]24,994.5[22,726.62-27,262.39]82.1[76.34-86.75]29,734.7[27,422.86-32,046.44]87.3[82.23-91.02]36,425.7[34,248.96-38,602.50]77.5[70.18-83.50]49,062.4[43,208.71-54,916.12]Law (LLB or JD)81.8[77.75-85.33]20,348.5[19,449.89-21,247.11]81.0[76.98-84.47]22,599.1[20,820.37-24,377.73]82.1[78.39-85.29]31,942.2[30,284.02-33,600.46]81.6[78.10-84.70]40,424.4[38,432.63-42,416.07]72.1[65.54-77.88]41,588.2[38,286.86-44,889.52]Theology (MDiv, MHL, BD)23.0[13.85-35.81]‡‡30.0[21.03-40.80]10,588.5[7,733.61-13,443.48]50.5[35.43-65.41]12,404.0[10,169.90-14,638.13]‡‡‡‡69.3[54.99-80.72]57,844.5[52,044.69-63,644.34]Post-baccalaureate certificate††††30.1[21.71-40.12]8,273.7[6,920.10-9,627.31]29.6[24.42-35.45]11,748.4[10,222.85-13,274.01]28.9[24.16-34.13]14,707.9[13,112.85-16,302.95]32.7[27.41-38.41]18,612.0[15,491.32-21,732.66]Other professional practice doctoral degree††††††††††††67.5[59.98-74.27]30,003.9[27,189.71-32,818.05]60.1[48.70-70.51]31,208.1[28,302.65-34,113.54]Not in a degree program14.4[12.26-16.73]9,457.4[8,541.87-10,372.87]28.0[20.54-36.93]13,937.7[12,382.24-15,493.07]17.5[12.42-24.21]12,693.8[10,148.32-15,239.37]10.9[6.87-16.89]12,585.8[9,173.47-15,998.07]8.8 ![4.30-17.01]‡‡— Not available.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.— Not available.† Not applicable.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.STDERR-SOURCE-END— Not available.† Not applicable.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: GRADPGM.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: TOTLOAN and GRADPGM. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: TOTLOAN (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), GRADPGM2 (NPSAS:2000) and GRADPGM (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.truebfebkaa76bfebkaa762Average Student budget (attendance adjusted) and Average Tuition and fees paid by Graduate degree program for years 1996, 2000, 2004, 2008, 2012 and 2016 Student budget (attendance adjusted)Tuition and fees paid(Avg)(Avg)EstimatesTotal199611,150.44,695.0200014,892.75,602.8200418,547.97,361.7200822,288.69,174.9201226,768.010,894.3201628,898.114,791.7Graduate degree programMaster's degree199610,307.54,168.2200013,264.14,747.4200415,947.66,086.4200819,893.97,868.5201222,944.19,302.7201624,980.612,174.5Doctoral degree199613,794.95,534.5200019,562.66,378.4200424,344.09,720.8200829,301.411,658.8201236,562.614,577.5201631,971.716,052.6First-professional degree199622,754.311,148.6200027,760.413,489.3200432,146.314,604.1200841,896.221,434.3201248,855.720,971.1201658,656.634,910.7Post-BA or post-master's certificate1996——200010,667.53,546.7200411,190.43,841.5200812,383.44,228.7201215,815.45,934.9201619,202.68,735.9Not in a degree program1996——2000——200411,775.63,938.220089,904.83,318.0201210,276.83,371.620167,850.32,445.3Other19965,327.82,023.020005,569.71,618.92004——2008——2012——2016——Average Student budget (attendance adjusted) and Average Tuition and fees paid by Graduate degree program for years 1996, 2000, 2004, 2008, 2012 and 2016 Student budget (attendance adjusted)Tuition and fees paid(Avg)(Avg)EstimatesTotal199611,150.44,695.0200014,892.75,602.8200418,547.97,361.7200822,288.69,174.9201226,768.010,894.3201628,898.114,791.7Graduate degree programMaster's degree199610,307.54,168.2200013,264.14,747.4200415,947.66,086.4200819,893.97,868.5201222,944.19,302.7201624,980.612,174.5Doctoral degree199613,794.95,534.5200019,562.66,378.4200424,344.09,720.8200829,301.411,658.8201236,562.614,577.5201631,971.716,052.6First-professional degree199622,754.311,148.6200027,760.413,489.3200432,146.314,604.1200841,896.221,434.3201248,855.720,971.1201658,656.634,910.7Post-BA or post-master's certificate1996——200010,667.53,546.7200411,190.43,841.5200812,383.44,228.7201215,815.45,934.9201619,202.68,735.9Not in a degree program1996——2000——200411,775.63,938.220089,904.83,318.0201210,276.83,371.620167,850.32,445.3Other19965,327.82,023.020005,569.71,618.92004——2008——2012——2016——Standard Error (BRR)Total1996{|1996|{216.29|{|1996|{117.65|2000{|2000|{176.97|{|2000|{92.37|2004{|2004|{527.12|{|2004|{362.89|2008{|2008|{214.47|{|2008|{171.08|2012{|2012|{274.58|{|2012|{181.23|2016{|2016|{325.81|{|2016|{225.30|Graduate degree programMaster's degree1996{|1996|{246.68|{|1996|{130.75|2000{|2000|{200.65|{|2000|{110.78|2004{|2004|{471.88|{|2004|{306.97|2008{|2008|{246.83|{|2008|{221.42|2012{|2012|{306.00|{|2012|{178.75|2016{|2016|{390.51|{|2016|{254.75|Doctoral degree1996{|1996|{499.16|{|1996|{297.00|2000{|2000|{277.75|{|2000|{156.51|2004{|2004|{847.86|{|2004|{506.96|2008{|2008|{542.34|{|2008|{299.15|2012{|2012|{689.93|{|2012|{417.93|2016{|2016|{1,016.13|{|2016|{670.18|First-professional degree1996{|1996|{695.63|{|1996|{536.48|2000{|2000|{685.07|{|2000|{479.32|2004{|2004|{1,364.39|{|2004|{1,088.45|2008{|2008|{652.58|{|2008|{448.63|2012{|2012|{722.55|{|2012|{771.88|2016{|2016|{1,214.73|{|2016|{899.58|Post-BA or post-master's certificate1996††2000{|2000|{477.19|{|2000|{222.20|2004{|2004|{875.86|{|2004|{408.14|2008{|2008|{596.12|{|2008|{260.33|2012{|2012|{777.21|{|2012|{369.05|2016{|2016|{1,063.14|{|2016|{621.88|Not in a degree program1996††2000††2004{|2004|{1,006.67|{|2004|{369.28|2008{|2008|{600.63|{|2008|{326.89|2012{|2012|{776.21|{|2012|{353.11|2016{|2016|{501.91|{|2016|{250.91|Other1996{|1996|{306.74|{|1996|{188.26|2000{|2000|{221.79|{|2000|{101.57|2004††2008††2012††2016††Relative Standard Error (%)Total19961.942.5120001.191.6520042.844.9320080.961.8620121.031.6620161.131.52Graduate degree programMaster's degree19962.393.1420001.512.3320042.965.0420081.242.8120121.331.9220161.562.09Doctoral degree19963.625.3720001.422.4520043.485.2220081.852.5720121.892.8720163.184.17First-professional degree19963.064.8120002.473.5520044.247.4520081.562.0920121.483.6820162.072.58Post-BA or post-master's certificate1996††20004.476.2720047.8310.6220084.816.1620124.916.2220165.547.12Not in a degree program1996††2000††20048.559.3820086.069.8520127.5510.4720166.3910.26Other19965.769.3120003.986.272004††2008††2012††2016††Weighted Sample Sizes (n/1,000s)Total19962,729.82,682.120002,524.22,524.220042,721.82,721.820083,340.83,340.820123,533.03,533.020163,411.73,411.7Graduate degree programMaster's degree19961,548.81,520.820001,486.21,486.220041,621.61,621.620082,151.82,151.820122,388.42,388.420162,334.52,334.5Doctoral degree1996338.1335.52000337.6337.62004377.8377.82008532.7532.72012475.1475.12016400.1400.1First-professional degree1996305.2298.72000290.7290.72004343.4343.42008286.8286.82012373.1373.12016389.9389.9Post-BA or post-master's certificate1996‡‡2000181.0181.02004129.3129.32008148.9148.92012203.4203.42016207.6207.6Not in a degree program1996‡‡2000‡‡2004249.8249.82008220.6220.6201293.093.0201679.679.6Other1996537.7527.12000228.7228.72004‡‡2008‡‡2012‡‡2016‡‡Average Student budget (attendance adjusted) and Average Tuition and fees paid by Graduate degree program for years 1996, 2000, 2004, 2008, 2012 and 2016 Student budget (attendance adjusted)Tuition and fees paid(Avg)(Avg)Amt.95% CIAmt.95% CIEstimatesTotal199611,150.4[10,713.94-11,586.88]4,695.0[4,457.55-4,932.38]200014,892.7[14,538.79-15,246.68]5,602.8[5,418.05-5,787.54]200418,547.9[17,508.39-19,587.37]7,361.7[6,646.07-8,077.31]200822,288.6[21,865.72-22,711.57]9,174.9[8,837.50-9,512.26]201226,768.0[26,226.51-27,309.47]10,894.3[10,536.89-11,251.68]201628,898.1[28,255.63-29,540.61]14,791.7[14,347.38-15,235.97]Graduate degree programMaster's degree199610,307.5[9,809.72-10,805.31]4,168.2[3,904.37-4,432.09]200013,264.1[12,862.84-13,665.42]4,747.4[4,525.86-4,968.97]200415,947.6[15,017.04-16,878.12]6,086.4[5,481.05-6,691.74]200819,893.9[19,407.12-20,380.63]7,868.5[7,431.87-8,305.13]201222,944.1[22,340.61-23,547.49]9,302.7[8,950.21-9,655.22]201624,980.6[24,210.48-25,750.66]12,174.5[11,672.17-12,676.88]Doctoral degree199613,794.9[12,787.57-14,802.17]5,534.5[4,935.18-6,133.89]200019,562.6[19,007.11-20,118.12]6,378.4[6,065.43-6,691.45]200424,344.0[22,672.04-26,016.00]9,720.8[8,721.08-10,720.55]200829,301.4[28,231.89-30,370.88]11,658.8[11,068.91-12,248.74]201236,562.6[35,202.07-37,923.14]14,577.5[13,753.30-15,401.61]201631,971.7[29,967.86-33,975.47]16,052.6[14,730.98-17,374.17]First-professional degree199622,754.3[21,350.49-24,158.07]11,148.6[10,066.04-12,231.26]200027,760.4[26,390.22-29,130.52]13,489.3[12,530.62-14,447.91]200432,146.3[29,455.68-34,836.84]14,604.1[12,457.70-16,750.53]200841,896.2[40,609.36-43,183.14]21,434.3[20,549.57-22,318.95]201248,855.7[47,430.80-50,280.55]20,971.1[19,448.99-22,493.28]201658,656.6[56,261.10-61,052.01]34,910.7[33,136.73-36,684.65]Post-BA or post-master's certificate1996††††200010,667.5[9,713.14-11,621.90]3,546.7[3,102.29-3,991.11]200411,190.4[9,463.22-12,917.63]3,841.5[3,036.65-4,646.36]200812,383.4[11,207.86-13,558.97]4,228.7[3,715.36-4,742.09]201215,815.4[14,282.78-17,348.11]5,934.9[5,207.17-6,662.70]201619,202.6[17,106.04-21,299.06]8,735.9[7,509.60-9,962.28]Not in a degree program1996††††2000††††200411,775.6[9,790.50-13,760.79]3,938.2[3,209.97-4,666.43]20089,904.8[8,720.32-11,089.19]3,318.0[2,673.42-3,962.67]201210,276.8[8,746.07-11,807.46]3,371.6[2,675.26-4,067.91]20167,850.3[6,860.50-8,840.02]2,445.3[1,950.48-2,940.07]Other19965,327.8[4,708.75-5,946.77]2,023.0[1,643.14-2,402.94]20005,569.7[5,126.15-6,013.31]1,618.9[1,415.74-1,822.00]2004††††2008††††2012††††2016††††199620002004200820122016 Student budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paid (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)EstimatesTotal11,150.44,695.014,892.75,602.818,547.97,361.722,288.69,174.926,768.010,894.328,898.114,791.7Graduate degree programMaster's degree10,307.54,168.213,264.14,747.415,947.66,086.419,893.97,868.522,944.19,302.724,980.612,174.5Doctoral degree13,794.95,534.519,562.66,378.424,344.09,720.829,301.411,658.836,562.614,577.531,971.716,052.6First-professional degree22,754.311,148.627,760.413,489.332,146.314,604.141,896.221,434.348,855.720,971.158,656.634,910.7Post-BA or post-master's certificate——10,667.53,546.711,190.43,841.512,383.44,228.715,815.45,934.919,202.68,735.9Not in a degree program————11,775.63,938.29,904.83,318.010,276.83,371.67,850.32,445.3Other5,327.82,023.05,569.71,618.9————————199620002004200820122016 Student budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paid (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)EstimatesTotal11,150.44,695.014,892.75,602.818,547.97,361.722,288.69,174.926,768.010,894.328,898.114,791.7Graduate degree programMaster's degree10,307.54,168.213,264.14,747.415,947.66,086.419,893.97,868.522,944.19,302.724,980.612,174.5Doctoral degree13,794.95,534.519,562.66,378.424,344.09,720.829,301.411,658.836,562.614,577.531,971.716,052.6First-professional degree22,754.311,148.627,760.413,489.332,146.314,604.141,896.221,434.348,855.720,971.158,656.634,910.7Post-BA or post-master's certificate——10,667.53,546.711,190.43,841.512,383.44,228.715,815.45,934.919,202.68,735.9Not in a degree program————11,775.63,938.29,904.83,318.010,276.83,371.67,850.32,445.3Other5,327.82,023.05,569.71,618.9————————Standard Error (BRR)Total{|1996|{216.29|{|1996|{117.65|{|2000|{176.97|{|2000|{92.37|{|2004|{527.12|{|2004|{362.89|{|2008|{214.47|{|2008|{171.08|{|2012|{274.58|{|2012|{181.23|{|2016|{325.81|{|2016|{225.30|Graduate degree programMaster's degree{|1996|{246.68|{|1996|{130.75|{|2000|{200.65|{|2000|{110.78|{|2004|{471.88|{|2004|{306.97|{|2008|{246.83|{|2008|{221.42|{|2012|{306.00|{|2012|{178.75|{|2016|{390.51|{|2016|{254.75|Doctoral degree{|1996|{499.16|{|1996|{297.00|{|2000|{277.75|{|2000|{156.51|{|2004|{847.86|{|2004|{506.96|{|2008|{542.34|{|2008|{299.15|{|2012|{689.93|{|2012|{417.93|{|2016|{1,016.13|{|2016|{670.18|First-professional degree{|1996|{695.63|{|1996|{536.48|{|2000|{685.07|{|2000|{479.32|{|2004|{1,364.39|{|2004|{1,088.45|{|2008|{652.58|{|2008|{448.63|{|2012|{722.55|{|2012|{771.88|{|2016|{1,214.73|{|2016|{899.58|Post-BA or post-master's certificate††{|2000|{477.19|{|2000|{222.20|{|2004|{875.86|{|2004|{408.14|{|2008|{596.12|{|2008|{260.33|{|2012|{777.21|{|2012|{369.05|{|2016|{1,063.14|{|2016|{621.88|Not in a degree program††††{|2004|{1,006.67|{|2004|{369.28|{|2008|{600.63|{|2008|{326.89|{|2012|{776.21|{|2012|{353.11|{|2016|{501.91|{|2016|{250.91|Other{|1996|{306.74|{|1996|{188.26|{|2000|{221.79|{|2000|{101.57|††††††††Relative Standard Error (%)Total1.942.511.191.652.844.930.961.861.031.661.131.52Graduate degree programMaster's degree2.393.141.512.332.965.041.242.811.331.921.562.09Doctoral degree3.625.371.422.453.485.221.852.571.892.873.184.17First-professional degree3.064.812.473.554.247.451.562.091.483.682.072.58Post-BA or post-master's certificate††4.476.277.8310.624.816.164.916.225.547.12Not in a degree program††††8.559.386.069.857.5510.476.3910.26Other5.769.313.986.27††††††††Weighted Sample Sizes (n/1,000s)Total2,729.82,682.12,524.22,524.22,721.82,721.83,340.83,340.83,533.03,533.03,411.73,411.7Graduate degree programMaster's degree1,548.81,520.81,486.21,486.21,621.61,621.62,151.82,151.82,388.42,388.42,334.52,334.5Doctoral degree338.1335.5337.6337.6377.8377.8532.7532.7475.1475.1400.1400.1First-professional degree305.2298.7290.7290.7343.4343.4286.8286.8373.1373.1389.9389.9Post-BA or post-master's certificate‡‡181.0181.0129.3129.3148.9148.9203.4203.4207.6207.6Not in a degree program‡‡‡‡249.8249.8220.6220.693.093.079.679.6Other537.7527.1228.7228.7‡‡‡‡‡‡‡‡199620002004200820122016 Student budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paidStudent budget (attendance adjusted)Tuition and fees paid (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg) Amt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIEstimatesTotal11,150.4[10,713.94-11,586.88]4,695.0[4,457.55-4,932.38]14,892.7[14,538.79-15,246.68]5,602.8[5,418.05-5,787.54]18,547.9[17,508.39-19,587.37]7,361.7[6,646.07-8,077.31]22,288.6[21,865.72-22,711.57]9,174.9[8,837.50-9,512.26]26,768.0[26,226.51-27,309.47]10,894.3[10,536.89-11,251.68]28,898.1[28,255.63-29,540.61]14,791.7[14,347.38-15,235.97]Graduate degree programMaster's degree10,307.5[9,809.72-10,805.31]4,168.2[3,904.37-4,432.09]13,264.1[12,862.84-13,665.42]4,747.4[4,525.86-4,968.97]15,947.6[15,017.04-16,878.12]6,086.4[5,481.05-6,691.74]19,893.9[19,407.12-20,380.63]7,868.5[7,431.87-8,305.13]22,944.1[22,340.61-23,547.49]9,302.7[8,950.21-9,655.22]24,980.6[24,210.48-25,750.66]12,174.5[11,672.17-12,676.88]Doctoral degree13,794.9[12,787.57-14,802.17]5,534.5[4,935.18-6,133.89]19,562.6[19,007.11-20,118.12]6,378.4[6,065.43-6,691.45]24,344.0[22,672.04-26,016.00]9,720.8[8,721.08-10,720.55]29,301.4[28,231.89-30,370.88]11,658.8[11,068.91-12,248.74]36,562.6[35,202.07-37,923.14]14,577.5[13,753.30-15,401.61]31,971.7[29,967.86-33,975.47]16,052.6[14,730.98-17,374.17]First-professional degree22,754.3[21,350.49-24,158.07]11,148.6[10,066.04-12,231.26]27,760.4[26,390.22-29,130.52]13,489.3[12,530.62-14,447.91]32,146.3[29,455.68-34,836.84]14,604.1[12,457.70-16,750.53]41,896.2[40,609.36-43,183.14]21,434.3[20,549.57-22,318.95]48,855.7[47,430.80-50,280.55]20,971.1[19,448.99-22,493.28]58,656.6[56,261.10-61,052.01]34,910.7[33,136.73-36,684.65]Post-BA or post-master's certificate††††10,667.5[9,713.14-11,621.90]3,546.7[3,102.29-3,991.11]11,190.4[9,463.22-12,917.63]3,841.5[3,036.65-4,646.36]12,383.4[11,207.86-13,558.97]4,228.7[3,715.36-4,742.09]15,815.4[14,282.78-17,348.11]5,934.9[5,207.17-6,662.70]19,202.6[17,106.04-21,299.06]8,735.9[7,509.60-9,962.28]Not in a degree program††††††††11,775.6[9,790.50-13,760.79]3,938.2[3,209.97-4,666.43]9,904.8[8,720.32-11,089.19]3,318.0[2,673.42-3,962.67]10,276.8[8,746.07-11,807.46]3,371.6[2,675.26-4,067.91]7,850.3[6,860.50-8,840.02]2,445.3[1,950.48-2,940.07]Other5,327.8[4,708.75-5,946.77]2,023.0[1,643.14-2,402.94]5,569.7[5,126.15-6,013.31]1,618.9[1,415.74-1,822.00]††††††††††††††††— Not available.— Not available.† Not applicable.‡ Reporting standards not met.STDERR-SOURCE-END— Not available.† Not applicable.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: BUDGETAJ, TUITION2 and GRADDEG.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: BUDGETAJ, TUITION2 and GRADDEG. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: BUDGETAJ (NPSAS:1996, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), TUITION2 (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), GRADDEG (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and BUDGETA2 (NPSAS:2000).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96), 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.truebfebkaf20bfebkaf203Total grants with (Percent>0.5) and Average>0 Total grants by Graduate degree program for years 1996, 2000, 2004, 2008, 2012 and 2016 Total grantsTotal grants(%>0.5)(Avg>0)EstimatesTotal199629.83,962.9200038.25,983.2200440.05,832.8200841.07,444.2201235.710,772.8201640.39,519.1Graduate degree programMaster's degree199630.13,462.8200037.44,939.3200438.44,599.6200840.46,478.5201232.27,880.3201637.97,450.6Post-BA or post-master's certificate1996——200026.83,964.6200422.62,562.4200827.13,505.8201223.05,273.2201633.06,448.1Doctoral degree199637.66,043.0200050.610,837.1200455.110,425.8200852.211,840.2201256.920,143.7201653.216,015.8Not in a degree program1996——2000——200434.92,797.9200829.82,549.5201230.34,192.0201627.53,867.0Total grants with (Percent>0.5) and Average>0 Total grants by Graduate degree program for years 1996, 2000, 2004, 2008, 2012 and 2016 Total grantsTotal grants(%>0.5)(Avg>0)EstimatesTotal199629.83,962.9200038.25,983.2200440.05,832.8200841.07,444.2201235.710,772.8201640.39,519.1Graduate degree programMaster's degree199630.13,462.8200037.44,939.3200438.44,599.6200840.46,478.5201232.27,880.3201637.97,450.6Post-BA or post-master's certificate1996——200026.83,964.6200422.62,562.4200827.13,505.8201223.05,273.2201633.06,448.1Doctoral degree199637.66,043.0200050.610,837.1200455.110,425.8200852.211,840.2201256.920,143.7201653.216,015.8Not in a degree program1996——2000——200434.92,797.9200829.82,549.5201230.34,192.0201627.53,867.0Standard Error (BRR)Total19961.10{|1996|{197.12|20000.60{|2000|{209.54|20040.94{|2004|{178.62|20080.78{|2008|{208.24|20120.80{|2012|{312.01|20160.70{|2016|{284.33|Graduate degree programMaster's degree19961.21{|1996|{178.96|20000.75{|2000|{160.54|20041.30{|2004|{155.27|20081.12{|2008|{221.97|20121.04{|2012|{327.11|20160.82{|2016|{266.55|Post-BA or post-master's certificate1996††20002.28{|2000|{382.56|20042.98{|2004|{386.92|20082.94{|2008|{415.47|20123.02{|2012|{645.62|20163.50{|2016|{869.00|Doctoral degree19962.39{|1996|{606.29|20001.15{|2000|{554.65|20041.65{|2004|{470.44|20081.64{|2008|{586.95|20121.39{|2012|{617.60|20161.75{|2016|{909.04|Not in a degree program1996††2000††20042.67{|2004|{364.90|20084.05{|2008|{494.40|20124.83{|2012|{967.78|20164.14{|2016|{819.09|Relative Standard Error (%)Total19963.704.9720001.573.5020042.353.0620081.912.8020122.232.9020161.732.99Graduate degree programMaster's degree19964.015.1720002.013.2520043.383.3820082.783.4320123.234.1520162.173.58Post-BA or post-master's certificate1996††20008.539.65200413.2115.10200810.8311.85201213.1612.24201610.6013.48Doctoral degree19966.3610.0320002.285.1220042.994.5120083.144.9620122.443.0720163.305.68Not in a degree program1996††2000††20047.6713.04200813.5919.39201215.9423.09201615.0521.18Weighted Sample Sizes (n/1,000s)Total19962,762.8822.020002,616.9999.920042,824.31,129.420083,492.01,431.120123,682.21,314.420163,572.91,441.6Graduate degree programMaster's degree19961,559.5469.020001,548.6578.620041,680.8646.320082,250.2908.920122,491.8802.920162,448.9927.0Post-BA or post-master's certificate1996‡‡2000186.049.82004134.330.32008160.043.42012212.648.92016217.571.9Doctoral degree1996343.3129.22000344.9174.62004386.9213.22008551.7287.92012489.6278.72016417.3221.8Not in a degree program1996‡‡2000‡‡2004272.495.02008235.270.12012102.931.2201686.723.9Total grants with (Percent>0.5) and Average>0 Total grants by Graduate degree program for years 1996, 2000, 2004, 2008, 2012 and 2016 Total grantsTotal grants(%>0.5)(Avg>0)Pct.95% CIAmt.95% CIEstimatesTotal199629.8[27.58-32.02]3,962.9[3,565.13-4,360.69]200038.2[37.02-39.42]5,983.2[5,564.11-6,402.26]200440.0[38.15-41.85]5,832.8[5,480.58-6,185.06]200841.0[39.45-42.53]7,444.2[7,033.55-7,854.85]201235.7[34.14-37.28]10,772.8[10,157.50-11,388.08]201640.3[38.98-41.74]9,519.1[8,958.38-10,079.76]Graduate degree programMaster's degree199630.1[27.70-32.57]3,462.8[3,101.71-3,823.99]200037.4[35.87-38.87]4,939.3[4,618.24-5,260.38]200438.4[35.92-41.04]4,599.6[4,293.38-4,905.75]200840.4[38.20-42.62]6,478.5[6,040.73-6,916.17]201232.2[30.21-34.31]7,880.3[7,235.26-8,525.37]201637.9[36.25-39.49]7,450.6[6,924.91-7,976.20]Post-BA or post-master's certificate1996††††200026.8[22.47-31.59]3,964.6[3,199.51-4,729.74]200422.6[17.23-28.97]2,562.4[1,799.36-3,325.38]200827.1[21.73-33.29]3,505.8[2,686.47-4,325.08]201223.0[17.57-29.48]5,273.2[4,000.04-6,546.36]201633.0[26.53-40.27]6,448.1[4,734.42-8,161.76]Doctoral degree199637.6[32.93-42.57]6,043.0[4,819.50-7,266.51]200050.6[48.34-52.95]10,837.1[9,727.77-11,946.37]200455.1[51.83-58.32]10,425.8[9,498.14-11,353.54]200852.2[48.95-55.40]11,840.2[10,682.77-12,997.70]201256.9[54.16-59.64]20,143.7[18,925.84-21,361.65]201653.2[49.69-56.60]16,015.8[14,223.19-17,808.45]Not in a degree program1996††††2000††††200434.9[29.79-40.31]2,797.9[2,078.29-3,517.47]200829.8[22.46-38.32]2,549.5[1,574.51-3,524.43]201230.3[21.70-40.60]4,192.0[2,283.52-6,100.44]201627.5[20.13-36.37]3,867.0[2,251.74-5,482.22]199620002004200820122016 Total grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grants (%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)EstimatesTotal29.83,962.938.25,983.240.05,832.841.07,444.235.710,772.840.39,519.1Graduate degree programMaster's degree30.13,462.837.44,939.338.44,599.640.46,478.532.27,880.337.97,450.6Post-BA or post-master's certificate——26.83,964.622.62,562.427.13,505.823.05,273.233.06,448.1Doctoral degree37.66,043.050.610,837.155.110,425.852.211,840.256.920,143.753.216,015.8Not in a degree program————34.92,797.929.82,549.530.34,192.027.53,867.0199620002004200820122016 Total grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grants (%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)EstimatesTotal29.83,962.938.25,983.240.05,832.841.07,444.235.710,772.840.39,519.1Graduate degree programMaster's degree30.13,462.837.44,939.338.44,599.640.46,478.532.27,880.337.97,450.6Post-BA or post-master's certificate——26.83,964.622.62,562.427.13,505.823.05,273.233.06,448.1Doctoral degree37.66,043.050.610,837.155.110,425.852.211,840.256.920,143.753.216,015.8Not in a degree program————34.92,797.929.82,549.530.34,192.027.53,867.0Standard Error (BRR)Total1.10{|1996|{197.12|0.60{|2000|{209.54|0.94{|2004|{178.62|0.78{|2008|{208.24|0.80{|2012|{312.01|0.70{|2016|{284.33|Graduate degree programMaster's degree1.21{|1996|{178.96|0.75{|2000|{160.54|1.30{|2004|{155.27|1.12{|2008|{221.97|1.04{|2012|{327.11|0.82{|2016|{266.55|Post-BA or post-master's certificate††2.28{|2000|{382.56|2.98{|2004|{386.92|2.94{|2008|{415.47|3.02{|2012|{645.62|3.50{|2016|{869.00|Doctoral degree2.39{|1996|{606.29|1.15{|2000|{554.65|1.65{|2004|{470.44|1.64{|2008|{586.95|1.39{|2012|{617.60|1.75{|2016|{909.04|Not in a degree program††††2.67{|2004|{364.90|4.05{|2008|{494.40|4.83{|2012|{967.78|4.14{|2016|{819.09|Relative Standard Error (%)Total3.704.971.573.502.353.061.912.802.232.901.732.99Graduate degree programMaster's degree4.015.172.013.253.383.382.783.433.234.152.173.58Post-BA or post-master's certificate††8.539.6513.2115.1010.8311.8513.1612.2410.6013.48Doctoral degree6.3610.032.285.122.994.513.144.962.443.073.305.68Not in a degree program††††7.6713.0413.5919.3915.9423.0915.0521.18Weighted Sample Sizes (n/1,000s)Total2,762.8822.02,616.9999.92,824.31,129.43,492.01,431.13,682.21,314.43,572.91,441.6Graduate degree programMaster's degree1,559.5469.01,548.6578.61,680.8646.32,250.2908.92,491.8802.92,448.9927.0Post-BA or post-master's certificate‡‡186.049.8134.330.3160.043.4212.648.9217.571.9Doctoral degree343.3129.2344.9174.6386.9213.2551.7287.9489.6278.7417.3221.8Not in a degree program‡‡‡‡272.495.0235.270.1102.931.286.723.9199620002004200820122016 Total grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grantsTotal grants (%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0)(%>0.5)(Avg>0) Pct.95% CIAmt.95% CIPct.95% CIAmt.95% CIPct.95% CIAmt.95% CIPct.95% CIAmt.95% CIPct.95% CIAmt.95% CIPct.95% CIAmt.95% CIEstimatesTotal29.8[27.58-32.02]3,962.9[3,565.13-4,360.69]38.2[37.02-39.42]5,983.2[5,564.11-6,402.26]40.0[38.15-41.85]5,832.8[5,480.58-6,185.06]41.0[39.45-42.53]7,444.2[7,033.55-7,854.85]35.7[34.14-37.28]10,772.8[10,157.50-11,388.08]40.3[38.98-41.74]9,519.1[8,958.38-10,079.76]Graduate degree programMaster's degree30.1[27.70-32.57]3,462.8[3,101.71-3,823.99]37.4[35.87-38.87]4,939.3[4,618.24-5,260.38]38.4[35.92-41.04]4,599.6[4,293.38-4,905.75]40.4[38.20-42.62]6,478.5[6,040.73-6,916.17]32.2[30.21-34.31]7,880.3[7,235.26-8,525.37]37.9[36.25-39.49]7,450.6[6,924.91-7,976.20]Post-BA or post-master's certificate††††26.8[22.47-31.59]3,964.6[3,199.51-4,729.74]22.6[17.23-28.97]2,562.4[1,799.36-3,325.38]27.1[21.73-33.29]3,505.8[2,686.47-4,325.08]23.0[17.57-29.48]5,273.2[4,000.04-6,546.36]33.0[26.53-40.27]6,448.1[4,734.42-8,161.76]Doctoral degree37.6[32.93-42.57]6,043.0[4,819.50-7,266.51]50.6[48.34-52.95]10,837.1[9,727.77-11,946.37]55.1[51.83-58.32]10,425.8[9,498.14-11,353.54]52.2[48.95-55.40]11,840.2[10,682.77-12,997.70]56.9[54.16-59.64]20,143.7[18,925.84-21,361.65]53.2[49.69-56.60]16,015.8[14,223.19-17,808.45]Not in a degree program††††††††34.9[29.79-40.31]2,797.9[2,078.29-3,517.47]29.8[22.46-38.32]2,549.5[1,574.51-3,524.43]30.3[21.70-40.60]4,192.0[2,283.52-6,100.44]27.5[20.13-36.37]3,867.0[2,251.74-5,482.22]— Not available.— Not available.† Not applicable.‡ Reporting standards not met.STDERR-SOURCE-END— Not available.† Not applicable.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: GRADDEG.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: TOTGRT and GRADDEG. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: TOTGRT (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and GRADDEG (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96), 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.truebfebkaf8bfebkaf84Average>0 Amount still owed on all undergraduate loans and Amount still owed on all undergraduate loans with (Percent>1) by Graduate degree program for years 2000, 2004, 2008, 2012 and 2016 Amount still owed on all undergraduate loansAmount still owed on all undergraduate loans(Avg>0)(%>1)EstimatesTotal200013,273.633.7200417,461.236.6200821,287.740.2201227,638.242.2201627,781.143.3Graduate degree programMaster's degree200013,512.734.0200417,269.936.9200821,592.242.3201227,866.644.6201628,593.845.7Doctoral degree200012,585.828.3200417,153.228.3200822,024.332.3201227,427.929.8201623,908.034.7First-professional degree200013,593.051.0200419,196.444.9200821,406.345.1201229,061.147.8201624,975.643.8Post-BA or post-master's certificate200014,549.230.8200415,296.841.8200820,118.740.2201225,536.935.8201629,143.439.1Not in a degree program2000——200417,474.933.6200816,491.032.1201218,843.736.0201629,078.527.2Other20009,630.520.52004——2008——2012——2016——Average>0 Amount still owed on all undergraduate loans and Amount still owed on all undergraduate loans with (Percent>1) by Graduate degree program for years 2000, 2004, 2008, 2012 and 2016 Amount still owed on all undergraduate loansAmount still owed on all undergraduate loans(Avg>0)(%>1)EstimatesTotal200013,273.633.7200417,461.236.6200821,287.740.2201227,638.242.2201627,781.143.3Graduate degree programMaster's degree200013,512.734.0200417,269.936.9200821,592.242.3201227,866.644.6201628,593.845.7Doctoral degree200012,585.828.3200417,153.228.3200822,024.332.3201227,427.929.8201623,908.034.7First-professional degree200013,593.051.0200419,196.444.9200821,406.345.1201229,061.147.8201624,975.643.8Post-BA or post-master's certificate200014,549.230.8200415,296.841.8200820,118.740.2201225,536.935.8201629,143.439.1Not in a degree program2000——200417,474.933.6200816,491.032.1201218,843.736.0201629,078.527.2Other20009,630.520.52004——2008——2012——2016——Standard Error (BRR)Total2000{|2000|{246.98|0.522004{|2004|{337.10|0.812008{|2008|{345.13|0.672012{|2012|{429.06|0.652016{|2016|{417.04|0.63Graduate degree programMaster's degree2000{|2000|{331.85|0.682004{|2004|{503.06|1.042008{|2008|{458.70|0.852012{|2012|{549.26|0.892016{|2016|{538.65|0.83Doctoral degree2000{|2000|{536.67|1.782004{|2004|{501.59|1.242008{|2008|{1,550.16|1.682012{|2012|{674.43|0.852016{|2016|{1,052.75|1.31First-professional degree2000{|2000|{526.31|1.892004{|2004|{808.44|1.892008{|2008|{704.99|1.512012{|2012|{1,058.28|1.402016{|2016|{1,008.63|1.71Post-BA or post-master's certificate2000{|2000|{850.25|2.262004{|2004|{1,479.84|4.262008{|2008|{1,194.15|3.122012{|2012|{1,513.38|2.852016{|2016|{1,828.38|2.50Not in a degree program2000††2004{|2004|{1,398.34|2.732008{|2008|{1,646.56|4.112012{|2012|{2,895.40|4.452016{|2016|{3,918.62|4.33Other2000{|2000|{857.72|1.812004††2008††2012††2016††Relative Standard Error (%)Total20001.861.5520041.932.2020081.621.6720121.551.5320161.501.45Graduate degree programMaster's degree20002.462.0020042.912.8220082.122.0020121.971.9820161.881.81Doctoral degree20004.266.3120042.924.3620087.045.2020122.462.8720164.403.78First-professional degree20003.873.7120044.214.2120083.293.3420123.642.9320164.043.91Post-BA or post-master's certificate20005.847.3220049.6710.2120085.947.7720125.937.9820166.276.39Not in a degree program2000††20048.008.1220089.9812.83201215.3712.37201613.4815.96Other20008.918.862004††2008††2012††2016††Weighted Sample Sizes (n/1,000s)Total2000881.12,616.920041,033.92,824.320081,402.73,492.020121,554.43,682.220161,547.93,572.9Graduate degree programMaster's degree2000526.01,548.62004619.61,680.82008951.62,250.220121,111.32,491.820161,118.32,448.9Doctoral degree200097.5344.92004109.6386.92008178.3551.72012145.7489.62016144.6417.3First-professional degree2000150.8295.92004157.1349.82008133.1294.82012184.3385.22016176.4402.5Post-BA or post-master's certificate200057.4186.0200456.1134.3200864.3160.0201276.1212.6201685.0217.5Not in a degree program2000‡‡200491.5272.4200875.4235.2201237.0102.9201623.686.7Other200049.4241.52004‡‡2008‡‡2012‡‡2016‡‡Average>0 Amount still owed on all undergraduate loans and Amount still owed on all undergraduate loans with (Percent>1) by Graduate degree program for years 2000, 2004, 2008, 2012 and 2016 Amount still owed on all undergraduate loansAmount still owed on all undergraduate loans(Avg>0)(%>1)Amt.95% CIPct.95% CIEstimatesTotal200013,273.6[12,779.66-13,767.59]33.7[32.63-34.73]200417,461.2[16,796.46-18,125.98]36.6[35.03-38.21]200821,287.7[20,607.09-21,968.30]40.2[38.85-41.50]201227,638.2[26,792.14-28,484.35]42.2[40.94-43.49]201627,781.1[26,958.74-28,603.56]43.3[42.09-44.57]Graduate degree programMaster's degree200013,512.7[12,848.97-14,176.39]34.0[32.62-35.34]200417,269.9[16,277.87-18,261.93]36.9[34.83-38.94]200821,592.2[20,687.62-22,496.74]42.3[40.63-43.97]201227,866.6[26,783.46-28,949.73]44.6[42.86-46.35]201628,593.8[27,531.55-29,655.99]45.7[44.04-47.30]Doctoral degree200012,585.8[11,512.50-13,659.18]28.3[24.85-31.97]200417,153.2[16,164.05-18,142.32]28.3[25.96-30.84]200822,024.3[18,967.39-25,081.23]32.3[29.10-35.72]201227,427.9[26,097.95-28,757.88]29.8[28.10-31.46]201623,908.0[21,831.96-25,984.01]34.7[32.13-37.29]First-professional degree200013,593.0[12,540.37-14,645.61]51.0[47.19-54.75]200419,196.4[17,602.18-20,790.66]44.9[41.20-48.65]200821,406.3[20,016.09-22,796.57]45.1[42.19-48.13]201229,061.1[26,974.18-31,148.06]47.8[45.09-50.60]201624,975.6[22,986.61-26,964.64]43.8[40.49-47.24]Post-BA or post-master's certificate200014,549.2[12,848.71-16,249.70]30.8[26.51-35.53]200415,296.8[12,378.59-18,215.08]41.8[33.69-50.35]200820,118.7[17,763.80-22,473.53]40.2[34.22-46.47]201225,536.9[22,552.50-28,521.28]35.8[30.36-41.57]201629,143.4[25,537.83-32,748.94]39.1[34.31-44.12]Not in a degree program2000††††200417,474.9[14,717.32-20,232.38]33.6[28.44-39.16]200816,491.0[13,244.03-19,738.06]32.1[24.54-40.64]201218,843.7[13,134.00-24,553.46]36.0[27.75-45.14]201629,078.5[21,351.01-36,806.07]27.2[19.49-36.49]Other20009,630.5[7,915.04-11,345.92]20.5[17.07-24.33]2004††††2008††††2012††††2016††††20002004200820122016 Amount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loans (Avg>0)(%>1)(Avg>0)(%>1)(Avg>0)(%>1)(Avg>0)(%>1)(Avg>0)(%>1)EstimatesTotal13,273.633.717,461.236.621,287.740.227,638.242.227,781.143.3Graduate degree programMaster's degree13,512.734.017,269.936.921,592.242.327,866.644.628,593.845.7Doctoral degree12,585.828.317,153.228.322,024.332.327,427.929.823,908.034.7First-professional degree13,593.051.019,196.444.921,406.345.129,061.147.824,975.643.8Post-BA or post-master's certificate14,549.230.815,296.841.820,118.740.225,536.935.829,143.439.1Not in a degree program——17,474.933.616,491.032.118,843.736.029,078.527.2Other9,630.520.5————————20002004200820122016 Amount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loans (Avg>0)(%>1)(Avg>0)(%>1)(Avg>0)(%>1)(Avg>0)(%>1)(Avg>0)(%>1)EstimatesTotal13,273.633.717,461.236.621,287.740.227,638.242.227,781.143.3Graduate degree programMaster's degree13,512.734.017,269.936.921,592.242.327,866.644.628,593.845.7Doctoral degree12,585.828.317,153.228.322,024.332.327,427.929.823,908.034.7First-professional degree13,593.051.019,196.444.921,406.345.129,061.147.824,975.643.8Post-BA or post-master's certificate14,549.230.815,296.841.820,118.740.225,536.935.829,143.439.1Not in a degree program——17,474.933.616,491.032.118,843.736.029,078.527.2Other9,630.520.5————————Standard Error (BRR)Total{|2000|{246.98|0.52{|2004|{337.10|0.81{|2008|{345.13|0.67{|2012|{429.06|0.65{|2016|{417.04|0.63Graduate degree programMaster's degree{|2000|{331.85|0.68{|2004|{503.06|1.04{|2008|{458.70|0.85{|2012|{549.26|0.89{|2016|{538.65|0.83Doctoral degree{|2000|{536.67|1.78{|2004|{501.59|1.24{|2008|{1,550.16|1.68{|2012|{674.43|0.85{|2016|{1,052.75|1.31First-professional degree{|2000|{526.31|1.89{|2004|{808.44|1.89{|2008|{704.99|1.51{|2012|{1,058.28|1.40{|2016|{1,008.63|1.71Post-BA or post-master's certificate{|2000|{850.25|2.26{|2004|{1,479.84|4.26{|2008|{1,194.15|3.12{|2012|{1,513.38|2.85{|2016|{1,828.38|2.50Not in a degree program††{|2004|{1,398.34|2.73{|2008|{1,646.56|4.11{|2012|{2,895.40|4.45{|2016|{3,918.62|4.33Other{|2000|{857.72|1.81††††††††Relative Standard Error (%)Total1.861.551.932.201.621.671.551.531.501.45Graduate degree programMaster's degree2.462.002.912.822.122.001.971.981.881.81Doctoral degree4.266.312.924.367.045.202.462.874.403.78First-professional degree3.873.714.214.213.293.343.642.934.043.91Post-BA or post-master's certificate5.847.329.6710.215.947.775.937.986.276.39Not in a degree program††8.008.129.9812.8315.3712.3713.4815.96Other8.918.86††††††††Weighted Sample Sizes (n/1,000s)Total881.12,616.91,033.92,824.31,402.73,492.01,554.43,682.21,547.93,572.9Graduate degree programMaster's degree526.01,548.6619.61,680.8951.62,250.21,111.32,491.81,118.32,448.9Doctoral degree97.5344.9109.6386.9178.3551.7145.7489.6144.6417.3First-professional degree150.8295.9157.1349.8133.1294.8184.3385.2176.4402.5Post-BA or post-master's certificate57.4186.056.1134.364.3160.076.1212.685.0217.5Not in a degree program‡‡91.5272.475.4235.237.0102.923.686.7Other49.4241.5‡‡‡‡‡‡‡‡20002004200820122016 Amount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loansAmount still owed on all undergraduate loans (Avg>0)(%>1)(Avg>0)(%>1)(Avg>0)(%>1)(Avg>0)(%>1)(Avg>0)(%>1) Amt.95% CIPct.95% CIAmt.95% CIPct.95% CIAmt.95% CIPct.95% CIAmt.95% CIPct.95% CIAmt.95% CIPct.95% CIEstimatesTotal13,273.6[12,779.66-13,767.59]33.7[32.63-34.73]17,461.2[16,796.46-18,125.98]36.6[35.03-38.21]21,287.7[20,607.09-21,968.30]40.2[38.85-41.50]27,638.2[26,792.14-28,484.35]42.2[40.94-43.49]27,781.1[26,958.74-28,603.56]43.3[42.09-44.57]Graduate degree programMaster's degree13,512.7[12,848.97-14,176.39]34.0[32.62-35.34]17,269.9[16,277.87-18,261.93]36.9[34.83-38.94]21,592.2[20,687.62-22,496.74]42.3[40.63-43.97]27,866.6[26,783.46-28,949.73]44.6[42.86-46.35]28,593.8[27,531.55-29,655.99]45.7[44.04-47.30]Doctoral degree12,585.8[11,512.50-13,659.18]28.3[24.85-31.97]17,153.2[16,164.05-18,142.32]28.3[25.96-30.84]22,024.3[18,967.39-25,081.23]32.3[29.10-35.72]27,427.9[26,097.95-28,757.88]29.8[28.10-31.46]23,908.0[21,831.96-25,984.01]34.7[32.13-37.29]First-professional degree13,593.0[12,540.37-14,645.61]51.0[47.19-54.75]19,196.4[17,602.18-20,790.66]44.9[41.20-48.65]21,406.3[20,016.09-22,796.57]45.1[42.19-48.13]29,061.1[26,974.18-31,148.06]47.8[45.09-50.60]24,975.6[22,986.61-26,964.64]43.8[40.49-47.24]Post-BA or post-master's certificate14,549.2[12,848.71-16,249.70]30.8[26.51-35.53]15,296.8[12,378.59-18,215.08]41.8[33.69-50.35]20,118.7[17,763.80-22,473.53]40.2[34.22-46.47]25,536.9[22,552.50-28,521.28]35.8[30.36-41.57]29,143.4[25,537.83-32,748.94]39.1[34.31-44.12]Not in a degree program††††17,474.9[14,717.32-20,232.38]33.6[28.44-39.16]16,491.0[13,244.03-19,738.06]32.1[24.54-40.64]18,843.7[13,134.00-24,553.46]36.0[27.75-45.14]29,078.5[21,351.01-36,806.07]27.2[19.49-36.49]Other9,630.5[7,915.04-11,345.92]20.5[17.07-24.33]††††††††††††††††— Not available.— Not available.† Not applicable.‡ Reporting standards not met.STDERR-SOURCE-END— Not available.† Not applicable.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: GRADDEG.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: OWEAMT1 and GRADDEG. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: OWEAMT1 (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and GRADDEG (NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.truebfebkam37bfebkam375Average>0 Tuition and fees paid by NPSAS institution type and NPSAS institution control for years 1996, 2000, 2004, 2008, 2012 and 2016 Tuition and fees paid(Avg>0)EstimatesTotal19964,695.020005,602.820047,361.720089,174.9201210,894.3201614,791.7NPSAS institution typePublic 4-year nondoctorate19961,504.720001,833.92004—20083,135.82012—2016—Public 4-year doctorate19963,415.320003,733.42004—20086,577.02012—2016—Private not-for-profit 4-yr nondoctorate19963,841.720004,820.62004—20086,524.22012—2016—Private not-for-profit 4-year doctorate19968,625.6200010,091.62004—200814,703.92012—2016—Private for-profit 2 years or more1996—2000—2004—20089,275.42012—2016—Private for profit19964,982.220006,247.42004—2008—2012—2016—Other1996—20001,292.62004—2008—2012—2016—NPSAS institution controlPublic19962,951.320003,287.520044,792.120086,002.020128,079.4201611,536.3Private not-for-profit19967,171.320008,861.8200410,615.7200812,933.2201214,647.9201619,048.3Private for-profit19964,982.220006,277.220046,835.720089,275.420129,270.7201611,208.0Average>0 Tuition and fees paid by NPSAS institution type and NPSAS institution control for years 1996, 2000, 2004, 2008, 2012 and 2016 Tuition and fees paid(Avg>0)EstimatesTotal19964,695.020005,602.820047,361.720089,174.9201210,894.3201614,791.7NPSAS institution typePublic 4-year nondoctorate19961,504.720001,833.92004—20083,135.82012—2016—Public 4-year doctorate19963,415.320003,733.42004—20086,577.02012—2016—Private not-for-profit 4-yr nondoctorate19963,841.720004,820.62004—20086,524.22012—2016—Private not-for-profit 4-year doctorate19968,625.6200010,091.62004—200814,703.92012—2016—Private for-profit 2 years or more1996—2000—2004—20089,275.42012—2016—Private for profit19964,982.220006,247.42004—2008—2012—2016—Other1996—20001,292.62004—2008—2012—2016—NPSAS institution controlPublic19962,951.320003,287.520044,792.120086,002.020128,079.4201611,536.3Private not-for-profit19967,171.320008,861.8200410,615.7200812,933.2201214,647.9201619,048.3Private for-profit19964,982.220006,277.220046,835.720089,275.420129,270.7201611,208.0Standard Error (BRR)Total1996{|1996|{117.65|2000{|2000|{92.37|2004{|2004|{362.89|2008{|2008|{171.08|2012{|2012|{181.23|2016{|2016|{225.30|NPSAS institution typePublic 4-year nondoctorate1996{|1996|{80.12|2000{|2000|{133.39|2004†2008{|2008|{156.52|2012†2016†Public 4-year doctorate1996{|1996|{136.78|2000{|2000|{73.84|2004†2008{|2008|{128.71|2012†2016†Private not-for-profit 4-yr nondoctorate1996{|1996|{330.52|2000{|2000|{463.89|2004†2008{|2008|{232.33|2012†2016†Private not-for-profit 4-year doctorate1996{|1996|{292.43|2000{|2000|{294.42|2004†2008{|2008|{278.14|2012†2016†Private for-profit 2 years or more1996†2000†2004†2008{|2008|{1,623.47|2012†2016†Private for profit1996{|1996|{691.37|2000{|2000|{875.16|2004†2008†2012†2016†Other1996†2000{|2000|{363.01|2004†2008†2012†2016†NPSAS institution controlPublic1996{|1996|{96.80|2000{|2000|{69.33|2004{|2004|{109.62|2008{|2008|{107.83|2012{|2012|{170.25|2016{|2016|{167.00|Private not-for-profit1996{|1996|{235.11|2000{|2000|{245.00|2004{|2004|{736.50|2008{|2008|{228.80|2012{|2012|{349.31|2016{|2016|{480.33|Private for-profit1996{|1996|{691.37|2000{|2000|{878.28|2004{|2004|{1,053.30|2008{|2008|{1,623.47|2012{|2012|{634.89|2016{|2016|{247.99|Relative Standard Error (%)Total19962.5120001.6520044.9320081.8620121.6620161.52NPSAS institution typePublic 4-year nondoctorate19965.3220007.272004†20084.992012†2016†Public 4-year doctorate19964.0120001.982004†20081.962012†2016†Private not-for-profit 4-yr nondoctorate19968.6020009.622004†20083.562012†2016†Private not-for-profit 4-year doctorate19963.3920002.922004†20081.892012†2016†Private for-profit 2 years or more1996†2000†2004†200817.502012†2016†Private for profit199613.88200014.012004†2008†2012†2016†Other1996†200028.082004†2008†2012†2016†NPSAS institution controlPublic19963.2820002.1120042.2920081.8020122.1120161.45Private not-for-profit19963.2820002.7620046.9420081.7720122.3820162.52Private for-profit199613.88200013.99200415.41200817.5020126.8520162.21Weighted Sample Sizes (n/1,000s)Total19962,682.120002,524.220042,721.820083,340.820123,533.020163,411.7NPSAS institution typePublic 4-year nondoctorate1996376.72000292.52004†2008278.42012†2016†Public 4-year doctorate19961,174.420001,124.22004†20081,387.82012†2016†Private not-for-profit 4-yr nondoctorate1996330.42000237.92004†2008302.92012†2016†Private not-for-profit 4-year doctorate1996756.62000784.32004†20081,096.42012†2016†Private for-profit 2 years or more1996‡2000‡2004†2008275.32012†2016†Private for profit199644.1200047.92004†2008‡2012†2016†Other1996‡200037.52004†2008‡2012†2016†NPSAS institution controlPublic19961,551.020001,453.520041,447.920081,666.220121,679.420161,608.3Private not-for-profit19961,087.020001,022.620041,161.520081,399.320121,438.820161,492.1Private for-profit199644.1200048.12004112.42008275.32012414.82016311.3Average>0 Tuition and fees paid by NPSAS institution type and NPSAS institution control for years 1996, 2000, 2004, 2008, 2012 and 2016 Tuition and fees paid(Avg>0)Amt.95% CIEstimatesTotal19964,695.0[4,457.55-4,932.38]20005,602.8[5,418.05-5,787.54]20047,361.7[6,646.07-8,077.31]20089,174.9[8,837.50-9,512.26]201210,894.3[10,536.89-11,251.68]201614,791.7[14,347.38-15,235.97]NPSAS institution typePublic 4-year nondoctorate19961,504.7[1,343.03-1,666.39]20001,833.9[1,567.09-2,100.66]2004—†20083,135.8[2,827.13-3,444.46]2012—†2016—†Public 4-year doctorate19963,415.3[3,139.25-3,691.31]20003,733.4[3,585.74-3,881.10]2004—†20086,577.0[6,323.22-6,830.85]2012—†2016—†Private not-for-profit 4-yr nondoctorate19963,841.7[3,174.70-4,508.69]20004,820.6[3,892.78-5,748.33]2004—†20086,524.2[6,066.06-6,982.35]2012—†2016—†Private not-for-profit 4-year doctorate19968,625.6[8,035.47-9,215.73]200010,091.6[9,502.79-10,680.46]2004—†200814,703.9[14,155.38-15,252.36]2012—†2016—†Private for-profit 2 years or more1996††2000††2004—†20089,275.4[6,073.88-12,476.84]2012—†2016—†Private for profit19964,982.2[3,587.05-6,377.44]20006,247.4[4,497.10-7,997.74]2004—†2008††2012—†2016—†Other1996††20001,292.6[566.58-2,018.63]2004—†2008††2012—†2016—†NPSAS institution controlPublic19962,951.3[2,755.95-3,146.64]20003,287.5[3,148.83-3,426.14]20044,792.1[4,575.95-5,008.29]20086,002.0[5,789.33-6,214.63]20128,079.4[7,743.67-8,415.14]201611,536.3[11,206.94-11,865.58]Private not-for-profit19967,171.3[6,696.88-7,645.79]20008,861.8[8,371.76-9,351.75]200410,615.7[9,163.30-12,068.04]200812,933.2[12,482.05-13,384.44]201214,647.9[13,959.09-15,336.76]201619,048.3[18,101.04-19,995.47]Private for-profit19964,982.2[3,587.05-6,377.44]20006,277.2[4,520.62-8,033.74]20046,835.7[4,758.59-8,912.79]20089,275.4[6,073.88-12,476.84]20129,270.7[8,018.71-10,522.73]201611,208.0[10,718.96-11,697.04]199620002004200820122016 Tuition and fees paidTuition and fees paidTuition and fees paidTuition and fees paidTuition and fees paidTuition and fees paid (Avg>0)(Avg>0)(Avg>0)(Avg>0)(Avg>0)(Avg>0)EstimatesTotal4,695.05,602.87,361.79,174.910,894.314,791.7NPSAS institution typePublic 4-year nondoctorate1,504.71,833.9—3,135.8——Public 4-year doctorate3,415.33,733.4—6,577.0——Private not-for-profit 4-yr nondoctorate3,841.74,820.6—6,524.2——Private not-for-profit 4-year doctorate8,625.610,091.6—14,703.9——Private for-profit 2 years or more———9,275.4——Private for profit4,982.26,247.4————Other—1,292.6————NPSAS institution controlPublic2,951.33,287.54,792.16,002.08,079.411,536.3Private not-for-profit7,171.38,861.810,615.712,933.214,647.919,048.3Private for-profit4,982.26,277.26,835.79,275.49,270.711,208.0199620002004200820122016 Tuition and fees paidTuition and fees paidTuition and fees paidTuition and fees paidTuition and fees paidTuition and fees paid (Avg>0)(Avg>0)(Avg>0)(Avg>0)(Avg>0)(Avg>0)EstimatesTotal4,695.05,602.87,361.79,174.910,894.314,791.7NPSAS institution typePublic 4-year nondoctorate1,504.71,833.9—3,135.8——Public 4-year doctorate3,415.33,733.4—6,577.0——Private not-for-profit 4-yr nondoctorate3,841.74,820.6—6,524.2——Private not-for-profit 4-year doctorate8,625.610,091.6—14,703.9——Private for-profit 2 years or more———9,275.4——Private for profit4,982.26,247.4————Other—1,292.6————NPSAS institution controlPublic2,951.33,287.54,792.16,002.08,079.411,536.3Private not-for-profit7,171.38,861.810,615.712,933.214,647.919,048.3Private for-profit4,982.26,277.26,835.79,275.49,270.711,208.0Standard Error (BRR)Total{|1996|{117.65|{|2000|{92.37|{|2004|{362.89|{|2008|{171.08|{|2012|{181.23|{|2016|{225.30|NPSAS institution typePublic 4-year nondoctorate{|1996|{80.12|{|2000|{133.39|†{|2008|{156.52|††Public 4-year doctorate{|1996|{136.78|{|2000|{73.84|†{|2008|{128.71|††Private not-for-profit 4-yr nondoctorate{|1996|{330.52|{|2000|{463.89|†{|2008|{232.33|††Private not-for-profit 4-year doctorate{|1996|{292.43|{|2000|{294.42|†{|2008|{278.14|††Private for-profit 2 years or more†††{|2008|{1,623.47|††Private for profit{|1996|{691.37|{|2000|{875.16|††††Other†{|2000|{363.01|††††NPSAS institution controlPublic{|1996|{96.80|{|2000|{69.33|{|2004|{109.62|{|2008|{107.83|{|2012|{170.25|{|2016|{167.00|Private not-for-profit{|1996|{235.11|{|2000|{245.00|{|2004|{736.50|{|2008|{228.80|{|2012|{349.31|{|2016|{480.33|Private for-profit{|1996|{691.37|{|2000|{878.28|{|2004|{1,053.30|{|2008|{1,623.47|{|2012|{634.89|{|2016|{247.99|Relative Standard Error (%)Total2.511.654.931.861.661.52NPSAS institution typePublic 4-year nondoctorate5.327.27†4.99††Public 4-year doctorate4.011.98†1.96††Private not-for-profit 4-yr nondoctorate8.609.62†3.56††Private not-for-profit 4-year doctorate3.392.92†1.89††Private for-profit 2 years or more†††17.50††Private for profit13.8814.01††††Other†28.08††††NPSAS institution controlPublic3.282.112.291.802.111.45Private not-for-profit3.282.766.941.772.382.52Private for-profit13.8813.9915.4117.506.852.21Weighted Sample Sizes (n/1,000s)Total2,682.12,524.22,721.83,340.83,533.03,411.7NPSAS institution typePublic 4-year nondoctorate376.7292.5†278.4††Public 4-year doctorate1,174.41,124.2†1,387.8††Private not-for-profit 4-yr nondoctorate330.4237.9†302.9††Private not-for-profit 4-year doctorate756.6784.3†1,096.4††Private for-profit 2 years or more‡‡†275.3††Private for profit44.147.9†‡††Other‡37.5†‡††NPSAS institution controlPublic1,551.01,453.51,447.91,666.21,679.41,608.3Private not-for-profit1,087.01,022.61,161.51,399.31,438.81,492.1Private for-profit44.148.1112.4275.3414.8311.3199620002004200820122016 Tuition and fees paidTuition and fees paidTuition and fees paidTuition and fees paidTuition and fees paidTuition and fees paid (Avg>0)(Avg>0)(Avg>0)(Avg>0)(Avg>0)(Avg>0) Amt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIEstimatesTotal4,695.0[4,457.55-4,932.38]5,602.8[5,418.05-5,787.54]7,361.7[6,646.07-8,077.31]9,174.9[8,837.50-9,512.26]10,894.3[10,536.89-11,251.68]14,791.7[14,347.38-15,235.97]NPSAS institution typePublic 4-year nondoctorate1,504.7[1,343.03-1,666.39]1,833.9[1,567.09-2,100.66]—†3,135.8[2,827.13-3,444.46]—†—†Public 4-year doctorate3,415.3[3,139.25-3,691.31]3,733.4[3,585.74-3,881.10]—†6,577.0[6,323.22-6,830.85]—†—†Private not-for-profit 4-yr nondoctorate3,841.7[3,174.70-4,508.69]4,820.6[3,892.78-5,748.33]—†6,524.2[6,066.06-6,982.35]—†—†Private not-for-profit 4-year doctorate8,625.6[8,035.47-9,215.73]10,091.6[9,502.79-10,680.46]—†14,703.9[14,155.38-15,252.36]—†—†Private for-profit 2 years or more††††—†9,275.4[6,073.88-12,476.84]—†—†Private for profit4,982.2[3,587.05-6,377.44]6,247.4[4,497.10-7,997.74]—†††—†—†Other††1,292.6[566.58-2,018.63]—†††—†—†NPSAS institution controlPublic2,951.3[2,755.95-3,146.64]3,287.5[3,148.83-3,426.14]4,792.1[4,575.95-5,008.29]6,002.0[5,789.33-6,214.63]8,079.4[7,743.67-8,415.14]11,536.3[11,206.94-11,865.58]Private not-for-profit7,171.3[6,696.88-7,645.79]8,861.8[8,371.76-9,351.75]10,615.7[9,163.30-12,068.04]12,933.2[12,482.05-13,384.44]14,647.9[13,959.09-15,336.76]19,048.3[18,101.04-19,995.47]Private for-profit4,982.2[3,587.05-6,377.44]6,277.2[4,520.62-8,033.74]6,835.7[4,758.59-8,912.79]9,275.4[6,073.88-12,476.84]9,270.7[8,018.71-10,522.73]11,208.0[10,718.96-11,697.04]— Not available.— Not available.† Not applicable.‡ Reporting standards not met.STDERR-SOURCE-END— Not available.† Not applicable.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: TUITION2 and SECTOR9.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: TUITION2, SECTOR9 and CONTROL. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: TUITION2 (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), SECTOR9 (NPSAS:1996, NPSAS:2000, NPSAS:2008) and CONTROL (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96), 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.truebfebka34 bfebka34 1Graduate programs by Parent's highest education level for years 2000, 2004, 2008, 2012 and 2016 Graduate programsBusiness administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)TotalEstimatesTotal200014.120.136.59.82.83.23.43.75.50.9 !100%200413.221.335.19.62.53.93.23.36.31.6 !100%200813.622.936.210.52.94.52.21.94.80.5100%201212.217.945.19.82.92.32.83.23.9#100%201611.515.349.67.72.64.02.42.92.81.1100%Parent's highest education levelHigh school or less200014.924.134.87.14.03.02.23.94.91.1 !100%200415.027.334.67.43.03.71.52.23.61.6 !!100%200813.927.735.08.64.64.50.71.42.90.6100%201210.922.646.77.83.82.91.32.21.7#100%201611.115.554.16.13.54.50.7 !1.81.51.3 !100%Some postsecondary education20009.226.538.97.32.82.92.34.35.10.8 !!100%200416.026.332.67.72.53.52.12.94.91.7100%200814.327.936.38.52.54.40.91.62.90.6100%201213.418.847.87.33.11.91.73.32.6#100%201612.218.947.66.12.64.21.32.82.71.6100%Bachelor's degree200016.417.836.411.32.83.22.24.14.90.9 !100%200411.619.038.910.02.23.62.73.86.91.4 !100%200814.818.738.312.01.94.32.82.34.40.4 !100%201213.917.444.610.31.92.12.63.53.60.1 !!100%201612.513.650.18.62.24.32.62.72.90.6100%Master's degree or higher200012.816.933.712.52.24.16.43.57.20.8 !100%200411.315.733.712.02.34.75.44.18.91.8100%200811.819.635.112.12.5 !4.63.82.27.80.5100%201210.915.042.412.22.82.34.53.66.2#100%201610.513.948.58.92.53.54.03.53.51.1 !100%Graduate programs by Parent's highest education level for years 2000, 2004, 2008, 2012 and 2016 Graduate programsBusiness administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)TotalEstimatesTotal200014.120.136.59.82.83.23.43.75.50.9 !100%200413.221.335.19.62.53.93.23.36.31.6 !100%200813.622.936.210.52.94.52.21.94.80.5100%201212.217.945.19.82.92.32.83.23.9#100%201611.515.349.67.72.64.02.42.92.81.1100%Parent's highest education levelHigh school or less200014.924.134.87.14.03.02.23.94.91.1 !100%200415.027.334.67.43.03.71.52.23.61.6 !!100%200813.927.735.08.64.64.50.71.42.90.6100%201210.922.646.77.83.82.91.32.21.7#100%201611.115.554.16.13.54.50.7 !1.81.51.3 !100%Some postsecondary education20009.226.538.97.32.82.92.34.35.10.8 !!100%200416.026.332.67.72.53.52.12.94.91.7100%200814.327.936.38.52.54.40.91.62.90.6100%201213.418.847.87.33.11.91.73.32.6#100%201612.218.947.66.12.64.21.32.82.71.6100%Bachelor's degree200016.417.836.411.32.83.22.24.14.90.9 !100%200411.619.038.910.02.23.62.73.86.91.4 !100%200814.818.738.312.01.94.32.82.34.40.4 !100%201213.917.444.610.31.92.12.63.53.60.1 !!100%201612.513.650.18.62.24.32.62.72.90.6100%Master's degree or higher200012.816.933.712.52.24.16.43.57.20.8 !100%200411.315.733.712.02.34.75.44.18.91.8100%200811.819.635.112.12.5 !4.63.82.27.80.5100%201210.915.042.412.22.82.34.53.66.2#100%201610.513.948.58.92.53.54.03.53.51.1 !100%Standard Error (BRR)Total20000.710.570.810.280.220.370.550.550.530.38 20040.810.901.190.380.190.390.390.520.440.52 20081.000.910.820.490.420.350.170.130.290.09 20120.610.650.910.280.150.140.220.300.21† 20160.440.620.900.420.200.300.150.320.300.23 Parent's highest education levelHigh school or less20000.990.951.270.370.400.410.330.800.650.44 20041.811.992.320.540.290.610.310.600.540.86 20082.162.051.630.940.771.020.180.250.440.18 20121.241.611.950.490.310.300.190.350.24† 20161.011.411.820.560.370.720.210.320.310.52 Some postsecondary education20000.991.641.880.710.440.610.600.870.980.65 20041.831.932.330.500.300.500.470.550.580.39 20082.361.661.981.200.351.030.160.260.370.18 20121.601.531.760.470.360.200.240.320.29† 20160.861.181.740.560.270.520.240.620.450.43 Bachelor's degree20001.171.151.320.830.420.450.410.610.830.39 20041.081.211.520.600.250.480.500.710.800.56 20081.641.221.570.860.230.480.410.300.370.16 20121.361.491.370.570.190.270.350.500.330.05 20161.011.041.460.600.300.600.380.350.390.09 Master's degree or higher20001.081.091.330.680.280.681.620.580.760.29 20040.971.221.620.580.280.520.650.630.710.55 20080.821.391.210.571.130.390.350.240.480.10 20120.951.081.240.500.230.210.450.510.44† 20160.670.911.260.690.290.410.370.450.530.41 Relative Standard Error (%)Total20005.052.822.222.917.9711.5216.1914.649.6341.42 20046.134.243.393.987.679.9212.2615.476.9231.63 20087.373.982.254.6314.757.827.596.655.9817.38 20124.983.642.022.855.376.098.099.355.37† 20163.864.041.815.537.717.406.2011.0210.5120.97 Parent's highest education levelHigh school or less20006.623.933.645.169.9413.9614.6520.6313.2240.88 200412.127.286.727.379.5816.4320.0526.6414.8752.06 200815.477.384.6710.9116.6522.7725.6217.2215.1329.24 201211.357.094.176.218.2510.5815.1616.0913.88† 20169.079.113.369.0810.6115.9630.3217.6020.5641.57 Some postsecondary education200010.756.214.849.7815.8120.9426.0820.0419.3478.82 200411.477.367.156.4811.9114.3622.9619.4111.8922.59 200816.525.945.4514.0814.1723.4218.6615.6212.7327.84 201211.928.123.686.3611.3210.8814.399.7111.12† 20167.086.283.659.2610.2712.4118.8121.9516.9727.48 Bachelor's degree20007.136.443.627.3615.1414.0418.5814.8216.8444.63 20049.326.343.906.0411.1113.2118.5318.7611.6840.94 200811.056.524.107.2211.9511.1614.7913.018.4239.80 20129.808.563.065.529.7413.0913.6514.389.2378.35 20168.097.642.927.0113.8214.0014.6312.8513.6316.84 Master's degree or higher20008.476.463.945.4812.6616.4225.4216.4710.6035.78 20048.597.774.824.8311.8911.2411.8615.488.0029.75 20086.927.113.444.6945.838.459.1310.756.1419.48 20128.677.192.924.118.239.119.9914.247.06† 20166.386.542.597.7311.6411.779.2412.6514.8936.54 Weighted Sample Sizes (n/1,000s)Total20002,189.3 20042,417.6 20083,096.7 20123,315.7 20163,202.4 Parent's highest education levelHigh school or less2000575.2 2004603.5 2008716.8 2012689.3 2016540.7 Some postsecondary education2000258.7 2004404.2 2008628.4 2012680.7 2016789.1 Bachelor's degree2000372.4 2004616.3 2008739.9 2012805.1 2016792.9 Master's degree or higher2000516.3 2004773.0 2008990.2 20121,108.5 20161,075.1 Graduate programs by Parent's highest education level for years 2000, 2004, 2008, 2012 and 2016 Graduate programsBusiness administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)TotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal200014.1[12.76-15.62]20.1[19.02-21.28]36.5[34.87-38.11]9.8[9.21-10.35]2.8[2.37-3.26]3.2[2.54-4.03]3.4[2.44-4.65]3.7[2.78-4.99]5.5[4.53-6.65]0.9 ![0.40-2.08]100%200413.2[11.69-14.89]21.3[19.54-23.09]35.1[32.75-37.44]9.6[8.83-10.33]2.5[2.16-2.92]3.9[3.24-4.79]3.2[2.48-4.02]3.3[2.45-4.51]6.3[5.53-7.26]1.6 ![0.87-3.04]100%200813.6[11.70-15.64]22.9[21.17-24.77]36.2[34.60-37.81]10.5[9.58-11.50]2.9[2.13-3.82]4.5[3.82-5.20]2.2[1.92-2.59]1.9[1.70-2.22]4.8[4.28-5.42]0.5[0.38-0.75]100%201212.2[11.01-13.40]17.9[16.69-19.27]45.1[43.26-46.86]9.8[9.25-10.34]2.9[2.58-3.19]2.3[2.02-2.57]2.8[2.35-3.24]3.2[2.70-3.90]3.9[3.50-4.32]#[0.01-0.07]100%201611.5[10.68-12.43]15.3[14.15-16.59]49.6[47.85-51.38]7.7[6.86-8.53]2.6[2.25-3.05]4.0[3.48-4.66]2.4[2.14-2.73]2.9[2.30-3.56]2.8[2.30-3.49]1.1[0.73-1.68]100%Parent's highest education levelHigh school or less200014.9[13.03-16.98]24.1[22.28-26.07]34.8[32.35-37.43]7.1[6.40-7.87]4.0[3.26-4.85]3.0[2.23-3.90]2.2[1.66-2.98]3.9[2.55-5.82]4.9[3.78-6.41]1.1 ![0.47-2.41]100%200415.0[11.72-18.89]27.3[23.58-31.41]34.6[30.14-39.28]7.4[6.37-8.51]3.0[2.50-3.64]3.7[2.70-5.16]1.5[1.04-2.29]2.2[1.32-3.77]3.6[2.69-4.83]1.6 !![0.59-4.53]100%200813.9[10.20-18.75]27.7[23.89-31.96]35.0[31.81-38.24]8.6[6.92-10.64]4.6[3.34-6.43]4.5[2.85-6.98]0.7[0.43-1.17]1.4[1.01-2.00]2.9[2.15-3.90]0.6[0.34-1.09]100%201210.9[8.68-13.57]22.6[19.64-25.97]46.7[42.93-50.60]7.8[6.93-8.85]3.8[3.24-4.48]2.9[2.34-3.55]1.3[0.93-1.69]2.2[1.59-2.99]1.7[1.33-2.30]#[0.00-0.03]100%201611.1[9.27-13.25]15.5[12.89-18.45]54.1[50.47-57.62]6.1[5.12-7.32]3.5[2.82-4.28]4.5[3.27-6.13]0.7 ![0.38-1.27]1.8[1.27-2.54]1.5[1.02-2.29]1.3 ![0.55-2.83]100%Some postsecondary education20009.2[7.40-11.37]26.5[23.31-29.88]38.9[35.19-42.70]7.3[5.97-8.82]2.8[2.00-3.77]2.9[1.92-4.43]2.3[1.35-3.84]4.3[2.90-6.47]5.1[3.43-7.42]0.8 !![0.17-3.92]100%200416.0[12.69-19.95]26.3[22.63-30.25]32.6[28.15-37.31]7.7[6.78-8.75]2.5[1.97-3.15]3.5[2.61-4.59]2.1[1.30-3.22]2.9[1.94-4.17]4.9[3.87-6.18]1.7[1.10-2.68]100%200814.3[10.25-19.63]27.9[24.76-31.29]36.3[32.49-40.27]8.5[6.44-11.22]2.5[1.89-3.30]4.4[2.75-6.91]0.9[0.59-1.23]1.6[1.20-2.23]2.9[2.28-3.77]0.6[0.37-1.12]100%201213.4[10.58-16.92]18.8[15.97-21.99]47.8[44.34-51.27]7.3[6.46-8.30]3.1[2.51-3.92]1.9[1.50-2.31]1.7[1.27-2.24]3.3[2.74-4.02]2.6[2.10-3.26]#[0.00-0.10]100%201612.2[10.58-13.98]18.9[16.65-21.32]47.6[44.23-51.09]6.1[5.06-7.29]2.6[2.14-3.20]4.2[3.31-5.39]1.3[0.89-1.86]2.8[1.83-4.35]2.7[1.92-3.74]1.6[0.91-2.69]100%Bachelor's degree200016.4[14.20-18.89]17.8[15.64-20.23]36.4[33.77-39.04]11.3[9.76-13.10]2.8[2.03-3.72]3.2[2.41-4.22]2.2[1.52-3.21]4.1[3.06-5.53]4.9[3.50-6.87]0.9 ![0.36-2.14]100%200411.6[9.60-13.87]19.0[16.78-21.54]38.9[35.92-41.89]10.0[8.83-11.20]2.2[1.79-2.77]3.6[2.79-4.69]2.7[1.88-3.91]3.8[2.59-5.43]6.9[5.46-8.65]1.4 ![0.60-3.03]100%200814.8[11.87-18.34]18.7[16.45-21.27]38.3[35.28-41.47]12.0[10.37-13.78]1.9[1.51-2.41]4.3[3.45-5.35]2.8[2.09-3.74]2.3[1.79-3.00]4.4[3.75-5.23]0.4 ![0.18-0.87]100%201213.9[11.43-16.82]17.4[14.66-20.54]44.6[41.94-47.32]10.3[9.27-11.52]1.9[1.57-2.31]2.1[1.62-2.71]2.6[1.97-3.37]3.5[2.62-4.61]3.6[3.00-4.32]0.1 !![0.01-0.30]100%201612.5[10.66-14.66]13.6[11.67-15.77]50.1[47.25-53.01]8.6[7.44-9.81]2.2[1.67-2.88]4.3[3.23-5.61]2.6[1.95-3.48]2.7[2.09-3.47]2.9[2.20-3.77]0.6[0.40-0.77]100%Master's degree or higher200012.8[10.77-15.11]16.9[14.79-19.16]33.7[31.07-36.37]12.5[11.16-13.90]2.2[1.74-2.88]4.1[2.96-5.70]6.4[3.81-10.49]3.5[2.51-4.85]7.2[5.79-8.85]0.8 ![0.40-1.67]100%200411.3[9.51-13.35]15.7[13.48-18.30]33.7[30.54-36.93]12.0[10.90-13.19]2.3[1.85-2.96]4.7[3.72-5.80]5.4[4.31-6.87]4.1[3.01-5.55]8.9[7.62-10.44]1.8[1.02-3.30]100%200811.8[10.26-13.49]19.6[16.97-22.46]35.1[32.80-37.56]12.1[11.05-13.30]2.5 ![0.99-6.00]4.6[3.88-5.41]3.8[3.19-4.57]2.2[1.82-2.78]7.8[6.87-8.75]0.5[0.34-0.74]100%201210.9[9.20-12.95]15.0[12.96-17.21]42.4[40.02-44.90]12.2[11.28-13.26]2.8[2.39-3.31]2.3[1.91-2.74]4.5[3.69-5.47]3.6[2.71-4.75]6.2[5.40-7.14]#[0.01-0.07]100%201610.5[9.28-11.94]13.9[12.23-15.82]48.5[46.02-50.97]8.9[7.65-10.38]2.5[1.98-3.14]3.5[2.74-4.35]4.0[3.31-4.76]3.5[2.75-4.53]3.5[2.64-4.75]1.1 ![0.54-2.28]100%20002004200820122016 Graduate programsGraduate programsGraduate programsGraduate programsGraduate programs Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)EstimatesTotal14.120.136.59.82.83.23.43.75.50.913.221.335.19.62.53.93.23.36.31.613.622.936.210.52.94.52.21.94.80.512.217.945.19.82.92.32.83.23.9#11.515.349.67.72.64.02.42.92.81.1Parent's highest education levelHigh school or less14.924.134.87.14.03.02.23.94.91.115.027.334.67.43.03.71.52.23.61.613.927.735.08.64.64.50.71.42.90.610.922.646.77.83.82.91.32.21.7#11.115.554.16.13.54.50.71.81.51.3Some postsecondary education9.226.538.97.32.82.92.34.35.10.816.026.332.67.72.53.52.12.94.91.714.327.936.38.52.54.40.91.62.90.613.418.847.87.33.11.91.73.32.6#12.218.947.66.12.64.21.32.82.71.6Bachelor's degree16.417.836.411.32.83.22.24.14.90.911.619.038.910.02.23.62.73.86.91.414.818.738.312.01.94.32.82.34.40.413.917.444.610.31.92.12.63.53.60.112.513.650.18.62.24.32.62.72.90.6Master's degree or higher12.816.933.712.52.24.16.43.57.20.811.315.733.712.02.34.75.44.18.91.811.819.635.112.12.54.63.82.27.80.510.915.042.412.22.82.34.53.66.2#10.513.948.58.92.53.54.03.53.51.120002004200820122016 Graduate programsGraduate programsGraduate programsGraduate programsGraduate programs Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)EstimatesTotal14.120.136.59.82.83.23.43.75.50.913.221.335.19.62.53.93.23.36.31.613.622.936.210.52.94.52.21.94.80.512.217.945.19.82.92.32.83.23.9#11.515.349.67.72.64.02.42.92.81.1Parent's highest education levelHigh school or less14.924.134.87.14.03.02.23.94.91.115.027.334.67.43.03.71.52.23.61.613.927.735.08.64.64.50.71.42.90.610.922.646.77.83.82.91.32.21.7#11.115.554.16.13.54.50.71.81.51.3Some postsecondary education9.226.538.97.32.82.92.34.35.10.816.026.332.67.72.53.52.12.94.91.714.327.936.38.52.54.40.91.62.90.613.418.847.87.33.11.91.73.32.6#12.218.947.66.12.64.21.32.82.71.6Bachelor's degree16.417.836.411.32.83.22.24.14.90.911.619.038.910.02.23.62.73.86.91.414.818.738.312.01.94.32.82.34.40.413.917.444.610.31.92.12.63.53.60.112.513.650.18.62.24.32.62.72.90.6Master's degree or higher12.816.933.712.52.24.16.43.57.20.811.315.733.712.02.34.75.44.18.91.811.819.635.112.12.54.63.82.27.80.510.915.042.412.22.82.34.53.66.2#10.513.948.58.92.53.54.03.53.51.1Standard Error (BRR)Total0.710.570.810.280.220.370.550.550.530.380.810.901.190.380.190.390.390.520.440.521.000.910.820.490.420.350.170.130.290.090.610.650.910.280.150.140.220.300.21†0.440.620.900.420.200.300.150.320.300.23Parent's highest education levelHigh school or less0.990.951.270.370.400.410.330.800.650.441.811.992.320.540.290.610.310.600.540.862.162.051.630.940.771.020.180.250.440.181.241.611.950.490.310.300.190.350.24†1.011.411.820.560.370.720.210.320.310.52Some postsecondary education0.991.641.880.710.440.610.600.870.980.651.831.932.330.500.300.500.470.550.580.392.361.661.981.200.351.030.160.260.370.181.601.531.760.470.360.200.240.320.29†0.861.181.740.560.270.520.240.620.450.43Bachelor's degree1.171.151.320.830.420.450.410.610.830.391.081.211.520.600.250.480.500.710.800.561.641.221.570.860.230.480.410.300.370.161.361.491.370.570.190.270.350.500.330.051.011.041.460.600.300.600.380.350.390.09Master's degree or higher1.081.091.330.680.280.681.620.580.760.290.971.221.620.580.280.520.650.630.710.550.821.391.210.571.130.390.350.240.480.100.951.081.240.500.230.210.450.510.44†0.670.911.260.690.290.410.370.450.530.41Relative Standard Error (%)Total5.052.822.222.917.9711.5216.1914.649.6341.426.134.243.393.987.679.9212.2615.476.9231.637.373.982.254.6314.757.827.596.655.9817.384.983.642.022.855.376.098.099.355.37†3.864.041.815.537.717.406.2011.0210.5120.97Parent's highest education levelHigh school or less6.623.933.645.169.9413.9614.6520.6313.2240.8812.127.286.727.379.5816.4320.0526.6414.8752.0615.477.384.6710.9116.6522.7725.6217.2215.1329.2411.357.094.176.218.2510.5815.1616.0913.88†9.079.113.369.0810.6115.9630.3217.6020.5641.57Some postsecondary education10.756.214.849.7815.8120.9426.0820.0419.3478.8211.477.367.156.4811.9114.3622.9619.4111.8922.5916.525.945.4514.0814.1723.4218.6615.6212.7327.8411.928.123.686.3611.3210.8814.399.7111.12†7.086.283.659.2610.2712.4118.8121.9516.9727.48Bachelor's degree7.136.443.627.3615.1414.0418.5814.8216.8444.639.326.343.906.0411.1113.2118.5318.7611.6840.9411.056.524.107.2211.9511.1614.7913.018.4239.809.808.563.065.529.7413.0913.6514.389.2378.358.097.642.927.0113.8214.0014.6312.8513.6316.84Master's degree or higher8.476.463.945.4812.6616.4225.4216.4710.6035.788.597.774.824.8311.8911.2411.8615.488.0029.756.927.113.444.6945.838.459.1310.756.1419.488.677.192.924.118.239.119.9914.247.06†6.386.542.597.7311.6411.779.2412.6514.8936.54Weighted Sample Sizes (n/1,000s)Total2,189.3 2,417.6 3,096.7 3,315.7 3,202.4 Parent's highest education levelHigh school or less575.2 603.5 716.8 689.3 540.7 Some postsecondary education258.7 404.2 628.4 680.7 789.1 Bachelor's degree372.4 616.3 739.9 805.1 792.9 Master's degree or higher516.3 773.0 990.2 1,108.5 1,075.1 20002004200820122016 Graduate programsGraduate programsGraduate programsGraduate programsGraduate programs Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD)Business administration (MBA)Education (any master's)MA, MS, or other master'sPhD except in educationEducation (any doctorate)Other doctoral degreeMedicine (MD)Other health science degreeLaw (LLB or JD)Theology (MDiv, MHL, BD) Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal14.1[12.76-15.62]20.1[19.02-21.28]36.5[34.87-38.11]9.8[9.21-10.35]2.8[2.37-3.26]3.2[2.54-4.03]3.4[2.44-4.65]3.7[2.78-4.99]5.5[4.53-6.65]0.9 ![0.40-2.08]13.2[11.69-14.89]21.3[19.54-23.09]35.1[32.75-37.44]9.6[8.83-10.33]2.5[2.16-2.92]3.9[3.24-4.79]3.2[2.48-4.02]3.3[2.45-4.51]6.3[5.53-7.26]1.6 ![0.87-3.04]13.6[11.70-15.64]22.9[21.17-24.77]36.2[34.60-37.81]10.5[9.58-11.50]2.9[2.13-3.82]4.5[3.82-5.20]2.2[1.92-2.59]1.9[1.70-2.22]4.8[4.28-5.42]0.5[0.38-0.75]12.2[11.01-13.40]17.9[16.69-19.27]45.1[43.26-46.86]9.8[9.25-10.34]2.9[2.58-3.19]2.3[2.02-2.57]2.8[2.35-3.24]3.2[2.70-3.90]3.9[3.50-4.32]#[0.01-0.07]11.5[10.68-12.43]15.3[14.15-16.59]49.6[47.85-51.38]7.7[6.86-8.53]2.6[2.25-3.05]4.0[3.48-4.66]2.4[2.14-2.73]2.9[2.30-3.56]2.8[2.30-3.49]1.1[0.73-1.68]Parent's highest education levelHigh school or less14.9[13.03-16.98]24.1[22.28-26.07]34.8[32.35-37.43]7.1[6.40-7.87]4.0[3.26-4.85]3.0[2.23-3.90]2.2[1.66-2.98]3.9[2.55-5.82]4.9[3.78-6.41]1.1 ![0.47-2.41]15.0[11.72-18.89]27.3[23.58-31.41]34.6[30.14-39.28]7.4[6.37-8.51]3.0[2.50-3.64]3.7[2.70-5.16]1.5[1.04-2.29]2.2[1.32-3.77]3.6[2.69-4.83]1.6 !![0.59-4.53]13.9[10.20-18.75]27.7[23.89-31.96]35.0[31.81-38.24]8.6[6.92-10.64]4.6[3.34-6.43]4.5[2.85-6.98]0.7[0.43-1.17]1.4[1.01-2.00]2.9[2.15-3.90]0.6[0.34-1.09]10.9[8.68-13.57]22.6[19.64-25.97]46.7[42.93-50.60]7.8[6.93-8.85]3.8[3.24-4.48]2.9[2.34-3.55]1.3[0.93-1.69]2.2[1.59-2.99]1.7[1.33-2.30]#[0.00-0.03]11.1[9.27-13.25]15.5[12.89-18.45]54.1[50.47-57.62]6.1[5.12-7.32]3.5[2.82-4.28]4.5[3.27-6.13]0.7 ![0.38-1.27]1.8[1.27-2.54]1.5[1.02-2.29]1.3 ![0.55-2.83]Some postsecondary education9.2[7.40-11.37]26.5[23.31-29.88]38.9[35.19-42.70]7.3[5.97-8.82]2.8[2.00-3.77]2.9[1.92-4.43]2.3[1.35-3.84]4.3[2.90-6.47]5.1[3.43-7.42]0.8 !![0.17-3.92]16.0[12.69-19.95]26.3[22.63-30.25]32.6[28.15-37.31]7.7[6.78-8.75]2.5[1.97-3.15]3.5[2.61-4.59]2.1[1.30-3.22]2.9[1.94-4.17]4.9[3.87-6.18]1.7[1.10-2.68]14.3[10.25-19.63]27.9[24.76-31.29]36.3[32.49-40.27]8.5[6.44-11.22]2.5[1.89-3.30]4.4[2.75-6.91]0.9[0.59-1.23]1.6[1.20-2.23]2.9[2.28-3.77]0.6[0.37-1.12]13.4[10.58-16.92]18.8[15.97-21.99]47.8[44.34-51.27]7.3[6.46-8.30]3.1[2.51-3.92]1.9[1.50-2.31]1.7[1.27-2.24]3.3[2.74-4.02]2.6[2.10-3.26]#[0.00-0.10]12.2[10.58-13.98]18.9[16.65-21.32]47.6[44.23-51.09]6.1[5.06-7.29]2.6[2.14-3.20]4.2[3.31-5.39]1.3[0.89-1.86]2.8[1.83-4.35]2.7[1.92-3.74]1.6[0.91-2.69]Bachelor's degree16.4[14.20-18.89]17.8[15.64-20.23]36.4[33.77-39.04]11.3[9.76-13.10]2.8[2.03-3.72]3.2[2.41-4.22]2.2[1.52-3.21]4.1[3.06-5.53]4.9[3.50-6.87]0.9 ![0.36-2.14]11.6[9.60-13.87]19.0[16.78-21.54]38.9[35.92-41.89]10.0[8.83-11.20]2.2[1.79-2.77]3.6[2.79-4.69]2.7[1.88-3.91]3.8[2.59-5.43]6.9[5.46-8.65]1.4 ![0.60-3.03]14.8[11.87-18.34]18.7[16.45-21.27]38.3[35.28-41.47]12.0[10.37-13.78]1.9[1.51-2.41]4.3[3.45-5.35]2.8[2.09-3.74]2.3[1.79-3.00]4.4[3.75-5.23]0.4 ![0.18-0.87]13.9[11.43-16.82]17.4[14.66-20.54]44.6[41.94-47.32]10.3[9.27-11.52]1.9[1.57-2.31]2.1[1.62-2.71]2.6[1.97-3.37]3.5[2.62-4.61]3.6[3.00-4.32]0.1 !![0.01-0.30]12.5[10.66-14.66]13.6[11.67-15.77]50.1[47.25-53.01]8.6[7.44-9.81]2.2[1.67-2.88]4.3[3.23-5.61]2.6[1.95-3.48]2.7[2.09-3.47]2.9[2.20-3.77]0.6[0.40-0.77]Master's degree or higher12.8[10.77-15.11]16.9[14.79-19.16]33.7[31.07-36.37]12.5[11.16-13.90]2.2[1.74-2.88]4.1[2.96-5.70]6.4[3.81-10.49]3.5[2.51-4.85]7.2[5.79-8.85]0.8 ![0.40-1.67]11.3[9.51-13.35]15.7[13.48-18.30]33.7[30.54-36.93]12.0[10.90-13.19]2.3[1.85-2.96]4.7[3.72-5.80]5.4[4.31-6.87]4.1[3.01-5.55]8.9[7.62-10.44]1.8[1.02-3.30]11.8[10.26-13.49]19.6[16.97-22.46]35.1[32.80-37.56]12.1[11.05-13.30]2.5 ![0.99-6.00]4.6[3.88-5.41]3.8[3.19-4.57]2.2[1.82-2.78]7.8[6.87-8.75]0.5[0.34-0.74]10.9[9.20-12.95]15.0[12.96-17.21]42.4[40.02-44.90]12.2[11.28-13.26]2.8[2.39-3.31]2.3[1.91-2.74]4.5[3.69-5.47]3.6[2.71-4.75]6.2[5.40-7.14]#[0.01-0.07]10.5[9.28-11.94]13.9[12.23-15.82]48.5[46.02-50.97]8.9[7.65-10.38]2.5[1.98-3.14]3.5[2.74-4.35]4.0[3.31-4.76]3.5[2.75-4.53]3.5[2.64-4.75]1.1 ![0.54-2.28]# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: GRADPGM and PAREDUC.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: GRADPGM and PAREDUC. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: GRADPGM2 (NPSAS:2000), NPARED (NPSAS:2000), GRADPGM (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and PAREDUC (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebkp73bfebkp732Graduate degree program by Gender, Attendance pattern and Institution control (with multiple) for years 1996, 2000, 2004, 2008, 2012 and 2016 Graduate degree programMaster's degreeDoctoral degreeFirst-professional degreeTotalEstimatesTotal199670.215.414.4100%200070.715.813.5100%200469.516.014.5100%200872.717.89.5100%201274.014.511.4100%201674.912.812.3100%GenderMale199663.019.417.6100%200065.418.116.6100%200464.018.217.8100%200868.520.311.2100%201269.817.312.9100%201673.014.312.6100%Female199676.911.711.3100%200075.013.911.1100%200473.814.311.9100%200875.616.18.4100%201276.912.610.5100%201676.311.712.1100%Attendance patternFull-time/full year199649.419.531.2100%200050.122.627.3100%200445.222.532.3100%200853.525.221.3100%201257.419.123.5100%201658.216.225.5100%Part-time or part year199682.213.04.8100%200083.011.75.3100%200482.612.54.9100%200883.413.73.0100%201285.211.53.3100%201685.310.64.1100%Institution control (with multiple)Public199672.117.610.3100%200070.818.710.4100%200469.819.011.3100%200871.420.18.4100%201271.818.69.6100%201675.313.211.5100%Private, not-for-profit199666.712.720.6100%200068.513.118.4100%200466.413.719.9100%200873.613.412.9100%201272.310.916.8100%201675.210.114.7100%Private, for profit199681.518.5 !!#100%200099.01.0 !!#100%200495.14.9 !!#100%200871.928.1 !#100%201286.112.51.4100%201669.623.47.0100%More than one institution199681.57.4 !11.1100%200083.49.76.9100%200479.212.18.7100%200878.515.26.4100%201279.511.29.3100%201679.411.98.7100%Graduate degree program by Gender, Attendance pattern and Institution control (with multiple) for years 1996, 2000, 2004, 2008, 2012 and 2016 Graduate degree programMaster's degreeDoctoral degreeFirst-professional degreeTotalEstimatesTotal199670.215.414.4100%200070.715.813.5100%200469.516.014.5100%200872.717.89.5100%201274.014.511.4100%201674.912.812.3100%GenderMale199663.019.417.6100%200065.418.116.6100%200464.018.217.8100%200868.520.311.2100%201269.817.312.9100%201673.014.312.6100%Female199676.911.711.3100%200075.013.911.1100%200473.814.311.9100%200875.616.18.4100%201276.912.610.5100%201676.311.712.1100%Attendance patternFull-time/full year199649.419.531.2100%200050.122.627.3100%200445.222.532.3100%200853.525.221.3100%201257.419.123.5100%201658.216.225.5100%Part-time or part year199682.213.04.8100%200083.011.75.3100%200482.612.54.9100%200883.413.73.0100%201285.211.53.3100%201685.310.64.1100%Institution control (with multiple)Public199672.117.610.3100%200070.818.710.4100%200469.819.011.3100%200871.420.18.4100%201271.818.69.6100%201675.313.211.5100%Private, not-for-profit199666.712.720.6100%200068.513.118.4100%200466.413.719.9100%200873.613.412.9100%201272.310.916.8100%201675.210.114.7100%Private, for profit199681.518.5 !!#100%200099.01.0 !!#100%200495.14.9 !!#100%200871.928.1 !#100%201286.112.51.4100%201669.623.47.0100%More than one institution199681.57.4 !11.1100%200083.49.76.9100%200479.212.18.7100%200878.515.26.4100%201279.511.29.3100%201679.411.98.7100%Standard Error (BRR)Total19960.950.970.21 20000.580.510.66 20040.760.820.42 20080.810.760.35 20120.490.340.35 20160.780.670.48 GenderMale19961.471.460.74 20000.940.710.89 20041.240.910.82 20081.070.880.55 20120.910.630.60 20160.960.800.66 Female19961.221.160.59 20000.640.600.77 20040.760.890.64 20081.121.070.42 20120.630.400.42 20160.890.750.58 Attendance patternFull-time/full year19961.691.511.15 20001.310.811.20 20041.861.290.95 20081.171.100.75 20120.770.560.69 20161.270.871.10 Part-time or part year19961.441.260.51 20000.590.530.61 20041.380.940.76 20081.281.150.45 20120.580.470.32 20160.800.710.39 Institution control (with multiple)Public19961.771.411.24 20000.630.540.45 20040.970.930.64 20080.840.740.38 20120.630.450.38 20160.990.900.50 Private, not-for-profit19961.681.491.77 20001.160.931.47 20041.640.981.21 20080.910.610.76 20120.880.520.69 20160.960.710.93 Private, for profit199613.8913.89† 20000.780.78† 20045.485.48† 20088.788.78† 20121.731.590.22 20162.031.520.74 More than one institution19962.852.592.12 20001.791.341.13 20041.991.551.88 20084.294.111.01 20121.891.251.32 20162.972.411.76 Relative Standard Error (%)Total19961.356.271.46 20000.823.224.89 20041.095.132.89 20081.124.243.71 20120.672.323.02 20161.045.223.90 GenderMale19962.347.554.20 20001.443.955.36 20041.945.034.62 20081.564.344.91 20121.313.644.64 20161.315.595.19 Female19961.599.865.18 20000.854.316.95 20041.026.255.35 20081.496.675.05 20120.823.143.97 20161.166.444.78 Attendance patternFull-time/full year19963.437.783.69 20002.623.594.40 20044.115.742.95 20082.184.343.55 20121.342.912.94 20162.185.374.29 Part-time or part year19961.759.6510.71 20000.714.5011.47 20041.687.4915.57 20081.538.4115.29 20120.684.139.54 20160.946.739.50 Institution control (with multiple)Public19962.457.9912.05 20000.892.914.29 20041.404.895.69 20081.173.694.53 20120.882.433.92 20161.326.794.37 Private, not-for-profit19962.5111.718.60 20001.697.107.96 20042.477.146.07 20081.234.565.85 20121.224.814.14 20161.277.016.30 Private, for profit199617.0475.24† 20000.7879.83† 20045.77112.38† 200812.2231.21† 20122.0112.7715.98 20162.926.4910.66 More than one institution19963.4935.1619.06 20002.1513.8316.48 20042.5112.8121.73 20085.4727.0515.83 20122.3711.1614.14 20163.7520.1520.23 Weighted Sample Sizes (n/1,000s)Total19962,222.4 20002,189.3 20042,417.6 20083,096.7 20123,366.7 20163,268.7 GenderMale19961,080.3 2000966.8 20041,056.5 20081,278.1 20121,368.2 20161,357.1 Female19961,142.1 20001,222.5 20041,361.0 20081,818.6 20121,998.5 20161,911.5 Attendance patternFull-time/full year1996794.0 2000815.9 2004843.1 20081,110.5 20121,354.1 20161,252.5 Part-time or part year19961,397.9 20001,373.5 20041,574.5 20081,986.3 20122,012.6 20162,016.2 Institution control (with multiple)Public19961,217.8 20001,155.4 20041,226.4 20081,447.0 20121,514.5 20161,452.0 Private, not-for-profit1996926.7 2000923.7 20041,030.4 20081,275.5 20121,320.9 20161,376.0 Private, for profit199643.5 200035.5 200485.9 2008248.9 2012401.3 2016296.6 More than one institution199634.4 200074.8 200474.8 2008125.4 2012130.1 2016144.1 Graduate degree program by Gender, Attendance pattern and Institution control (with multiple) for years 1996, 2000, 2004, 2008, 2012 and 2016 Graduate degree programMaster's degreeDoctoral degreeFirst-professional degreeTotalPct.95% CIPct.95% CIPct.95% CI EstimatesTotal199670.2[68.22-72.05]15.4[13.59-17.50]14.4[13.97-14.82]100%200070.7[69.55-71.89]15.8[14.76-16.79]13.5[12.24-14.89]100%200469.5[68.01-71.00]16.0[14.45-17.69]14.5[13.67-15.31]100%200872.7[71.03-74.24]17.8[16.38-19.36]9.5[8.85-10.24]100%201274.0[73.03-74.98]14.5[13.89-15.22]11.4[10.78-12.14]100%201674.9[73.35-76.43]12.8[11.51-14.14]12.3[11.40-13.29]100%GenderMale199663.0[60.00-65.94]19.4[16.58-22.48]17.6[16.18-19.17]100%200065.4[63.45-67.22]18.1[16.70-19.56]16.6[14.86-18.41]100%200464.0[61.53-66.44]18.2[16.45-20.05]17.8[16.23-19.47]100%200868.5[66.38-70.60]20.3[18.63-22.11]11.2[10.13-12.29]100%201269.8[67.96-71.56]17.3[16.12-18.60]12.9[11.75-14.11]100%201673.0[71.11-74.88]14.3[12.82-15.98]12.6[11.40-13.99]100%Female199676.9[74.38-79.30]11.7[9.60-14.29]11.3[10.19-12.56]100%200075.0[73.68-76.24]13.9[12.75-15.15]11.1[9.66-12.75]100%200473.8[72.28-75.26]14.3[12.64-16.17]11.9[10.69-13.20]100%200875.6[73.29-77.72]16.1[14.06-18.29]8.4[7.57-9.24]100%201276.9[75.64-78.12]12.6[11.88-13.44]10.5[9.67-11.30]100%201676.3[74.46-77.96]11.7[10.26-13.22]12.1[10.99-13.27]100%Attendance patternFull-time/full year199649.4[45.98-52.80]19.5[16.58-22.70]31.2[28.88-33.52]100%200050.1[47.50-52.75]22.6[20.98-24.22]27.3[24.99-29.79]100%200445.2[41.54-48.86]22.5[20.04-25.13]32.3[30.49-34.25]100%200853.5[51.19-55.80]25.2[23.13-27.45]21.3[19.81-22.79]100%201257.4[55.88-58.92]19.1[18.04-20.23]23.5[22.14-24.87]100%201658.2[55.69-60.70]16.2[14.59-18.03]25.5[23.44-27.77]100%Part-time or part year199682.2[79.16-84.96]13.0[10.67-15.75]4.8[3.82-5.89]100%200083.0[81.77-84.13]11.7[10.70-12.80]5.3[4.22-6.67]100%200482.6[79.67-85.13]12.5[10.80-14.51]4.9[3.60-6.64]100%200883.4[80.70-85.74]13.7[11.56-16.10]3.0[2.18-3.99]100%201285.2[84.01-86.30]11.5[10.57-12.43]3.3[2.77-4.03]100%201685.3[83.64-86.82]10.6[9.28-12.10]4.1[3.39-4.93]100%Institution control (with multiple)Public199672.1[68.39-75.51]17.6[14.98-20.68]10.3[8.02-13.04]100%200070.8[69.56-72.07]18.7[17.65-19.83]10.4[9.59-11.38]100%200469.8[67.83-71.67]19.0[17.19-20.85]11.3[10.06-12.59]100%200871.4[69.75-73.05]20.1[18.72-21.65]8.4[7.70-9.20]100%201271.8[70.55-73.03]18.6[17.68-19.47]9.6[8.91-10.40]100%201675.3[73.26-77.17]13.2[11.56-15.11]11.5[10.54-12.52]100%Private, not-for-profit199666.7[63.22-69.98]12.7[10.00-16.03]20.6[17.25-24.41]100%200068.5[66.13-70.77]13.1[11.35-15.07]18.4[15.65-21.52]100%200466.4[63.07-69.53]13.7[11.88-15.75]19.9[17.64-22.41]100%200873.6[71.82-75.39]13.4[12.26-14.67]12.9[11.51-14.50]100%201272.3[70.55-74.02]10.9[9.90-11.97]16.8[15.46-18.20]100%201675.2[73.29-77.07]10.1[8.75-11.54]14.7[12.97-16.63]100%Private, for profit199681.5[40.69-96.60]18.5 !![3.40-59.31]##100%200099.0[95.31-99.80]1.0 !![0.20-4.69]##100%200495.1[65.48-99.50]4.9 !![0.50-34.52]##100%200871.9[52.03-85.74]28.1 ![14.26-47.97]##100%201286.1[82.35-89.21]12.5[9.67-15.99]1.4[1.00-1.88]100%201669.6[65.46-73.47]23.4[20.55-26.54]7.0[5.64-8.59]100%More than one institution199681.5[75.06-86.56]7.4 ![3.57-14.62]11.1[7.52-16.19]100%200083.4[79.54-86.73]9.7[7.31-12.70]6.9[4.93-9.52]100%200479.2[75.05-82.88]12.1[9.37-15.51]8.7[5.60-13.16]100%200878.5[68.82-85.75]15.2[8.71-25.12]6.4[4.64-8.66]100%201279.5[75.55-83.00]11.2[8.94-13.88]9.3[7.01-12.24]100%201679.4[72.88-84.61]11.9[7.95-17.56]8.7[5.80-12.86]100%199620002004200820122016 Graduate degree programGraduate degree programGraduate degree programGraduate degree programGraduate degree programGraduate degree program Master's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeEstimatesTotal70.215.414.470.715.813.569.516.014.572.717.89.574.014.511.474.912.812.3GenderMale63.019.417.665.418.116.664.018.217.868.520.311.269.817.312.973.014.312.6Female76.911.711.375.013.911.173.814.311.975.616.18.476.912.610.576.311.712.1Attendance patternFull-time/full year49.419.531.250.122.627.345.222.532.353.525.221.357.419.123.558.216.225.5Part-time or part year82.213.04.883.011.75.382.612.54.983.413.73.085.211.53.385.310.64.1Institution control (with multiple)Public72.117.610.370.818.710.469.819.011.371.420.18.471.818.69.675.313.211.5Private, not-for-profit66.712.720.668.513.118.466.413.719.973.613.412.972.310.916.875.210.114.7Private, for profit81.518.5#99.01.0#95.14.9#71.928.1#86.112.51.469.623.47.0More than one institution81.57.411.183.49.76.979.212.18.778.515.26.479.511.29.379.411.98.7199620002004200820122016 Graduate degree programGraduate degree programGraduate degree programGraduate degree programGraduate degree programGraduate degree program Master's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeEstimatesTotal70.215.414.470.715.813.569.516.014.572.717.89.574.014.511.474.912.812.3GenderMale63.019.417.665.418.116.664.018.217.868.520.311.269.817.312.973.014.312.6Female76.911.711.375.013.911.173.814.311.975.616.18.476.912.610.576.311.712.1Attendance patternFull-time/full year49.419.531.250.122.627.345.222.532.353.525.221.357.419.123.558.216.225.5Part-time or part year82.213.04.883.011.75.382.612.54.983.413.73.085.211.53.385.310.64.1Institution control (with multiple)Public72.117.610.370.818.710.469.819.011.371.420.18.471.818.69.675.313.211.5Private, not-for-profit66.712.720.668.513.118.466.413.719.973.613.412.972.310.916.875.210.114.7Private, for profit81.518.5#99.01.0#95.14.9#71.928.1#86.112.51.469.623.47.0More than one institution81.57.411.183.49.76.979.212.18.778.515.26.479.511.29.379.411.98.7Standard Error (BRR)Total0.950.970.210.580.510.660.760.820.420.810.760.350.490.340.350.780.670.48GenderMale1.471.460.740.940.710.891.240.910.821.070.880.550.910.630.600.960.800.66Female1.221.160.590.640.600.770.760.890.641.121.070.420.630.400.420.890.750.58Attendance patternFull-time/full year1.691.511.151.310.811.201.861.290.951.171.100.750.770.560.691.270.871.10Part-time or part year1.441.260.510.590.530.611.380.940.761.281.150.450.580.470.320.800.710.39Institution control (with multiple)Public1.771.411.240.630.540.450.970.930.640.840.740.380.630.450.380.990.900.50Private, not-for-profit1.681.491.771.160.931.471.640.981.210.910.610.760.880.520.690.960.710.93Private, for profit13.8913.89†0.780.78†5.485.48†8.788.78†1.731.590.222.031.520.74More than one institution2.852.592.121.791.341.131.991.551.884.294.111.011.891.251.322.972.411.76Relative Standard Error (%)Total1.356.271.460.823.224.891.095.132.891.124.243.710.672.323.021.045.223.90GenderMale2.347.554.201.443.955.361.945.034.621.564.344.911.313.644.641.315.595.19Female1.599.865.180.854.316.951.026.255.351.496.675.050.823.143.971.166.444.78Attendance patternFull-time/full year3.437.783.692.623.594.404.115.742.952.184.343.551.342.912.942.185.374.29Part-time or part year1.759.6510.710.714.5011.471.687.4915.571.538.4115.290.684.139.540.946.739.50Institution control (with multiple)Public2.457.9912.050.892.914.291.404.895.691.173.694.530.882.433.921.326.794.37Private, not-for-profit2.5111.718.601.697.107.962.477.146.071.234.565.851.224.814.141.277.016.30Private, for profit17.0475.24†0.7879.83†5.77112.38†12.2231.21†2.0112.7715.982.926.4910.66More than one institution3.4935.1619.062.1513.8316.482.5112.8121.735.4727.0515.832.3711.1614.143.7520.1520.23Weighted Sample Sizes (n/1,000s)Total2,222.4 2,189.3 2,417.6 3,096.7 3,366.7 3,268.7 GenderMale1,080.3 966.8 1,056.5 1,278.1 1,368.2 1,357.1 Female1,142.1 1,222.5 1,361.0 1,818.6 1,998.5 1,911.5 Attendance patternFull-time/full year794.0 815.9 843.1 1,110.5 1,354.1 1,252.5 Part-time or part year1,397.9 1,373.5 1,574.5 1,986.3 2,012.6 2,016.2 Institution control (with multiple)Public1,217.8 1,155.4 1,226.4 1,447.0 1,514.5 1,452.0 Private, not-for-profit926.7 923.7 1,030.4 1,275.5 1,320.9 1,376.0 Private, for profit43.5 35.5 85.9 248.9 401.3 296.6 More than one institution34.4 74.8 74.8 125.4 130.1 144.1 199620002004200820122016 Graduate degree programGraduate degree programGraduate degree programGraduate degree programGraduate degree programGraduate degree program Master's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degreeMaster's degreeDoctoral degreeFirst-professional degree Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal70.2[68.22-72.05]15.4[13.59-17.50]14.4[13.97-14.82]70.7[69.55-71.89]15.8[14.76-16.79]13.5[12.24-14.89]69.5[68.01-71.00]16.0[14.45-17.69]14.5[13.67-15.31]72.7[71.03-74.24]17.8[16.38-19.36]9.5[8.85-10.24]74.0[73.03-74.98]14.5[13.89-15.22]11.4[10.78-12.14]74.9[73.35-76.43]12.8[11.51-14.14]12.3[11.40-13.29]GenderMale63.0[60.00-65.94]19.4[16.58-22.48]17.6[16.18-19.17]65.4[63.45-67.22]18.1[16.70-19.56]16.6[14.86-18.41]64.0[61.53-66.44]18.2[16.45-20.05]17.8[16.23-19.47]68.5[66.38-70.60]20.3[18.63-22.11]11.2[10.13-12.29]69.8[67.96-71.56]17.3[16.12-18.60]12.9[11.75-14.11]73.0[71.11-74.88]14.3[12.82-15.98]12.6[11.40-13.99]Female76.9[74.38-79.30]11.7[9.60-14.29]11.3[10.19-12.56]75.0[73.68-76.24]13.9[12.75-15.15]11.1[9.66-12.75]73.8[72.28-75.26]14.3[12.64-16.17]11.9[10.69-13.20]75.6[73.29-77.72]16.1[14.06-18.29]8.4[7.57-9.24]76.9[75.64-78.12]12.6[11.88-13.44]10.5[9.67-11.30]76.3[74.46-77.96]11.7[10.26-13.22]12.1[10.99-13.27]Attendance patternFull-time/full year49.4[45.98-52.80]19.5[16.58-22.70]31.2[28.88-33.52]50.1[47.50-52.75]22.6[20.98-24.22]27.3[24.99-29.79]45.2[41.54-48.86]22.5[20.04-25.13]32.3[30.49-34.25]53.5[51.19-55.80]25.2[23.13-27.45]21.3[19.81-22.79]57.4[55.88-58.92]19.1[18.04-20.23]23.5[22.14-24.87]58.2[55.69-60.70]16.2[14.59-18.03]25.5[23.44-27.77]Part-time or part year82.2[79.16-84.96]13.0[10.67-15.75]4.8[3.82-5.89]83.0[81.77-84.13]11.7[10.70-12.80]5.3[4.22-6.67]82.6[79.67-85.13]12.5[10.80-14.51]4.9[3.60-6.64]83.4[80.70-85.74]13.7[11.56-16.10]3.0[2.18-3.99]85.2[84.01-86.30]11.5[10.57-12.43]3.3[2.77-4.03]85.3[83.64-86.82]10.6[9.28-12.10]4.1[3.39-4.93]Institution control (with multiple)Public72.1[68.39-75.51]17.6[14.98-20.68]10.3[8.02-13.04]70.8[69.56-72.07]18.7[17.65-19.83]10.4[9.59-11.38]69.8[67.83-71.67]19.0[17.19-20.85]11.3[10.06-12.59]71.4[69.75-73.05]20.1[18.72-21.65]8.4[7.70-9.20]71.8[70.55-73.03]18.6[17.68-19.47]9.6[8.91-10.40]75.3[73.26-77.17]13.2[11.56-15.11]11.5[10.54-12.52]Private, not-for-profit66.7[63.22-69.98]12.7[10.00-16.03]20.6[17.25-24.41]68.5[66.13-70.77]13.1[11.35-15.07]18.4[15.65-21.52]66.4[63.07-69.53]13.7[11.88-15.75]19.9[17.64-22.41]73.6[71.82-75.39]13.4[12.26-14.67]12.9[11.51-14.50]72.3[70.55-74.02]10.9[9.90-11.97]16.8[15.46-18.20]75.2[73.29-77.07]10.1[8.75-11.54]14.7[12.97-16.63]Private, for profit81.5[40.69-96.60]18.5 !![3.40-59.31]##99.0[95.31-99.80]1.0 !![0.20-4.69]##95.1[65.48-99.50]4.9 !![0.50-34.52]##71.9[52.03-85.74]28.1 ![14.26-47.97]##86.1[82.35-89.21]12.5[9.67-15.99]1.4[1.00-1.88]69.6[65.46-73.47]23.4[20.55-26.54]7.0[5.64-8.59]More than one institution81.5[75.06-86.56]7.4 ![3.57-14.62]11.1[7.52-16.19]83.4[79.54-86.73]9.7[7.31-12.70]6.9[4.93-9.52]79.2[75.05-82.88]12.1[9.37-15.51]8.7[5.60-13.16]78.5[68.82-85.75]15.2[8.71-25.12]6.4[4.64-8.66]79.5[75.55-83.00]11.2[8.94-13.88]9.3[7.01-12.24]79.4[72.88-84.61]11.9[7.95-17.56]8.7[5.80-12.86]# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: GRADDEG and ATTNSTAT.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: GRADDEG, GENDER, ATTNSTAT and AIDCTRL. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: GRADDEG (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), GENDER (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), ATTNSTAT (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and AIDCTRL (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96), 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebkp95bfebkp953Employer aid (includes college staff) by Graduate degree program and Graduate class level for years 1996, 2000, 2004, 2008, 2012 and 2016 Employer aid (includes college staff)$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000TotalEstimatesTotal199693.25.41.00.4100%200089.77.42.01.0100%200484.911.33.70.2100%200881.811.14.13.0100%201287.56.23.03.3100%201688.16.93.31.7100%Graduate degree programMaster's degree199690.97.31.20.6100%200086.19.82.81.3100%200481.113.84.90.2 !100%200877.514.15.13.3100%201287.36.93.42.4100%201686.57.63.92.1100%Doctoral degree199695.13.31.2 !0.4 !!100%200093.34.21.31.1100%200489.38.12.20.3 !100%200887.94.82.64.7100%201280.36.23.410.1100%201690.54.93.21.4100%First-professional degree199698.21.2 !0.3 !!0.3 !100%200096.81.80.9 !0.5 !100%200494.23.62.1#100%200893.64.61.20.5 !100%201295.42.30.81.6100%201696.02.21.0 !0.8 !100%Post-BA or post-master's certificate1996————100%200094.84.40.7 !0.1 !!100%200491.87.80.4 !!#100%200886.011.02.3 !0.8 !!100%201290.06.81.6 !1.7 !100%201687.010.22.20.5 !!100%Not in a degree program1996————100%2000————100%200486.511.71.70.1 !!100%200891.45.62.8 !0.2 !!100%201292.13.63.5 !!0.9 !!100%201688.29.82.0 !#100%Other199695.73.70.5 !!0.2 !!100%200095.14.9##100%2004————100%2008————100%2012————100%2016————100%Graduate class levelFirst year199692.86.00.90.3 !100%200086.810.12.11.1100%200485.910.23.70.2 !100%200880.711.94.03.5100%201288.86.02.72.5100%201687.97.53.11.5100%Second year199693.15.21.1 !0.6 !100%200083.99.94.22.0100%200482.612.25.00.2 !100%200880.311.94.43.3100%201287.85.53.43.3100%201688.16.13.72.1100%Third year199693.74.41.5 !0.5 !!100%200088.38.22.31.2100%200484.112.73.2#100%200881.311.54.72.5100%201285.56.83.54.2100%201687.36.93.42.4100%Fourth year or higher199694.34.50.8 !!0.3 !!100%200088.19.11.71.0100%200487.410.22.3#100%200884.69.33.52.5100%201283.58.62.45.4100%201690.45.53.11.1100%Not in a degree program1996————100%2000————100%200486.511.71.70.1 !!100%200891.45.62.8 !0.2 !!100%201292.13.63.5 !!0.9 !!100%201688.29.82.0 !#100%Employer aid (includes college staff) by Graduate degree program and Graduate class level for years 1996, 2000, 2004, 2008, 2012 and 2016 Employer aid (includes college staff)$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000TotalEstimatesTotal199693.25.41.00.4100%200089.77.42.01.0100%200484.911.33.70.2100%200881.811.14.13.0100%201287.56.23.03.3100%201688.16.93.31.7100%Graduate degree programMaster's degree199690.97.31.20.6100%200086.19.82.81.3100%200481.113.84.90.2 !100%200877.514.15.13.3100%201287.36.93.42.4100%201686.57.63.92.1100%Doctoral degree199695.13.31.2 !0.4 !!100%200093.34.21.31.1100%200489.38.12.20.3 !100%200887.94.82.64.7100%201280.36.23.410.1100%201690.54.93.21.4100%First-professional degree199698.21.2 !0.3 !!0.3 !100%200096.81.80.9 !0.5 !100%200494.23.62.1#100%200893.64.61.20.5 !100%201295.42.30.81.6100%201696.02.21.0 !0.8 !100%Post-BA or post-master's certificate1996————100%200094.84.40.7 !0.1 !!100%200491.87.80.4 !!#100%200886.011.02.3 !0.8 !!100%201290.06.81.6 !1.7 !100%201687.010.22.20.5 !!100%Not in a degree program1996————100%2000————100%200486.511.71.70.1 !!100%200891.45.62.8 !0.2 !!100%201292.13.63.5 !!0.9 !!100%201688.29.82.0 !#100%Other199695.73.70.5 !!0.2 !!100%200095.14.9##100%2004————100%2008————100%2012————100%2016————100%Graduate class levelFirst year199692.86.00.90.3 !100%200086.810.12.11.1100%200485.910.23.70.2 !100%200880.711.94.03.5100%201288.86.02.72.5100%201687.97.53.11.5100%Second year199693.15.21.1 !0.6 !100%200083.99.94.22.0100%200482.612.25.00.2 !100%200880.311.94.43.3100%201287.85.53.43.3100%201688.16.13.72.1100%Third year199693.74.41.5 !0.5 !!100%200088.38.22.31.2100%200484.112.73.2#100%200881.311.54.72.5100%201285.56.83.54.2100%201687.36.93.42.4100%Fourth year or higher199694.34.50.8 !!0.3 !!100%200088.19.11.71.0100%200487.410.22.3#100%200884.69.33.52.5100%201283.58.62.45.4100%201690.45.53.11.1100%Not in a degree program1996————100%2000————100%200486.511.71.70.1 !!100%200891.45.62.8 !0.2 !!100%201292.13.63.5 !!0.9 !!100%201688.29.82.0 !#100%Standard Error (BRR)Total19960.480.440.180.10 20000.400.340.210.12 20040.750.590.390.04 20080.690.560.370.34 20120.520.340.320.21 20160.440.370.200.16 Graduate degree programMaster's degree19960.810.760.250.16 20000.550.470.300.18 20040.940.740.560.06 20081.000.820.540.51 20120.710.440.440.28 20160.560.460.280.23 Doctoral degree19960.870.910.520.29 20000.530.430.280.12 20040.800.690.260.13 20080.890.480.350.92 20120.850.470.320.58 20160.930.590.700.24 First-professional degree19960.570.560.190.14 20000.590.470.300.22 20040.900.550.62† 20080.760.690.310.18 20120.570.450.220.31 20160.770.620.360.33 Post-BA or post-master's certificate1996†††† 20000.940.920.250.14 20041.731.710.20† 20082.332.080.800.47 20121.841.640.710.82 20162.232.040.660.29 Not in a degree program1996†††† 2000†††† 20042.011.960.440.07 20081.461.130.850.10 20123.351.033.060.92 20162.862.640.82† Other19960.760.770.280.15 20001.121.12†† 2004†††† 2008†††† 2012†††† 2016†††† Graduate class levelFirst year19960.630.590.210.15 20000.850.670.360.29 20041.140.910.630.10 20081.541.180.850.73 20120.900.660.440.33 20160.760.610.330.23 Second year19960.830.850.370.26 20000.970.750.530.39 20041.220.990.710.07 20080.800.710.400.38 20120.910.580.540.41 20160.710.530.370.35 Third year19961.130.960.530.23 20001.371.220.580.31 20041.561.440.65† 20081.211.080.570.36 20121.190.990.720.55 20161.100.950.540.52 Fourth year or higher19961.091.110.530.26 20000.910.950.390.26 20041.281.140.47† 20081.040.790.470.45 20121.000.940.430.57 20161.040.850.640.24 Not in a degree program1996†††† 2000†††† 20042.011.960.440.07 20081.461.130.850.10 20123.351.033.060.92 20162.862.640.82† Relative Standard Error (%)Total19960.528.2717.9123.64 20000.454.6610.8811.82 20040.885.2110.6026.96 20080.845.078.9811.33 20120.595.4210.446.30 20160.495.366.009.56 Graduate degree programMaster's degree19960.8910.5020.5028.36 20000.644.7610.7313.78 20041.155.3111.4437.67 20081.305.8210.7115.27 20120.816.3913.0211.51 20160.656.097.3111.19 Doctoral degree19960.9227.6441.9781.63 20000.5710.0320.8710.41 20040.898.5211.6538.35 20081.029.9613.4719.74 20121.067.519.415.79 20161.0212.0922.1616.69 First-professional degree19960.5847.7556.8444.44 20000.6125.9133.7248.72 20040.9515.1328.97† 20080.8114.8825.6335.01 20120.6019.2629.0020.04 20160.8128.2234.8639.79 Post-BA or post-master's certificate1996†††† 20000.9921.0233.86102.01 20041.8921.8753.49† 20082.7119.0135.3459.83 20122.0524.0245.8749.12 20162.5619.9629.8155.84 Not in a degree program1996†††† 2000†††† 20042.3216.8025.8568.59 20081.6020.1430.1853.77 20123.6428.9887.04106.31 20163.2427.0241.06† Other19960.7920.9458.2183.86 20001.1822.68†† 2004†††† 2008†††† 2012†††† 2016†††† Graduate class levelFirst year19960.689.7924.5942.96 20000.976.6717.3526.95 20041.338.9217.2741.33 20081.919.9521.4321.24 20121.0110.9516.1713.24 20160.878.1110.5714.89 Second year19960.8916.1935.0244.13 20001.157.6612.3819.62 20041.488.0914.1343.51 20081.005.949.0211.50 20121.0310.4316.1812.37 20160.808.559.8716.59 Third year19961.2021.9035.7850.92 20001.5514.8624.7726.51 20041.8611.3020.12† 20081.499.4312.1314.10 20121.3914.6420.2412.98 20161.2613.7615.9022.04 Fourth year or higher19961.1524.4762.9286.48 20001.0310.3422.8725.08 20041.4611.1420.52† 20081.238.4613.4317.86 20121.1910.8617.7210.44 20161.1515.5820.4322.62 Not in a degree program1996†††† 2000†††† 20042.3216.8025.8568.59 20081.6020.1430.1853.77 20123.6428.9887.04106.31 20163.2427.0241.06† Weighted Sample Sizes (n/1,000s)Total19962,762.8 20002,616.9 20042,824.3 20083,492.0 20123,682.2 20163,572.9 Graduate degree programMaster's degree19961,559.5 20001,548.6 20041,680.8 20082,250.2 20122,491.8 20162,448.9 Doctoral degree1996343.3 2000344.9 2004386.9 2008551.7 2012489.6 2016417.3 First-professional degree1996319.7 2000295.9 2004349.8 2008294.8 2012385.2 2016402.5 Post-BA or post-master's certificate1996‡ 2000186.0 2004134.3 2008160.0 2012212.6 2016217.5 Not in a degree program1996‡ 2000‡ 2004272.4 2008235.2 2012102.9 201686.7 Other1996540.3 2000241.5 2004‡ 2008‡ 2012‡ 2016‡ Graduate class levelFirst year19961,357.4 2000653.5 2004934.9 20081,335.4 20121,462.8 20161,610.4 Second year1996681.3 2000523.9 2004869.3 20081,056.3 20121,192.8 20161,192.2 Third year1996369.3 2000261.7 2004397.9 2008450.6 2012482.8 2016375.9 Fourth year or higher1996329.5 2000311.1 2004349.8 2008414.5 2012440.9 2016307.5 Not in a degree program1996‡ 2000‡ 2004272.4 2008235.2 2012102.9 201686.7 Employer aid (includes college staff) by Graduate degree program and Graduate class level for years 1996, 2000, 2004, 2008, 2012 and 2016 Employer aid (includes college staff)$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000TotalPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal199693.2[92.17-94.13]5.4[4.54-6.33]1.0[0.68-1.41]0.4[0.27-0.70]100%200089.7[88.88-90.49]7.4[6.70-8.07]2.0[1.57-2.43]1.0[0.77-1.23]100%200484.9[83.36-86.31]11.3[10.19-12.52]3.7[2.96-4.50]0.2[0.09-0.26]100%200881.8[80.44-83.16]11.1[10.05-12.28]4.1[3.41-4.85]3.0[2.38-3.71]100%201287.5[86.43-88.48]6.2[5.57-6.90]3.0[2.47-3.72]3.3[2.89-3.70]100%201688.1[87.21-88.93]6.9[6.17-7.62]3.3[2.95-3.73]1.7[1.42-2.08]100%Graduate degree programMaster's degree199690.9[89.15-92.43]7.3[5.87-8.96]1.2[0.82-1.87]0.6[0.32-1.02]100%200086.1[84.98-87.18]9.8[8.94-10.81]2.8[2.22-3.41]1.3[0.98-1.71]100%200481.1[79.21-82.90]13.8[12.46-15.36]4.9[3.88-6.09]0.2 ![0.07-0.33]100%200877.5[75.46-79.42]14.1[12.56-15.80]5.1[4.10-6.25]3.3[2.46-4.49]100%201287.3[85.84-88.62]6.9[6.04-7.77]3.4[2.64-4.41]2.4[1.94-3.06]100%201686.5[85.34-87.56]7.6[6.70-8.52]3.9[3.34-4.46]2.1[1.67-2.60]100%Doctoral degree199695.1[93.02-96.60]3.3[1.88-5.73]1.2 ![0.53-2.87]0.4 !![0.07-1.81]100%200093.3[92.16-94.29]4.2[3.47-5.19]1.3[0.88-2.03]1.1[0.90-1.37]100%200489.3[87.64-90.78]8.1[6.85-9.59]2.2[1.78-2.81]0.3 ![0.16-0.71]100%200887.9[86.06-89.59]4.8[3.95-5.85]2.6[1.99-3.38]4.7[3.14-6.84]100%201280.3[78.54-81.88]6.2[5.38-7.23]3.4[2.84-4.11]10.1[8.99-11.29]100%201690.5[88.53-92.20]4.9[3.84-6.19]3.2[2.05-4.90]1.4[1.02-1.98]100%First-professional degree199698.2[96.61-99.05]1.2 ![0.44-3.02]0.3 !![0.10-1.03]0.3 ![0.12-0.75]100%200096.8[95.41-97.84]1.8[1.09-3.07]0.9 ![0.45-1.71]0.5 ![0.17-1.19]100%200494.2[92.15-95.72]3.6[2.69-4.89]2.1[1.21-3.79]#[0.01-0.14]100%200893.6[91.97-94.98]4.6[3.44-6.17]1.2[0.74-2.02]0.5 ![0.26-1.04]100%201295.4[94.10-96.38]2.3[1.59-3.40]0.8[0.42-1.33]1.6[1.04-2.30]100%201696.0[94.12-97.23]2.2[1.26-3.82]1.0 ![0.51-2.03]0.8 ![0.37-1.80]100%Post-BA or post-master's certificate1996—†—†—†—†100%200094.8[92.55-96.36]4.4[2.86-6.61]0.7 ![0.37-1.43]0.1 !![0.02-1.03]100%200491.8[87.66-94.62]7.8[5.05-11.95]0.4 !![0.13-1.08]##100%200886.0[80.76-90.01]11.0[7.47-15.78]2.3 ![1.12-4.50]0.8 !![0.24-2.51]100%201290.0[85.71-93.06]6.8[4.21-10.83]1.6 ![0.63-3.82]1.7 ![0.63-4.33]100%201687.0[81.97-90.85]10.2[6.85-15.03]2.2[1.22-3.95]0.5 !![0.17-1.54]100%Not in a degree program1996—†—†—†—†100%2000—†—†—†—†100%200486.5[82.07-90.03]11.7[8.33-16.13]1.7[1.02-2.81]0.1 !![0.02-0.37]100%200891.4[88.04-93.86]5.6[3.76-8.31]2.8 ![1.54-5.06]0.2 !![0.07-0.56]100%201292.1[82.43-96.62]3.6[2.00-6.27]3.5 !![0.61-17.76]0.9 !![0.11-6.73]100%201688.2[81.33-92.80]9.8[5.66-16.35]2.0 ![0.89-4.46]##100%Other199695.7[93.86-96.97]3.7[2.40-5.59]0.5 !![0.15-1.54]0.2 !![0.03-0.94]100%200095.1[92.26-96.87]4.9[3.13-7.74]####100%2004—†—†—†—†100%2008—†—†—†—†100%2012—†—†—†—†100%2016—†—†—†—†100%Graduate class levelFirst year199692.8[91.43-93.99]6.0[4.90-7.28]0.9[0.52-1.41]0.3 ![0.14-0.82]100%200086.8[84.98-88.37]10.1[8.81-11.50]2.1[1.46-2.93]1.1[0.63-1.86]100%200485.9[83.53-88.03]10.2[8.51-12.09]3.7[2.60-5.13]0.2 ![0.11-0.56]100%200880.7[77.45-83.52]11.9[9.76-14.44]4.0[2.59-6.04]3.5[2.27-5.24]100%201288.8[86.89-90.43]6.0[4.84-7.45]2.7[1.99-3.77]2.5[1.90-3.19]100%201687.9[86.29-89.30]7.5[6.38-8.79]3.1[2.52-3.82]1.5[1.13-2.04]100%Second year199693.1[91.26-94.62]5.2[3.76-7.22]1.1 ![0.52-2.15]0.6 ![0.24-1.42]100%200083.9[81.88-85.75]9.9[8.44-11.47]4.2[3.31-5.44]2.0[1.34-2.94]100%200482.6[80.08-84.91]12.2[10.38-14.27]5.0[3.79-6.61]0.2 ![0.07-0.40]100%200880.3[78.71-81.87]11.9[10.59-13.38]4.4[3.69-5.26]3.3[2.66-4.19]100%201287.8[85.88-89.47]5.5[4.51-6.80]3.4[2.44-4.62]3.3[2.58-4.21]100%201688.1[86.58-89.38]6.1[5.19-7.26]3.7[3.06-4.51]2.1[1.50-2.89]100%Third year199693.7[90.97-95.60]4.4[2.82-6.81]1.5 ![0.72-3.03]0.5 !![0.16-1.26]100%200088.3[85.27-90.75]8.2[6.08-11.01]2.3[1.42-3.81]1.2[0.69-1.99]100%200484.1[80.72-86.90]12.7[10.14-15.83]3.2[2.15-4.75]#[0.01-0.10]100%200881.3[78.76-83.54]11.5[9.50-13.78]4.7[3.72-6.00]2.5[1.92-3.35]100%201285.5[82.99-87.69]6.8[5.05-9.00]3.5[2.37-5.25]4.2[3.25-5.42]100%201687.3[85.00-89.35]6.9[5.24-9.01]3.4[2.49-4.67]2.4[1.52-3.63]100%Fourth year or higher199694.3[91.68-96.15]4.5[2.76-7.40]0.8 !![0.24-2.96]0.3 !![0.05-1.69]100%200088.1[86.17-89.82]9.1[7.42-11.21]1.7[1.08-2.70]1.0[0.63-1.71]100%200487.4[84.69-89.74]10.2[8.20-12.72]2.3[1.52-3.41]#[0.01-0.19]100%200884.6[82.49-86.59]9.3[7.87-10.99]3.5[2.70-4.58]2.5[1.77-3.58]100%201283.5[81.47-85.41]8.6[6.94-10.65]2.4[1.71-3.43]5.4[4.41-6.66]100%201690.4[88.12-92.23]5.5[4.01-7.41]3.1[2.08-4.65]1.1[0.67-1.64]100%Not in a degree program1996—†—†—†—†100%2000—†—†—†—†100%200486.5[82.07-90.03]11.7[8.33-16.13]1.7[1.02-2.81]0.1 !![0.02-0.37]100%200891.4[88.04-93.86]5.6[3.76-8.31]2.8 ![1.54-5.06]0.2 !![0.07-0.56]100%201292.1[82.43-96.62]3.6[2.00-6.27]3.5 !![0.61-17.76]0.9 !![0.11-6.73]100%201688.2[81.33-92.80]9.8[5.66-16.35]2.0 ![0.89-4.46]##100%199620002004200820122016 Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff) $1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000EstimatesTotal93.25.41.00.489.77.42.01.084.911.33.70.281.811.14.13.087.56.23.03.388.16.93.31.7Graduate degree programMaster's degree90.97.31.20.686.19.82.81.381.113.84.90.277.514.15.13.387.36.93.42.486.57.63.92.1Doctoral degree95.13.31.20.493.34.21.31.189.38.12.20.387.94.82.64.780.36.23.410.190.54.93.21.4First-professional degree98.21.20.30.396.81.80.90.594.23.62.1#93.64.61.20.595.42.30.81.696.02.21.00.8Post-BA or post-master's certificate————94.84.40.70.191.87.80.4#86.011.02.30.890.06.81.61.787.010.22.20.5Not in a degree program————————86.511.71.70.191.45.62.80.292.13.63.50.988.29.82.0#Other95.73.70.50.295.14.9##————————————————Graduate class levelFirst year92.86.00.90.386.810.12.11.185.910.23.70.280.711.94.03.588.86.02.72.587.97.53.11.5Second year93.15.21.10.683.99.94.22.082.612.25.00.280.311.94.43.387.85.53.43.388.16.13.72.1Third year93.74.41.50.588.38.22.31.284.112.73.2#81.311.54.72.585.56.83.54.287.36.93.42.4Fourth year or higher94.34.50.80.388.19.11.71.087.410.22.3#84.69.33.52.583.58.62.45.490.45.53.11.1Not in a degree program————————86.511.71.70.191.45.62.80.292.13.63.50.988.29.82.0#199620002004200820122016 Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff) $1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000EstimatesTotal93.25.41.00.489.77.42.01.084.911.33.70.281.811.14.13.087.56.23.03.388.16.93.31.7Graduate degree programMaster's degree90.97.31.20.686.19.82.81.381.113.84.90.277.514.15.13.387.36.93.42.486.57.63.92.1Doctoral degree95.13.31.20.493.34.21.31.189.38.12.20.387.94.82.64.780.36.23.410.190.54.93.21.4First-professional degree98.21.20.30.396.81.80.90.594.23.62.1#93.64.61.20.595.42.30.81.696.02.21.00.8Post-BA or post-master's certificate————94.84.40.70.191.87.80.4#86.011.02.30.890.06.81.61.787.010.22.20.5Not in a degree program————————86.511.71.70.191.45.62.80.292.13.63.50.988.29.82.0#Other95.73.70.50.295.14.9##————————————————Graduate class levelFirst year92.86.00.90.386.810.12.11.185.910.23.70.280.711.94.03.588.86.02.72.587.97.53.11.5Second year93.15.21.10.683.99.94.22.082.612.25.00.280.311.94.43.387.85.53.43.388.16.13.72.1Third year93.74.41.50.588.38.22.31.284.112.73.2#81.311.54.72.585.56.83.54.287.36.93.42.4Fourth year or higher94.34.50.80.388.19.11.71.087.410.22.3#84.69.33.52.583.58.62.45.490.45.53.11.1Not in a degree program————————86.511.71.70.191.45.62.80.292.13.63.50.988.29.82.0#Standard Error (BRR)Total0.480.440.180.100.400.340.210.120.750.590.390.040.690.560.370.340.520.340.320.210.440.370.200.16Graduate degree programMaster's degree0.810.760.250.160.550.470.300.180.940.740.560.061.000.820.540.510.710.440.440.280.560.460.280.23Doctoral degree0.870.910.520.290.530.430.280.120.800.690.260.130.890.480.350.920.850.470.320.580.930.590.700.24First-professional degree0.570.560.190.140.590.470.300.220.900.550.62†0.760.690.310.180.570.450.220.310.770.620.360.33Post-BA or post-master's certificate††††0.940.920.250.141.731.710.20†2.332.080.800.471.841.640.710.822.232.040.660.29Not in a degree program††††††††2.011.960.440.071.461.130.850.103.351.033.060.922.862.640.82†Other0.760.770.280.151.121.12††††††††††††††††††Graduate class levelFirst year0.630.590.210.150.850.670.360.291.140.910.630.101.541.180.850.730.900.660.440.330.760.610.330.23Second year0.830.850.370.260.970.750.530.391.220.990.710.070.800.710.400.380.910.580.540.410.710.530.370.35Third year1.130.960.530.231.371.220.580.311.561.440.65†1.211.080.570.361.190.990.720.551.100.950.540.52Fourth year or higher1.091.110.530.260.910.950.390.261.281.140.47†1.040.790.470.451.000.940.430.571.040.850.640.24Not in a degree program††††††††2.011.960.440.071.461.130.850.103.351.033.060.922.862.640.82†Relative Standard Error (%)Total0.528.2717.9123.640.454.6610.8811.820.885.2110.6026.960.845.078.9811.330.595.4210.446.300.495.366.009.56Graduate degree programMaster's degree0.8910.5020.5028.360.644.7610.7313.781.155.3111.4437.671.305.8210.7115.270.816.3913.0211.510.656.097.3111.19Doctoral degree0.9227.6441.9781.630.5710.0320.8710.410.898.5211.6538.351.029.9613.4719.741.067.519.415.791.0212.0922.1616.69First-professional degree0.5847.7556.8444.440.6125.9133.7248.720.9515.1328.97†0.8114.8825.6335.010.6019.2629.0020.040.8128.2234.8639.79Post-BA or post-master's certificate††††0.9921.0233.86102.011.8921.8753.49†2.7119.0135.3459.832.0524.0245.8749.122.5619.9629.8155.84Not in a degree program††††††††2.3216.8025.8568.591.6020.1430.1853.773.6428.9887.04106.313.2427.0241.06†Other0.7920.9458.2183.861.1822.68††††††††††††††††††Graduate class levelFirst year0.689.7924.5942.960.976.6717.3526.951.338.9217.2741.331.919.9521.4321.241.0110.9516.1713.240.878.1110.5714.89Second year0.8916.1935.0244.131.157.6612.3819.621.488.0914.1343.511.005.949.0211.501.0310.4316.1812.370.808.559.8716.59Third year1.2021.9035.7850.921.5514.8624.7726.511.8611.3020.12†1.499.4312.1314.101.3914.6420.2412.981.2613.7615.9022.04Fourth year or higher1.1524.4762.9286.481.0310.3422.8725.081.4611.1420.52†1.238.4613.4317.861.1910.8617.7210.441.1515.5820.4322.62Not in a degree program††††††††2.3216.8025.8568.591.6020.1430.1853.773.6428.9887.04106.313.2427.0241.06†Weighted Sample Sizes (n/1,000s)Total2,762.8 2,616.9 2,824.3 3,492.0 3,682.2 3,572.9 Graduate degree programMaster's degree1,559.5 1,548.6 1,680.8 2,250.2 2,491.8 2,448.9 Doctoral degree343.3 344.9 386.9 551.7 489.6 417.3 First-professional degree319.7 295.9 349.8 294.8 385.2 402.5 Post-BA or post-master's certificate‡ 186.0 134.3 160.0 212.6 217.5 Not in a degree program‡ ‡ 272.4 235.2 102.9 86.7 Other540.3 241.5 ‡ ‡ ‡ ‡ Graduate class levelFirst year1,357.4 653.5 934.9 1,335.4 1,462.8 1,610.4 Second year681.3 523.9 869.3 1,056.3 1,192.8 1,192.2 Third year369.3 261.7 397.9 450.6 482.8 375.9 Fourth year or higher329.5 311.1 349.8 414.5 440.9 307.5 Not in a degree program‡ ‡ 272.4 235.2 102.9 86.7 199620002004200820122016 Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff)Employer aid (includes college staff) $1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000$1,000 or less$1,001 to $5,000$5,001 to $10,000More than $10,000 Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal93.2[92.17-94.13]5.4[4.54-6.33]1.0[0.68-1.41]0.4[0.27-0.70]89.7[88.88-90.49]7.4[6.70-8.07]2.0[1.57-2.43]1.0[0.77-1.23]84.9[83.36-86.31]11.3[10.19-12.52]3.7[2.96-4.50]0.2[0.09-0.26]81.8[80.44-83.16]11.1[10.05-12.28]4.1[3.41-4.85]3.0[2.38-3.71]87.5[86.43-88.48]6.2[5.57-6.90]3.0[2.47-3.72]3.3[2.89-3.70]88.1[87.21-88.93]6.9[6.17-7.62]3.3[2.95-3.73]1.7[1.42-2.08]Graduate degree programMaster's degree90.9[89.15-92.43]7.3[5.87-8.96]1.2[0.82-1.87]0.6[0.32-1.02]86.1[84.98-87.18]9.8[8.94-10.81]2.8[2.22-3.41]1.3[0.98-1.71]81.1[79.21-82.90]13.8[12.46-15.36]4.9[3.88-6.09]0.2 ![0.07-0.33]77.5[75.46-79.42]14.1[12.56-15.80]5.1[4.10-6.25]3.3[2.46-4.49]87.3[85.84-88.62]6.9[6.04-7.77]3.4[2.64-4.41]2.4[1.94-3.06]86.5[85.34-87.56]7.6[6.70-8.52]3.9[3.34-4.46]2.1[1.67-2.60]Doctoral degree95.1[93.02-96.60]3.3[1.88-5.73]1.2 ![0.53-2.87]0.4 !![0.07-1.81]93.3[92.16-94.29]4.2[3.47-5.19]1.3[0.88-2.03]1.1[0.90-1.37]89.3[87.64-90.78]8.1[6.85-9.59]2.2[1.78-2.81]0.3 ![0.16-0.71]87.9[86.06-89.59]4.8[3.95-5.85]2.6[1.99-3.38]4.7[3.14-6.84]80.3[78.54-81.88]6.2[5.38-7.23]3.4[2.84-4.11]10.1[8.99-11.29]90.5[88.53-92.20]4.9[3.84-6.19]3.2[2.05-4.90]1.4[1.02-1.98]First-professional degree98.2[96.61-99.05]1.2 ![0.44-3.02]0.3 !![0.10-1.03]0.3 ![0.12-0.75]96.8[95.41-97.84]1.8[1.09-3.07]0.9 ![0.45-1.71]0.5 ![0.17-1.19]94.2[92.15-95.72]3.6[2.69-4.89]2.1[1.21-3.79]#[0.01-0.14]93.6[91.97-94.98]4.6[3.44-6.17]1.2[0.74-2.02]0.5 ![0.26-1.04]95.4[94.10-96.38]2.3[1.59-3.40]0.8[0.42-1.33]1.6[1.04-2.30]96.0[94.12-97.23]2.2[1.26-3.82]1.0 ![0.51-2.03]0.8 ![0.37-1.80]Post-BA or post-master's certificate—†—†—†—†94.8[92.55-96.36]4.4[2.86-6.61]0.7 ![0.37-1.43]0.1 !![0.02-1.03]91.8[87.66-94.62]7.8[5.05-11.95]0.4 !![0.13-1.08]##86.0[80.76-90.01]11.0[7.47-15.78]2.3 ![1.12-4.50]0.8 !![0.24-2.51]90.0[85.71-93.06]6.8[4.21-10.83]1.6 ![0.63-3.82]1.7 ![0.63-4.33]87.0[81.97-90.85]10.2[6.85-15.03]2.2[1.22-3.95]0.5 !![0.17-1.54]Not in a degree program—†—†—†—†—†—†—†—†86.5[82.07-90.03]11.7[8.33-16.13]1.7[1.02-2.81]0.1 !![0.02-0.37]91.4[88.04-93.86]5.6[3.76-8.31]2.8 ![1.54-5.06]0.2 !![0.07-0.56]92.1[82.43-96.62]3.6[2.00-6.27]3.5 !![0.61-17.76]0.9 !![0.11-6.73]88.2[81.33-92.80]9.8[5.66-16.35]2.0 ![0.89-4.46]##Other95.7[93.86-96.97]3.7[2.40-5.59]0.5 !![0.15-1.54]0.2 !![0.03-0.94]95.1[92.26-96.87]4.9[3.13-7.74]####—†—†—†—†—†—†—†—†—†—†—†—†—†—†—†—†Graduate class levelFirst year92.8[91.43-93.99]6.0[4.90-7.28]0.9[0.52-1.41]0.3 ![0.14-0.82]86.8[84.98-88.37]10.1[8.81-11.50]2.1[1.46-2.93]1.1[0.63-1.86]85.9[83.53-88.03]10.2[8.51-12.09]3.7[2.60-5.13]0.2 ![0.11-0.56]80.7[77.45-83.52]11.9[9.76-14.44]4.0[2.59-6.04]3.5[2.27-5.24]88.8[86.89-90.43]6.0[4.84-7.45]2.7[1.99-3.77]2.5[1.90-3.19]87.9[86.29-89.30]7.5[6.38-8.79]3.1[2.52-3.82]1.5[1.13-2.04]Second year93.1[91.26-94.62]5.2[3.76-7.22]1.1 ![0.52-2.15]0.6 ![0.24-1.42]83.9[81.88-85.75]9.9[8.44-11.47]4.2[3.31-5.44]2.0[1.34-2.94]82.6[80.08-84.91]12.2[10.38-14.27]5.0[3.79-6.61]0.2 ![0.07-0.40]80.3[78.71-81.87]11.9[10.59-13.38]4.4[3.69-5.26]3.3[2.66-4.19]87.8[85.88-89.47]5.5[4.51-6.80]3.4[2.44-4.62]3.3[2.58-4.21]88.1[86.58-89.38]6.1[5.19-7.26]3.7[3.06-4.51]2.1[1.50-2.89]Third year93.7[90.97-95.60]4.4[2.82-6.81]1.5 ![0.72-3.03]0.5 !![0.16-1.26]88.3[85.27-90.75]8.2[6.08-11.01]2.3[1.42-3.81]1.2[0.69-1.99]84.1[80.72-86.90]12.7[10.14-15.83]3.2[2.15-4.75]#[0.01-0.10]81.3[78.76-83.54]11.5[9.50-13.78]4.7[3.72-6.00]2.5[1.92-3.35]85.5[82.99-87.69]6.8[5.05-9.00]3.5[2.37-5.25]4.2[3.25-5.42]87.3[85.00-89.35]6.9[5.24-9.01]3.4[2.49-4.67]2.4[1.52-3.63]Fourth year or higher94.3[91.68-96.15]4.5[2.76-7.40]0.8 !![0.24-2.96]0.3 !![0.05-1.69]88.1[86.17-89.82]9.1[7.42-11.21]1.7[1.08-2.70]1.0[0.63-1.71]87.4[84.69-89.74]10.2[8.20-12.72]2.3[1.52-3.41]#[0.01-0.19]84.6[82.49-86.59]9.3[7.87-10.99]3.5[2.70-4.58]2.5[1.77-3.58]83.5[81.47-85.41]8.6[6.94-10.65]2.4[1.71-3.43]5.4[4.41-6.66]90.4[88.12-92.23]5.5[4.01-7.41]3.1[2.08-4.65]1.1[0.67-1.64]Not in a degree program—†—†—†—†—†—†—†—†86.5[82.07-90.03]11.7[8.33-16.13]1.7[1.02-2.81]0.1 !![0.02-0.37]91.4[88.04-93.86]5.6[3.76-8.31]2.8 ![1.54-5.06]0.2 !![0.07-0.56]92.1[82.43-96.62]3.6[2.00-6.27]3.5 !![0.61-17.76]0.9 !![0.11-6.73]88.2[81.33-92.80]9.8[5.66-16.35]2.0 ![0.89-4.46]##— Not available.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.— Not available.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.STDERR-SOURCE-END— Not available.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: EMPLYAMT, GRADDEG and GRADLVL.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: EMPLYAMT, GRADDEG and GRADLVL. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: EMPLYAMT (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), GRADDEG (NPSAS:1996, NPSAS:2000, NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), GRADLEV2 (NPSAS:1996, NPSAS:2000) and GRADLVL (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 1995-96 National Postsecondary Student Aid Study (NPSAS:96), 1999-2000 National Postsecondary Student Aid Study (NPSAS:2000), 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebkpefbfebkpef4Years between BA and graduate school by Graduate and first professional degree programs for years 2004, 2008, 2012 and 2016 Years between BA and graduate school1-15 months16-25 months26-39 months40 or more monthsTotalEstimatesTotal200486.49.83.60.1 !100%200886.19.74.00.2 !100%201288.08.13.9#100%201687.39.03.6#100%Graduate and first professional degree programsMaster of Science (MS)200488.28.63.2#100%200888.19.52.30.1 !!100%201289.97.13.0#100%201691.36.72.0#100%Master of Arts (MA)200486.68.15.1 !0.2 !!100%200885.49.54.80.3 !!100%201288.68.33.1 !#100%201689.57.23.3#100%Master of Education or Teaching200484.59.65.70.2 !!100%200883.513.03.40.1 !!100%201288.77.04.4#100%201686.011.42.6#100%Master of Business Administration (MBA)200489.28.62.0 !0.2 !!100%200889.78.71.6 !!#100%201288.19.22.7 !#100%201692.26.01.8#100%Master of Public Admin or Policy200484.114.0 !1.9 !!#100%200880.012.9 !7.1 !!#100%201284.39.1 !6.5 !!#100%201692.47.5 !0.1 !!#100%Master of Social Work (MSW)200479.719.4 !0.9 !!#100%200890.64.6 !4.8 !#100%201295.72.1 !!2.2 !!#100%201695.32.9 !!1.9 !!#100%Master of Fine Arts (MFA)200484.212.1 !!3.7 !!#100%200883.210.5 !6.0 !!0.3 !!100%201289.16.14.8 !!#100%201691.36.4 !2.2 !!#100%Master of Public Health (MPH)200495.43.4 !!1.2 !!#100%200892.84.0 !!3.3 !!#100%201289.97.6 !2.4 !!#100%201687.611.2 !!0.9 !!0.3 !!100%Other masters degree program200484.411.83.6 !0.2 !!100%200885.39.84.70.3 !!100%201284.89.35.80.1 !!100%201684.310.55.2#100%Doctor of Philosophy (PhD)200487.99.42.60.1 !!100%200889.46.93.60.1 !!100%201289.67.03.5#100%201685.89.94.30.1 !!100%Doctor of Education (EdD)200457.229.013.60.2 !!100%200872.118.89.00.1 !!100%201264.824.510.60.1 !!100%201665.424.99.7#100%Doctor of Science or Engineering200493.75.6 !0.7 !!#100%200884.29.2 !5.8 !!0.8 !!100%201290.87.1 !2.2 !!#100%201692.55.1 !2.4 !!#100%Doctor of Psychology (PsyD)200488.19.4 !2.5 !!#100%200893.84.8 !!0.8 !!0.6 !!100%201287.46.2 !6.4 !!#100%201684.89.3 !5.8 !0.1 !!100%Doctor of Business or Public Admin200475.117.2 !!7.7 !!#100%2008‡‡‡‡100%201262.417.220.4#100%201672.917.99.2#100%Doctor of Fine Arts (DFA)2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Doctor of Theology (ThD)2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Other Doctoral Degree200482.611.45.7 !!0.4 !!100%200884.98.94.31.9 !!100%201277.014.58.5#100%201684.113.12.8#100%Ministry or Divinity200473.620.65.8 !!#100%200870.413.3 !15.6 !0.7 !!100%2012‡‡‡‡100%201699.6#0.4 !!#100%Law (LLB or JD)200494.84.40.8 !!#100%200893.15.41.4 !#100%201297.81.5 !0.7 !!#100%201694.64.3 !1.1 !!#100%Medicine or Osteopathic Medicine200499.30.6 !!0.1 !!#100%200898.30.9 !!0.8 !!#100%201299.10.9 !!##100%201698.51.5 !!##100%Dentistry (DDS, DMD)200498.81.2 !!##100%200898.91.1 !!##100%201296.13.9 !!##100%201697.62.4 !!##100%Chiropractic (DC, DCM)2004‡‡‡‡100%2008‡‡‡‡100%201297.32.0 !!0.7 !!#100%2016‡‡‡‡100%Pharmacy (PharmD)200493.53.5 !!3.0 !!#100%200894.62.8 !!2.5 !!#100%201298.70.7 !!0.6 !!#100%201696.13.9 !!##100%Optometry (OD)2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Podiatry (DPM, DP, PodD)2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Veterinary Medicine (DVM)200497.2#2.8 !!#100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Post-baccalaureate certificate200478.615.95.2 !0.3 !!100%200872.114.213.10.6 !!100%201281.812.95.3#100%201669.717.313.0#100%Other professional practice doctoral degree2004————100%2008————100%201283.78.08.2 !#100%201683.712.3 !4.0 !#100%Not in a degree program2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Years between BA and graduate school by Graduate and first professional degree programs for years 2004, 2008, 2012 and 2016 Years between BA and graduate school1-15 months16-25 months26-39 months40 or more monthsTotalEstimatesTotal200486.49.83.60.1 !100%200886.19.74.00.2 !100%201288.08.13.9#100%201687.39.03.6#100%Graduate and first professional degree programsMaster of Science (MS)200488.28.63.2#100%200888.19.52.30.1 !!100%201289.97.13.0#100%201691.36.72.0#100%Master of Arts (MA)200486.68.15.1 !0.2 !!100%200885.49.54.80.3 !!100%201288.68.33.1 !#100%201689.57.23.3#100%Master of Education or Teaching200484.59.65.70.2 !!100%200883.513.03.40.1 !!100%201288.77.04.4#100%201686.011.42.6#100%Master of Business Administration (MBA)200489.28.62.0 !0.2 !!100%200889.78.71.6 !!#100%201288.19.22.7 !#100%201692.26.01.8#100%Master of Public Admin or Policy200484.114.0 !1.9 !!#100%200880.012.9 !7.1 !!#100%201284.39.1 !6.5 !!#100%201692.47.5 !0.1 !!#100%Master of Social Work (MSW)200479.719.4 !0.9 !!#100%200890.64.6 !4.8 !#100%201295.72.1 !!2.2 !!#100%201695.32.9 !!1.9 !!#100%Master of Fine Arts (MFA)200484.212.1 !!3.7 !!#100%200883.210.5 !6.0 !!0.3 !!100%201289.16.14.8 !!#100%201691.36.4 !2.2 !!#100%Master of Public Health (MPH)200495.43.4 !!1.2 !!#100%200892.84.0 !!3.3 !!#100%201289.97.6 !2.4 !!#100%201687.611.2 !!0.9 !!0.3 !!100%Other masters degree program200484.411.83.6 !0.2 !!100%200885.39.84.70.3 !!100%201284.89.35.80.1 !!100%201684.310.55.2#100%Doctor of Philosophy (PhD)200487.99.42.60.1 !!100%200889.46.93.60.1 !!100%201289.67.03.5#100%201685.89.94.30.1 !!100%Doctor of Education (EdD)200457.229.013.60.2 !!100%200872.118.89.00.1 !!100%201264.824.510.60.1 !!100%201665.424.99.7#100%Doctor of Science or Engineering200493.75.6 !0.7 !!#100%200884.29.2 !5.8 !!0.8 !!100%201290.87.1 !2.2 !!#100%201692.55.1 !2.4 !!#100%Doctor of Psychology (PsyD)200488.19.4 !2.5 !!#100%200893.84.8 !!0.8 !!0.6 !!100%201287.46.2 !6.4 !!#100%201684.89.3 !5.8 !0.1 !!100%Doctor of Business or Public Admin200475.117.2 !!7.7 !!#100%2008‡‡‡‡100%201262.417.220.4#100%201672.917.99.2#100%Doctor of Fine Arts (DFA)2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Doctor of Theology (ThD)2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Other Doctoral Degree200482.611.45.7 !!0.4 !!100%200884.98.94.31.9 !!100%201277.014.58.5#100%201684.113.12.8#100%Ministry or Divinity200473.620.65.8 !!#100%200870.413.3 !15.6 !0.7 !!100%2012‡‡‡‡100%201699.6#0.4 !!#100%Law (LLB or JD)200494.84.40.8 !!#100%200893.15.41.4 !#100%201297.81.5 !0.7 !!#100%201694.64.3 !1.1 !!#100%Medicine or Osteopathic Medicine200499.30.6 !!0.1 !!#100%200898.30.9 !!0.8 !!#100%201299.10.9 !!##100%201698.51.5 !!##100%Dentistry (DDS, DMD)200498.81.2 !!##100%200898.91.1 !!##100%201296.13.9 !!##100%201697.62.4 !!##100%Chiropractic (DC, DCM)2004‡‡‡‡100%2008‡‡‡‡100%201297.32.0 !!0.7 !!#100%2016‡‡‡‡100%Pharmacy (PharmD)200493.53.5 !!3.0 !!#100%200894.62.8 !!2.5 !!#100%201298.70.7 !!0.6 !!#100%201696.13.9 !!##100%Optometry (OD)2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Podiatry (DPM, DP, PodD)2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Veterinary Medicine (DVM)200497.2#2.8 !!#100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Post-baccalaureate certificate200478.615.95.2 !0.3 !!100%200872.114.213.10.6 !!100%201281.812.95.3#100%201669.717.313.0#100%Other professional practice doctoral degree2004————100%2008————100%201283.78.08.2 !#100%201683.712.3 !4.0 !#100%Not in a degree program2004‡‡‡‡100%2008‡‡‡‡100%2012‡‡‡‡100%2016‡‡‡‡100%Standard Error (BRR)Total20040.780.680.310.04 20080.670.470.420.07 20120.560.480.35† 20160.610.470.33† Graduate and first professional degree programsMaster of Science (MS)20041.681.480.69† 20081.421.410.400.09 20121.441.130.88† 20160.930.840.44† Master of Arts (MA)20042.021.391.590.24 20081.751.470.760.23 20121.891.741.08† 20161.891.790.81† Master of Education or Teaching20041.891.601.000.16 20081.811.660.580.07 20121.731.371.13† 20161.781.630.64† Master of Business Administration (MBA)20041.881.690.630.16 20082.531.641.85† 20122.081.771.16† 20161.110.970.52† Master of Public Admin or Policy20044.824.891.26† 20085.875.163.82† 20125.304.533.79† 20162.922.930.08† Master of Social Work (MSW)20046.356.350.63† 20082.942.182.21† 20122.151.211.79† 20162.101.671.30† Master of Fine Arts (MFA)20048.167.563.12† 20085.404.014.060.33 20123.061.342.66† 20163.673.142.17† Master of Public Health (MPH)20042.892.271.78† 20083.272.192.28† 20123.783.681.58† 20166.766.830.610.40 Other masters degree program20043.873.111.230.16 20081.551.171.040.16 20121.791.611.060.14 20161.741.351.18† Doctor of Philosophy (PhD)20041.301.150.370.04 20081.150.910.720.05 20120.690.520.36† 20161.281.090.730.07 Doctor of Education (EdD)20043.502.442.340.18 20086.574.712.650.13 20123.273.361.850.07 20162.992.812.12† Doctor of Science or Engineering20042.572.480.64† 20085.623.474.080.77 20123.543.411.57† 20162.892.341.48† Doctor of Psychology (PsyD)20043.513.451.88† 20083.373.210.910.55 20123.642.543.20† 20163.483.522.200.08 Doctor of Business or Public Admin200415.9312.097.33† 2008‡‡‡‡ 20125.133.373.21† 20163.364.762.18† Doctor of Fine Arts (DFA)2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Doctor of Theology (ThD)2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Other Doctoral Degree20045.122.445.160.24 20082.551.501.271.90 20123.952.992.04† 20162.782.760.81† Ministry or Divinity20047.396.054.17† 20086.914.287.160.71 2012‡‡‡‡ 20160.49†0.49† Law (LLB or JD)20041.471.050.62† 20081.451.230.51† 20120.850.730.49† 20161.781.650.76† Medicine or Osteopathic Medicine20040.460.410.17† 20080.930.790.57† 20120.530.53†† 20161.411.41†† Dentistry (DDS, DMD)20041.411.41†† 20081.191.19†† 20123.043.04†† 20163.653.65†† Chiropractic (DC, DCM)2004‡‡‡‡ 2008‡‡‡‡ 20121.541.491.31† 2016‡‡‡‡ Pharmacy (PharmD)20045.263.083.99† 20083.252.172.50† 20120.800.540.61† 20162.182.18†† Optometry (OD)2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Podiatry (DPM, DP, PodD)2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Veterinary Medicine (DVM)20042.48†2.48† 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Post-baccalaureate certificate20043.032.531.660.30 20083.562.283.410.36 20122.331.901.57† 20163.452.202.69† Other professional practice doctoral degree2004†††† 2008†††† 20123.102.352.56† 20164.854.721.43† Not in a degree program2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Relative Standard Error (%)Total20040.906.898.4434.11 20080.774.8410.5933.05 20120.635.889.03† 20160.705.249.02† Graduate and first professional degree programsMaster of Science (MS)20041.9017.2321.64† 20081.6214.8117.4981.32 20121.6015.8629.28† 20161.0212.6022.11† Master of Arts (MA)20042.3317.2331.42101.90 20082.0515.5215.7082.55 20122.1321.0434.54† 20162.1124.9924.07† Master of Education or Teaching20042.2416.6217.7080.62 20082.1612.8116.9174.15 20121.9519.6525.90† 20162.0714.2524.44† Master of Business Administration (MBA)20042.1119.6331.5287.99 20082.8218.90116.88† 20122.3619.2043.03† 20161.2016.1529.04† Master of Public Admin or Policy20045.7334.9466.13† 20087.3440.1253.68† 20126.2949.5658.16† 20163.1639.1484.75† Master of Social Work (MSW)20047.9632.7173.86† 20083.2447.1246.32† 20122.2556.4381.11† 20162.2158.3169.47† Master of Fine Arts (MFA)20049.6962.4084.29† 20086.5038.2567.19104.17 20123.4321.8855.59† 20164.0248.7097.48† Master of Public Health (MPH)20043.0367.17143.05† 20083.5255.2869.45† 20124.2048.0765.28† 20167.7261.0869.03118.74 Other masters degree program20044.5826.3834.3797.33 20081.8211.9822.3658.03 20122.1117.2318.39100.05 20162.0612.9022.66† Doctor of Philosophy (PhD)20041.4812.1514.5762.69 20081.2913.2719.6671.16 20120.777.4810.18† 20161.5010.9517.23107.36 Doctor of Education (EdD)20046.128.4317.14102.00 20089.1125.1029.29112.13 20125.0413.7117.4398.93 20164.5711.3021.85† Doctor of Science or Engineering20042.7444.7592.22† 20086.6737.5970.9499.50 20123.9048.3471.77† 20163.1345.8561.25† Doctor of Psychology (PsyD)20043.9836.6675.87† 20083.6066.73113.0596.00 20124.1741.0250.07† 20164.1137.9637.6076.08 Doctor of Business or Public Admin200421.2170.3294.85† 2008‡‡‡‡ 20128.2219.5815.75† 20164.6226.6023.58† Doctor of Fine Arts (DFA)2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Doctor of Theology (ThD)2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Other Doctoral Degree20046.2121.3890.8069.24 20083.0016.7829.7897.52 20125.1220.6723.95† 20163.3121.0428.91† Ministry or Divinity200410.0529.4271.73† 20089.8132.1845.94108.76 2012‡‡‡‡ 20160.49†113.04† Law (LLB or JD)20041.5523.6581.93† 20081.5622.6835.33† 20120.8648.9373.95† 20161.8838.1772.36† Medicine or Osteopathic Medicine20040.4665.87144.74† 20080.9590.6969.35† 20120.5461.43†† 20161.4391.19†† Dentistry (DDS, DMD)20041.43116.61†† 20081.21104.80†† 20123.1677.24†† 20163.74153.17†† Chiropractic (DC, DCM)2004‡‡‡‡ 2008‡‡‡‡ 20121.5876.27182.88† 2016‡‡‡‡ Pharmacy (PharmD)20045.6387.00133.73† 20083.4476.3298.62† 20120.8172.27110.37† 20162.2655.37†† Optometry (OD)2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Podiatry (DPM, DP, PodD)2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Veterinary Medicine (DVM)20042.55†90.17† 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Post-baccalaureate certificate20043.8515.9031.86118.00 20084.9416.0525.9861.58 20122.8414.7429.66† 20164.9512.6520.68† Other professional practice doctoral degree2004†††† 2008†††† 20123.7129.2831.04† 20165.8038.3235.76† Not in a degree program2004‡‡‡‡ 2008‡‡‡‡ 2012‡‡‡‡ 2016‡‡‡‡ Weighted Sample Sizes (n/1,000s)Total20042,069.2 20082,673.0 20122,872.3 20162,757.4 Graduate and first professional degree programsMaster of Science (MS)2004340.7 2008426.0 2012547.0 2016588.1 Master of Arts (MA)2004185.7 2008264.1 2012277.3 2016229.0 Master of Education or Teaching2004310.8 2008409.8 2012376.2 2016290.0 Master of Business Administration (MBA)2004283.3 2008398.5 2012351.2 2016320.7 Master of Public Admin or Policy200439.0 200829.7 201238.8 201640.9 Master of Social Work (MSW)200429.5 200841.1 201247.4 201663.3 Master of Fine Arts (MFA)200414.3 200824.6 201245.6 201630.5 Master of Public Health (MPH)200415.5 200826.5 201240.0 201632.8 Other masters degree program2004192.5 2008266.8 2012307.0 2016374.3 Doctor of Philosophy (PhD)2004202.4 2008286.3 2012274.7 2016218.4 Doctor of Education (EdD)200435.8 200856.8 201261.5 201652.4 Doctor of Science or Engineering200410.5 200814.1 20128.3 20169.0 Doctor of Psychology (PsyD)200410.7 200811.2 201211.3 201619.0 Doctor of Business or Public Admin20044.0 2008‡ 20129.1 20169.9 Doctor of Fine Arts (DFA)2004‡ 2008‡ 2012‡ 2016‡ Doctor of Theology (ThD)2004‡ 2008‡ 2012‡ 2016‡ Other Doctoral Degree200445.3 200876.4 201238.7 201674.0 Ministry or Divinity200433.4 200813.9 2012‡ 201623.8 Law (LLB or JD)2004104.4 200899.7 201288.3 201658.0 Medicine or Osteopathic Medicine200444.3 200838.1 201257.9 201647.2 Dentistry (DDS, DMD)200411.1 200810.7 20127.2 201615.1 Chiropractic (DC, DCM)2004‡ 2008‡ 201211.0 2016‡ Pharmacy (PharmD)200412.3 200814.8 201234.4 201618.0 Optometry (OD)2004‡ 2008‡ 2012‡ 2016‡ Podiatry (DPM, DP, PodD)2004‡ 2008‡ 2012‡ 2016‡ Veterinary Medicine (DVM)200410.0 2008‡ 2012‡ 2016‡ Post-baccalaureate certificate2004116.7 2008149.7 2012196.1 2016184.4 Other professional practice doctoral degree2004‡ 2008‡ 201234.2 201642.1 Not in a degree program2004‡ 2008‡ 2012‡ 2016‡ Years between BA and graduate school by Graduate and first professional degree programs for years 2004, 2008, 2012 and 2016 Years between BA and graduate school1-15 months16-25 months26-39 months40 or more monthsTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal200486.4[84.80-87.87]9.8[8.58-11.26]3.6[3.08-4.29]0.1 ![0.06-0.24]100%200886.1[84.76-87.39]9.7[8.82-10.68]4.0[3.21-4.88]0.2 ![0.10-0.38]100%201288.0[86.83-89.03]8.1[7.23-9.11]3.9[3.25-4.64]#[0.00-0.10]100%201687.3[86.09-88.51]9.0[8.12-9.99]3.6[3.03-4.32]#[0.01-0.04]100%Graduate and first professional degree programsMaster of Science (MS)200488.2[84.50-91.15]8.6[6.08-11.97]3.2[2.08-4.89]##100%200888.1[85.02-90.66]9.5[7.07-12.66]2.3[1.60-3.19]0.1 !![0.02-0.54]100%201289.9[86.65-92.36]7.1[5.21-9.73]3.0[1.68-5.31]##100%201691.3[89.30-93.00]6.7[5.20-8.54]2.0[1.29-3.08]#[0.00-0.04]100%Master of Arts (MA)200486.6[82.13-90.13]8.1[5.71-11.26]5.1 ![2.71-9.31]0.2 !![0.03-1.76]100%200885.4[81.63-88.57]9.5[6.94-12.79]4.8[3.53-6.55]0.3 !![0.05-1.40]100%201288.6[84.34-91.85]8.3[5.42-12.39]3.1 ![1.57-6.10]##100%201689.5[85.11-92.64]7.2[4.34-11.60]3.3[2.07-5.35]#[0.00-0.39]100%Master of Education or Teaching200484.5[80.41-87.90]9.6[6.90-13.28]5.7[3.98-7.98]0.2 !![0.04-0.96]100%200883.5[79.67-86.80]13.0[10.02-16.60]3.4[2.44-4.76]0.1 !![0.02-0.38]100%201288.7[84.78-91.65]7.0[4.70-10.20]4.4[2.61-7.24]##100%201686.0[82.06-89.12]11.4[8.57-15.02]2.6[1.62-4.26]##100%Master of Business Administration (MBA)200489.2[84.88-92.38]8.6[5.82-12.60]2.0 ![1.07-3.70]0.2 !![0.03-1.05]100%200889.7[83.56-93.75]8.7[5.95-12.52]1.6 !![0.15-14.32]##100%201288.1[83.33-91.60]9.2[6.28-13.37]2.7 ![1.15-6.23]##100%201692.2[89.75-94.14]6.0[4.34-8.20]1.8[1.01-3.16]##100%Master of Public Admin or Policy200484.1[72.22-91.50]14.0 ![6.80-26.61]1.9 !![0.51-6.84]##100%200880.0[66.00-89.21]12.9 ![5.62-26.80]7.1 !![2.39-19.30]##100%201284.3[70.93-92.25]9.1 ![3.31-22.76]6.5 !![2.00-19.22]##100%201692.4[84.26-96.52]7.5 ![3.40-15.72]0.1 !![0.02-0.50]##100%Master of Social Work (MSW)200479.7[64.46-89.52]19.4 ![9.76-34.89]0.9 !![0.20-3.63]##100%200890.6[83.00-95.01]4.6 ![1.80-11.40]4.8 ![1.88-11.55]##100%201295.7[88.82-98.39]2.1 !![0.70-6.39]2.2 !![0.44-10.37]##100%201695.3[88.91-98.05]2.9 !![0.90-8.80]1.9 !![0.47-7.16]##100%Master of Fine Arts (MFA)200484.2[61.38-94.69]12.1 !![3.29-35.87]3.7 !![0.68-17.76]##100%200883.2[69.76-91.35]10.5 ![4.80-21.39]6.0 !![1.55-20.85]0.3 !![0.04-2.42]100%201289.1[81.45-93.81]6.1[3.96-9.37]4.8 !![1.57-13.73]##100%201691.3[80.84-96.34]6.4 ![2.41-16.13]2.2 !![0.32-13.96]##100%Master of Public Health (MPH)200495.4[85.02-98.68]3.4 !![0.88-12.13]1.2 !![0.07-17.98]##100%200892.8[83.08-97.09]4.0 !![1.31-11.36]3.3 !![0.82-12.28]##100%201289.9[79.68-95.31]7.6 ![2.88-18.77]2.4 !![0.66-8.51]##100%201687.6[67.42-96.02]11.2 !![3.14-32.83]0.9 !![0.22-3.40]0.3 !![0.03-3.40]100%Other masters degree program200484.4[75.23-90.66]11.8[6.91-19.45]3.6 ![1.81-6.99]0.2 !![0.02-1.10]100%200885.3[81.94-88.06]9.8[7.72-12.37]4.7[2.99-7.20]0.3 !![0.09-0.88]100%201284.8[80.90-87.98]9.3[6.61-13.03]5.8[3.99-8.23]0.1 !![0.02-0.99]100%201684.3[80.58-87.44]10.5[8.10-13.46]5.2[3.32-8.09]##100%Doctor of Philosophy (PhD)200487.9[85.12-90.28]9.4[7.40-11.94]2.6[1.93-3.42]0.1 !![0.02-0.22]100%200889.4[86.90-91.47]6.9[5.29-8.93]3.6[2.47-5.35]0.1 !![0.02-0.28]100%201289.6[88.11-90.84]7.0[6.00-8.06]3.5[2.85-4.26]##100%201685.8[83.04-88.11]9.9[7.97-12.28]4.3[3.02-5.96]0.1 !![0.01-0.52]100%Doctor of Education (EdD)200457.2[50.19-63.92]29.0[24.41-34.04]13.6[9.65-18.94]0.2 !![0.02-1.31]100%200872.1[57.54-83.07]18.8[11.17-29.85]9.0[5.01-15.80]0.1 !![0.01-1.03]100%201264.8[58.09-70.91]24.5[18.51-31.75]10.6[7.49-14.88]0.1 !![0.01-0.47]100%201665.4[59.28-71.02]24.9[19.75-30.82]9.7[6.26-14.79]#[0.01-0.07]100%Doctor of Science or Engineering200493.7[86.35-97.27]5.6 ![2.26-13.02]0.7 !![0.11-4.21]##100%200884.2[69.86-92.48]9.2 ![4.31-18.73]5.8 !![1.37-21.23]0.8 !![0.11-5.36]100%201290.8[81.03-95.76]7.1 ![2.65-17.47]2.2 !![0.52-8.68]##100%201692.5[84.42-96.55]5.1 ![2.03-12.23]2.4 !![0.71-7.84]##100%Doctor of Psychology (PsyD)200488.1[79.29-93.48]9.4 ![4.47-18.76]2.5 !![0.55-10.55]##100%200893.8[82.82-97.95]4.8 !![1.25-16.74]0.8 !![0.09-7.16]0.6 !![0.09-3.74]100%201287.4[78.34-93.03]6.2 ![2.71-13.50]6.4 !![2.33-16.41]##100%201684.8[76.59-90.47]9.3 ![4.28-18.90]5.8 ![2.75-12.00]0.1 !![0.02-0.48]100%Doctor of Business or Public Admin200475.1[35.99-94.17]17.2 !![3.74-52.55]7.7 !![1.09-38.87]##100%2008‡‡‡‡‡‡‡‡100%201262.4[51.87-71.83]17.2[11.54-24.91]20.4[14.79-27.47]##100%201672.9[65.76-78.99]17.9[10.31-29.20]9.2[5.75-14.52]##100%Doctor of Fine Arts (DFA)2004100.0†‡‡‡‡‡‡100%2008‡‡‡‡‡‡‡‡100%2012‡‡‡‡‡‡‡‡100%2016‡‡‡‡‡‡‡‡100%Doctor of Theology (ThD)2004‡‡‡‡‡‡‡‡100%2008‡‡‡‡‡‡‡‡100%2012100.0†‡‡‡‡‡‡100%2016‡‡‡‡‡‡‡‡100%Other Doctoral Degree200482.6[70.12-90.51]11.4[7.41-17.17]5.7 !![0.89-28.69]0.4 !![0.09-1.38]100%200884.9[79.13-89.25]8.9[6.37-12.33]4.3[2.36-7.61]1.9 !![0.28-12.36]100%201277.0[68.36-83.88]14.5[9.49-21.39]8.5[5.27-13.50]##100%201684.1[77.78-88.81]13.1[8.56-19.56]2.8[1.59-4.95]#[0.01-0.10]100%Ministry or Divinity200473.6[56.83-85.52]20.6[11.10-34.97]5.8 !![1.36-21.72]##100%200870.4[55.32-82.09]13.3 ![6.88-24.21]15.6 ![5.94-35.09]0.7 !![0.08-5.40]100%2012‡‡‡‡‡‡‡‡100%201699.6[96.11-99.95]##0.4 !![0.05-3.89]##100%Law (LLB or JD)200494.8[91.02-97.04]4.4[2.77-7.04]0.8 !![0.15-3.74]##100%200893.1[89.68-95.51]5.4[3.45-8.42]1.4 ![0.71-2.87]##100%201297.8[95.36-99.00]1.5 ![0.57-3.90]0.7 !![0.15-2.82]##100%201694.6[89.84-97.23]4.3 ![2.01-9.02]1.1 !![0.25-4.30]##100%Medicine or Osteopathic Medicine200499.3[97.51-99.78]0.6 !![0.17-2.27]0.1 !![0.01-2.06]##100%200898.3[95.04-99.44]0.9 !![0.14-5.04]0.8 !![0.21-3.18]##100%201299.1[97.11-99.74]0.9 !![0.26-2.89]####100%201698.5[91.11-99.75]1.5 !![0.25-8.89]####100%Dentistry (DDS, DMD)200498.8[88.83-99.88]1.2 !![0.12-11.17]####100%200898.9[91.47-99.86]1.1 !![0.14-8.53]####100%201296.1[83.34-99.17]3.9 !![0.83-16.66]####100%201697.6[64.99-99.89]2.4 !![0.11-35.01]####100%Chiropractic (DC, DCM)2004‡‡‡‡‡‡‡‡100%2008100.0†‡‡‡‡‡‡100%201297.3[91.91-99.15]2.0 !![0.43-8.47]0.7 !![0.02-21.43]##100%2016‡‡‡‡‡‡‡‡100%Pharmacy (PharmD)200493.5[72.32-98.74]3.5 !![0.62-17.83]3.0 !![0.20-31.81]##100%200894.6[83.30-98.41]2.8 !![0.62-12.11]2.5 !![0.35-16.06]##100%201298.7[95.71-99.61]0.7 !![0.18-3.06]0.6 !![0.06-4.73]##100%201696.1[88.70-98.70]3.9 !![1.30-11.30]####100%Optometry (OD)2004100.0†‡‡‡‡‡‡100%2008100.0†‡‡‡‡‡‡100%2012100.0†‡‡‡‡‡‡100%2016100.0†‡‡‡‡‡‡100%Podiatry (DPM, DP, PodD)2004100.0†‡‡‡‡‡‡100%2008—†—†—†—†100%2012100.0†‡‡‡‡‡‡100%2016—†—†—†—†100%Veterinary Medicine (DVM)200497.2[85.03-99.55]##2.8 !![0.45-14.97]##100%2008100.0†‡‡‡‡‡‡100%2012100.0†‡‡‡‡‡‡100%2016100.0†‡‡‡‡‡‡100%Post-baccalaureate certificate200478.6[72.07-84.00]15.9[11.52-21.54]5.2 ![2.76-9.64]0.3 !![0.02-2.56]100%200872.1[64.56-78.56]14.2[10.27-19.31]13.1[7.72-21.40]0.6 !![0.17-1.96]100%201281.8[76.75-85.94]12.9[9.59-17.14]5.3[2.93-9.41]##100%201669.7[62.46-76.00]17.3[13.44-22.11]13.0[8.55-19.27]##100%Other professional practice doctoral degree2004—†—†—†—†100%2008—†—†—†—†100%201283.7[76.66-88.97]8.0[4.45-14.04]8.2 ![4.41-14.90]##100%201683.7[71.78-91.17]12.3 ![5.60-24.96]4.0 ![1.96-8.00]##100%Not in a degree program2004—†—†—†—†100%2008—†—†—†—†100%2012—†—†—†—†100%2016—†—†—†—†100%2004200820122016 Years between BA and graduate schoolYears between BA and graduate schoolYears between BA and graduate schoolYears between BA and graduate school 1-15 months16-25 months26-39 months40 or more months1-15 months16-25 months26-39 months40 or more months1-15 months16-25 months26-39 months40 or more months1-15 months16-25 months26-39 months40 or more monthsEstimatesTotal86.49.83.60.186.19.74.00.288.08.13.9#87.39.03.6#Graduate and first professional degree programsMaster of Science (MS)88.28.63.2#88.19.52.30.189.97.13.0#91.36.72.0#Master of Arts (MA)86.68.15.10.285.49.54.80.388.68.33.1#89.57.23.3#Master of Education or Teaching84.59.65.70.283.513.03.40.188.77.04.4#86.011.42.6#Master of Business Administration (MBA)89.28.62.00.289.78.71.6#88.19.22.7#92.26.01.8#Master of Public Admin or Policy84.114.01.9#80.012.97.1#84.39.16.5#92.47.50.1#Master of Social Work (MSW)79.719.40.9#90.64.64.8#95.72.12.2#95.32.91.9#Master of Fine Arts (MFA)84.212.13.7#83.210.56.00.389.16.14.8#91.36.42.2#Master of Public Health (MPH)95.43.41.2#92.84.03.3#89.97.62.4#87.611.20.90.3Other masters degree program84.411.83.60.285.39.84.70.384.89.35.80.184.310.55.2#Doctor of Philosophy (PhD)87.99.42.60.189.46.93.60.189.67.03.5#85.89.94.30.1Doctor of Education (EdD)57.229.013.60.272.118.89.00.164.824.510.60.165.424.99.7#Doctor of Science or Engineering93.75.60.7#84.29.25.80.890.87.12.2#92.55.12.4#Doctor of Psychology (PsyD)88.19.42.5#93.84.80.80.687.46.26.4#84.89.35.80.1Doctor of Business or Public Admin75.117.27.7#‡‡‡‡62.417.220.4#72.917.99.2#Doctor of Fine Arts (DFA)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Doctor of Theology (ThD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Other Doctoral Degree82.611.45.70.484.98.94.31.977.014.58.5#84.113.12.8#Ministry or Divinity73.620.65.8#70.413.315.60.7‡‡‡‡99.6#0.4#Law (LLB or JD)94.84.40.8#93.15.41.4#97.81.50.7#94.64.31.1#Medicine or Osteopathic Medicine99.30.60.1#98.30.90.8#99.10.9##98.51.5##Dentistry (DDS, DMD)98.81.2##98.91.1##96.13.9##97.62.4##Chiropractic (DC, DCM)‡‡‡‡‡‡‡‡97.32.00.7#‡‡‡‡Pharmacy (PharmD)93.53.53.0#94.62.82.5#98.70.70.6#96.13.9##Optometry (OD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Podiatry (DPM, DP, PodD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Veterinary Medicine (DVM)97.2#2.8#‡‡‡‡‡‡‡‡‡‡‡‡Post-baccalaureate certificate78.615.95.20.372.114.213.10.681.812.95.3#69.717.313.0#Other professional practice doctoral degree————————83.78.08.2#83.712.34.0#Not in a degree program‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡2004200820122016 Years between BA and graduate schoolYears between BA and graduate schoolYears between BA and graduate schoolYears between BA and graduate school 1-15 months16-25 months26-39 months40 or more months1-15 months16-25 months26-39 months40 or more months1-15 months16-25 months26-39 months40 or more months1-15 months16-25 months26-39 months40 or more monthsEstimatesTotal86.49.83.60.186.19.74.00.288.08.13.9#87.39.03.6#Graduate and first professional degree programsMaster of Science (MS)88.28.63.2#88.19.52.30.189.97.13.0#91.36.72.0#Master of Arts (MA)86.68.15.10.285.49.54.80.388.68.33.1#89.57.23.3#Master of Education or Teaching84.59.65.70.283.513.03.40.188.77.04.4#86.011.42.6#Master of Business Administration (MBA)89.28.62.00.289.78.71.6#88.19.22.7#92.26.01.8#Master of Public Admin or Policy84.114.01.9#80.012.97.1#84.39.16.5#92.47.50.1#Master of Social Work (MSW)79.719.40.9#90.64.64.8#95.72.12.2#95.32.91.9#Master of Fine Arts (MFA)84.212.13.7#83.210.56.00.389.16.14.8#91.36.42.2#Master of Public Health (MPH)95.43.41.2#92.84.03.3#89.97.62.4#87.611.20.90.3Other masters degree program84.411.83.60.285.39.84.70.384.89.35.80.184.310.55.2#Doctor of Philosophy (PhD)87.99.42.60.189.46.93.60.189.67.03.5#85.89.94.30.1Doctor of Education (EdD)57.229.013.60.272.118.89.00.164.824.510.60.165.424.99.7#Doctor of Science or Engineering93.75.60.7#84.29.25.80.890.87.12.2#92.55.12.4#Doctor of Psychology (PsyD)88.19.42.5#93.84.80.80.687.46.26.4#84.89.35.80.1Doctor of Business or Public Admin75.117.27.7#‡‡‡‡62.417.220.4#72.917.99.2#Doctor of Fine Arts (DFA)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Doctor of Theology (ThD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Other Doctoral Degree82.611.45.70.484.98.94.31.977.014.58.5#84.113.12.8#Ministry or Divinity73.620.65.8#70.413.315.60.7‡‡‡‡99.6#0.4#Law (LLB or JD)94.84.40.8#93.15.41.4#97.81.50.7#94.64.31.1#Medicine or Osteopathic Medicine99.30.60.1#98.30.90.8#99.10.9##98.51.5##Dentistry (DDS, DMD)98.81.2##98.91.1##96.13.9##97.62.4##Chiropractic (DC, DCM)‡‡‡‡‡‡‡‡97.32.00.7#‡‡‡‡Pharmacy (PharmD)93.53.53.0#94.62.82.5#98.70.70.6#96.13.9##Optometry (OD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Podiatry (DPM, DP, PodD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Veterinary Medicine (DVM)97.2#2.8#‡‡‡‡‡‡‡‡‡‡‡‡Post-baccalaureate certificate78.615.95.20.372.114.213.10.681.812.95.3#69.717.313.0#Other professional practice doctoral degree————————83.78.08.2#83.712.34.0#Not in a degree program‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Standard Error (BRR)Total0.780.680.310.040.670.470.420.070.560.480.35†0.610.470.33†Graduate and first professional degree programsMaster of Science (MS)1.681.480.69†1.421.410.400.091.441.130.88†0.930.840.44†Master of Arts (MA)2.021.391.590.241.751.470.760.231.891.741.08†1.891.790.81†Master of Education or Teaching1.891.601.000.161.811.660.580.071.731.371.13†1.781.630.64†Master of Business Administration (MBA)1.881.690.630.162.531.641.85†2.081.771.16†1.110.970.52†Master of Public Admin or Policy4.824.891.26†5.875.163.82†5.304.533.79†2.922.930.08†Master of Social Work (MSW)6.356.350.63†2.942.182.21†2.151.211.79†2.101.671.30†Master of Fine Arts (MFA)8.167.563.12†5.404.014.060.333.061.342.66†3.673.142.17†Master of Public Health (MPH)2.892.271.78†3.272.192.28†3.783.681.58†6.766.830.610.40Other masters degree program3.873.111.230.161.551.171.040.161.791.611.060.141.741.351.18†Doctor of Philosophy (PhD)1.301.150.370.041.150.910.720.050.690.520.36†1.281.090.730.07Doctor of Education (EdD)3.502.442.340.186.574.712.650.133.273.361.850.072.992.812.12†Doctor of Science or Engineering2.572.480.64†5.623.474.080.773.543.411.57†2.892.341.48†Doctor of Psychology (PsyD)3.513.451.88†3.373.210.910.553.642.543.20†3.483.522.200.08Doctor of Business or Public Admin15.9312.097.33†‡‡‡‡5.133.373.21†3.364.762.18†Doctor of Fine Arts (DFA)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Doctor of Theology (ThD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Other Doctoral Degree5.122.445.160.242.551.501.271.903.952.992.04†2.782.760.81†Ministry or Divinity7.396.054.17†6.914.287.160.71‡‡‡‡0.49†0.49†Law (LLB or JD)1.471.050.62†1.451.230.51†0.850.730.49†1.781.650.76†Medicine or Osteopathic Medicine0.460.410.17†0.930.790.57†0.530.53††1.411.41††Dentistry (DDS, DMD)1.411.41††1.191.19††3.043.04††3.653.65††Chiropractic (DC, DCM)‡‡‡‡‡‡‡‡1.541.491.31†‡‡‡‡Pharmacy (PharmD)5.263.083.99†3.252.172.50†0.800.540.61†2.182.18††Optometry (OD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Podiatry (DPM, DP, PodD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Veterinary Medicine (DVM)2.48†2.48†‡‡‡‡‡‡‡‡‡‡‡‡Post-baccalaureate certificate3.032.531.660.303.562.283.410.362.331.901.57†3.452.202.69†Other professional practice doctoral degree††††††††3.102.352.56†4.854.721.43†Not in a degree program‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Relative Standard Error (%)Total0.906.898.4434.110.774.8410.5933.050.635.889.03†0.705.249.02†Graduate and first professional degree programsMaster of Science (MS)1.9017.2321.64†1.6214.8117.4981.321.6015.8629.28†1.0212.6022.11†Master of Arts (MA)2.3317.2331.42101.902.0515.5215.7082.552.1321.0434.54†2.1124.9924.07†Master of Education or Teaching2.2416.6217.7080.622.1612.8116.9174.151.9519.6525.90†2.0714.2524.44†Master of Business Administration (MBA)2.1119.6331.5287.992.8218.90116.88†2.3619.2043.03†1.2016.1529.04†Master of Public Admin or Policy5.7334.9466.13†7.3440.1253.68†6.2949.5658.16†3.1639.1484.75†Master of Social Work (MSW)7.9632.7173.86†3.2447.1246.32†2.2556.4381.11†2.2158.3169.47†Master of Fine Arts (MFA)9.6962.4084.29†6.5038.2567.19104.173.4321.8855.59†4.0248.7097.48†Master of Public Health (MPH)3.0367.17143.05†3.5255.2869.45†4.2048.0765.28†7.7261.0869.03118.74Other masters degree program4.5826.3834.3797.331.8211.9822.3658.032.1117.2318.39100.052.0612.9022.66†Doctor of Philosophy (PhD)1.4812.1514.5762.691.2913.2719.6671.160.777.4810.18†1.5010.9517.23107.36Doctor of Education (EdD)6.128.4317.14102.009.1125.1029.29112.135.0413.7117.4398.934.5711.3021.85†Doctor of Science or Engineering2.7444.7592.22†6.6737.5970.9499.503.9048.3471.77†3.1345.8561.25†Doctor of Psychology (PsyD)3.9836.6675.87†3.6066.73113.0596.004.1741.0250.07†4.1137.9637.6076.08Doctor of Business or Public Admin21.2170.3294.85†‡‡‡‡8.2219.5815.75†4.6226.6023.58†Doctor of Fine Arts (DFA)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Doctor of Theology (ThD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Other Doctoral Degree6.2121.3890.8069.243.0016.7829.7897.525.1220.6723.95†3.3121.0428.91†Ministry or Divinity10.0529.4271.73†9.8132.1845.94108.76‡‡‡‡0.49†113.04†Law (LLB or JD)1.5523.6581.93†1.5622.6835.33†0.8648.9373.95†1.8838.1772.36†Medicine or Osteopathic Medicine0.4665.87144.74†0.9590.6969.35†0.5461.43††1.4391.19††Dentistry (DDS, DMD)1.43116.61††1.21104.80††3.1677.24††3.74153.17††Chiropractic (DC, DCM)‡‡‡‡‡‡‡‡1.5876.27182.88†‡‡‡‡Pharmacy (PharmD)5.6387.00133.73†3.4476.3298.62†0.8172.27110.37†2.2655.37††Optometry (OD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Podiatry (DPM, DP, PodD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Veterinary Medicine (DVM)2.55†90.17†‡‡‡‡‡‡‡‡‡‡‡‡Post-baccalaureate certificate3.8515.9031.86118.004.9416.0525.9861.582.8414.7429.66†4.9512.6520.68†Other professional practice doctoral degree††††††††3.7129.2831.04†5.8038.3235.76†Not in a degree program‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Weighted Sample Sizes (n/1,000s)Total2,069.2 2,673.0 2,872.3 2,757.4 Graduate and first professional degree programsMaster of Science (MS)340.7 426.0 547.0 588.1 Master of Arts (MA)185.7 264.1 277.3 229.0 Master of Education or Teaching310.8 409.8 376.2 290.0 Master of Business Administration (MBA)283.3 398.5 351.2 320.7 Master of Public Admin or Policy39.0 29.7 38.8 40.9 Master of Social Work (MSW)29.5 41.1 47.4 63.3 Master of Fine Arts (MFA)14.3 24.6 45.6 30.5 Master of Public Health (MPH)15.5 26.5 40.0 32.8 Other masters degree program192.5 266.8 307.0 374.3 Doctor of Philosophy (PhD)202.4 286.3 274.7 218.4 Doctor of Education (EdD)35.8 56.8 61.5 52.4 Doctor of Science or Engineering10.5 14.1 8.3 9.0 Doctor of Psychology (PsyD)10.7 11.2 11.3 19.0 Doctor of Business or Public Admin4.0 ‡ 9.1 9.9 Doctor of Fine Arts (DFA)‡ ‡ ‡ ‡ Doctor of Theology (ThD)‡ ‡ ‡ ‡ Other Doctoral Degree45.3 76.4 38.7 74.0 Ministry or Divinity33.4 13.9 ‡ 23.8 Law (LLB or JD)104.4 99.7 88.3 58.0 Medicine or Osteopathic Medicine44.3 38.1 57.9 47.2 Dentistry (DDS, DMD)11.1 10.7 7.2 15.1 Chiropractic (DC, DCM)‡ ‡ 11.0 ‡ Pharmacy (PharmD)12.3 14.8 34.4 18.0 Optometry (OD)‡ ‡ ‡ ‡ Podiatry (DPM, DP, PodD)‡ ‡ ‡ ‡ Veterinary Medicine (DVM)10.0 ‡ ‡ ‡ Post-baccalaureate certificate116.7 149.7 196.1 184.4 Other professional practice doctoral degree‡ ‡ 34.2 42.1 Not in a degree program‡ ‡ ‡ ‡ 2004200820122016 Years between BA and graduate schoolYears between BA and graduate schoolYears between BA and graduate schoolYears between BA and graduate school 1-15 months16-25 months26-39 months40 or more months1-15 months16-25 months26-39 months40 or more months1-15 months16-25 months26-39 months40 or more months1-15 months16-25 months26-39 months40 or more months Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal86.4[84.80-87.87]9.8[8.58-11.26]3.6[3.08-4.29]0.1 ![0.06-0.24]86.1[84.76-87.39]9.7[8.82-10.68]4.0[3.21-4.88]0.2 ![0.10-0.38]88.0[86.83-89.03]8.1[7.23-9.11]3.9[3.25-4.64]#[0.00-0.10]87.3[86.09-88.51]9.0[8.12-9.99]3.6[3.03-4.32]#[0.01-0.04]Graduate and first professional degree programsMaster of Science (MS)88.2[84.50-91.15]8.6[6.08-11.97]3.2[2.08-4.89]##88.1[85.02-90.66]9.5[7.07-12.66]2.3[1.60-3.19]0.1 !![0.02-0.54]89.9[86.65-92.36]7.1[5.21-9.73]3.0[1.68-5.31]##91.3[89.30-93.00]6.7[5.20-8.54]2.0[1.29-3.08]#[0.00-0.04]Master of Arts (MA)86.6[82.13-90.13]8.1[5.71-11.26]5.1 ![2.71-9.31]0.2 !![0.03-1.76]85.4[81.63-88.57]9.5[6.94-12.79]4.8[3.53-6.55]0.3 !![0.05-1.40]88.6[84.34-91.85]8.3[5.42-12.39]3.1 ![1.57-6.10]##89.5[85.11-92.64]7.2[4.34-11.60]3.3[2.07-5.35]#[0.00-0.39]Master of Education or Teaching84.5[80.41-87.90]9.6[6.90-13.28]5.7[3.98-7.98]0.2 !![0.04-0.96]83.5[79.67-86.80]13.0[10.02-16.60]3.4[2.44-4.76]0.1 !![0.02-0.38]88.7[84.78-91.65]7.0[4.70-10.20]4.4[2.61-7.24]##86.0[82.06-89.12]11.4[8.57-15.02]2.6[1.62-4.26]##Master of Business Administration (MBA)89.2[84.88-92.38]8.6[5.82-12.60]2.0 ![1.07-3.70]0.2 !![0.03-1.05]89.7[83.56-93.75]8.7[5.95-12.52]1.6 !![0.15-14.32]##88.1[83.33-91.60]9.2[6.28-13.37]2.7 ![1.15-6.23]##92.2[89.75-94.14]6.0[4.34-8.20]1.8[1.01-3.16]##Master of Public Admin or Policy84.1[72.22-91.50]14.0 ![6.80-26.61]1.9 !![0.51-6.84]##80.0[66.00-89.21]12.9 ![5.62-26.80]7.1 !![2.39-19.30]##84.3[70.93-92.25]9.1 ![3.31-22.76]6.5 !![2.00-19.22]##92.4[84.26-96.52]7.5 ![3.40-15.72]0.1 !![0.02-0.50]##Master of Social Work (MSW)79.7[64.46-89.52]19.4 ![9.76-34.89]0.9 !![0.20-3.63]##90.6[83.00-95.01]4.6 ![1.80-11.40]4.8 ![1.88-11.55]##95.7[88.82-98.39]2.1 !![0.70-6.39]2.2 !![0.44-10.37]##95.3[88.91-98.05]2.9 !![0.90-8.80]1.9 !![0.47-7.16]##Master of Fine Arts (MFA)84.2[61.38-94.69]12.1 !![3.29-35.87]3.7 !![0.68-17.76]##83.2[69.76-91.35]10.5 ![4.80-21.39]6.0 !![1.55-20.85]0.3 !![0.04-2.42]89.1[81.45-93.81]6.1[3.96-9.37]4.8 !![1.57-13.73]##91.3[80.84-96.34]6.4 ![2.41-16.13]2.2 !![0.32-13.96]##Master of Public Health (MPH)95.4[85.02-98.68]3.4 !![0.88-12.13]1.2 !![0.07-17.98]##92.8[83.08-97.09]4.0 !![1.31-11.36]3.3 !![0.82-12.28]##89.9[79.68-95.31]7.6 ![2.88-18.77]2.4 !![0.66-8.51]##87.6[67.42-96.02]11.2 !![3.14-32.83]0.9 !![0.22-3.40]0.3 !![0.03-3.40]Other masters degree program84.4[75.23-90.66]11.8[6.91-19.45]3.6 ![1.81-6.99]0.2 !![0.02-1.10]85.3[81.94-88.06]9.8[7.72-12.37]4.7[2.99-7.20]0.3 !![0.09-0.88]84.8[80.90-87.98]9.3[6.61-13.03]5.8[3.99-8.23]0.1 !![0.02-0.99]84.3[80.58-87.44]10.5[8.10-13.46]5.2[3.32-8.09]##Doctor of Philosophy (PhD)87.9[85.12-90.28]9.4[7.40-11.94]2.6[1.93-3.42]0.1 !![0.02-0.22]89.4[86.90-91.47]6.9[5.29-8.93]3.6[2.47-5.35]0.1 !![0.02-0.28]89.6[88.11-90.84]7.0[6.00-8.06]3.5[2.85-4.26]##85.8[83.04-88.11]9.9[7.97-12.28]4.3[3.02-5.96]0.1 !![0.01-0.52]Doctor of Education (EdD)57.2[50.19-63.92]29.0[24.41-34.04]13.6[9.65-18.94]0.2 !![0.02-1.31]72.1[57.54-83.07]18.8[11.17-29.85]9.0[5.01-15.80]0.1 !![0.01-1.03]64.8[58.09-70.91]24.5[18.51-31.75]10.6[7.49-14.88]0.1 !![0.01-0.47]65.4[59.28-71.02]24.9[19.75-30.82]9.7[6.26-14.79]#[0.01-0.07]Doctor of Science or Engineering93.7[86.35-97.27]5.6 ![2.26-13.02]0.7 !![0.11-4.21]##84.2[69.86-92.48]9.2 ![4.31-18.73]5.8 !![1.37-21.23]0.8 !![0.11-5.36]90.8[81.03-95.76]7.1 ![2.65-17.47]2.2 !![0.52-8.68]##92.5[84.42-96.55]5.1 ![2.03-12.23]2.4 !![0.71-7.84]##Doctor of Psychology (PsyD)88.1[79.29-93.48]9.4 ![4.47-18.76]2.5 !![0.55-10.55]##93.8[82.82-97.95]4.8 !![1.25-16.74]0.8 !![0.09-7.16]0.6 !![0.09-3.74]87.4[78.34-93.03]6.2 ![2.71-13.50]6.4 !![2.33-16.41]##84.8[76.59-90.47]9.3 ![4.28-18.90]5.8 ![2.75-12.00]0.1 !![0.02-0.48]Doctor of Business or Public Admin75.1[35.99-94.17]17.2 !![3.74-52.55]7.7 !![1.09-38.87]##‡‡‡‡‡‡‡‡62.4[51.87-71.83]17.2[11.54-24.91]20.4[14.79-27.47]##72.9[65.76-78.99]17.9[10.31-29.20]9.2[5.75-14.52]##Doctor of Fine Arts (DFA)100.0†‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡Doctor of Theology (ThD)‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡100.0†‡‡‡‡‡‡‡‡‡‡‡‡‡‡Other Doctoral Degree82.6[70.12-90.51]11.4[7.41-17.17]5.7 !![0.89-28.69]0.4 !![0.09-1.38]84.9[79.13-89.25]8.9[6.37-12.33]4.3[2.36-7.61]1.9 !![0.28-12.36]77.0[68.36-83.88]14.5[9.49-21.39]8.5[5.27-13.50]##84.1[77.78-88.81]13.1[8.56-19.56]2.8[1.59-4.95]#[0.01-0.10]Ministry or Divinity73.6[56.83-85.52]20.6[11.10-34.97]5.8 !![1.36-21.72]##70.4[55.32-82.09]13.3 ![6.88-24.21]15.6 ![5.94-35.09]0.7 !![0.08-5.40]‡‡‡‡‡‡‡‡99.6[96.11-99.95]##0.4 !![0.05-3.89]##Law (LLB or JD)94.8[91.02-97.04]4.4[2.77-7.04]0.8 !![0.15-3.74]##93.1[89.68-95.51]5.4[3.45-8.42]1.4 ![0.71-2.87]##97.8[95.36-99.00]1.5 ![0.57-3.90]0.7 !![0.15-2.82]##94.6[89.84-97.23]4.3 ![2.01-9.02]1.1 !![0.25-4.30]##Medicine or Osteopathic Medicine99.3[97.51-99.78]0.6 !![0.17-2.27]0.1 !![0.01-2.06]##98.3[95.04-99.44]0.9 !![0.14-5.04]0.8 !![0.21-3.18]##99.1[97.11-99.74]0.9 !![0.26-2.89]####98.5[91.11-99.75]1.5 !![0.25-8.89]####Dentistry (DDS, DMD)98.8[88.83-99.88]1.2 !![0.12-11.17]####98.9[91.47-99.86]1.1 !![0.14-8.53]####96.1[83.34-99.17]3.9 !![0.83-16.66]####97.6[64.99-99.89]2.4 !![0.11-35.01]####Chiropractic (DC, DCM)‡‡‡‡‡‡‡‡100.0†‡‡‡‡‡‡97.3[91.91-99.15]2.0 !![0.43-8.47]0.7 !![0.02-21.43]##‡‡‡‡‡‡‡‡Pharmacy (PharmD)93.5[72.32-98.74]3.5 !![0.62-17.83]3.0 !![0.20-31.81]##94.6[83.30-98.41]2.8 !![0.62-12.11]2.5 !![0.35-16.06]##98.7[95.71-99.61]0.7 !![0.18-3.06]0.6 !![0.06-4.73]##96.1[88.70-98.70]3.9 !![1.30-11.30]####Optometry (OD)100.0†‡‡‡‡‡‡100.0†‡‡‡‡‡‡100.0†‡‡‡‡‡‡100.0†‡‡‡‡‡‡Podiatry (DPM, DP, PodD)100.0†‡‡‡‡‡‡—†—†—†—†100.0†‡‡‡‡‡‡—†—†—†—†Veterinary Medicine (DVM)97.2[85.03-99.55]##2.8 !![0.45-14.97]##100.0†‡‡‡‡‡‡100.0†‡‡‡‡‡‡100.0†‡‡‡‡‡‡Post-baccalaureate certificate78.6[72.07-84.00]15.9[11.52-21.54]5.2 ![2.76-9.64]0.3 !![0.02-2.56]72.1[64.56-78.56]14.2[10.27-19.31]13.1[7.72-21.40]0.6 !![0.17-1.96]81.8[76.75-85.94]12.9[9.59-17.14]5.3[2.93-9.41]##69.7[62.46-76.00]17.3[13.44-22.11]13.0[8.55-19.27]##Other professional practice doctoral degree—†—†—†—†—†—†—†—†83.7[76.66-88.97]8.0[4.45-14.04]8.2 ![4.41-14.90]##83.7[71.78-91.17]12.3 ![5.60-24.96]4.0 ![1.96-8.00]##Not in a degree program—†—†—†—†—†—†—†—†—†—†—†—†—†—†—†—†— Not available.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.— Not available.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.STDERR-SOURCE-END— Not available.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: GRADGAP and GRADGPG.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: GRADGAP and GRADGPG. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: GRADGAP (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and GRADGPG (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebkp6cbfebkp6c5Highest level of education ever expected by Graduate fellowship amount, Graduate research assistantship amount, Graduate teaching assistantship amount and Graduate traineeship amount for years 2004, 2008, 2012 and 2016 Highest level of education ever expectedMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeTotalEstimatesTotal200441.24.212.142.5100%200845.27.69.937.3100%201243.27.1—49.7100%201641.38.8—50.0100%Graduate fellowship amountLowest 25 Percent200428.12.3 !!18.051.6100%200836.64.1 !12.247.1100%201244.53.9 !—51.6100%201643.56.4—50.2100%Lower Middle 25 Percent200428.01.7 !!20.549.8100%200830.22.5 !14.952.4100%201230.47.4 !—62.1100%201639.83.0—57.2100%Upper Middle 25 Percent200421.42.5 !24.251.9100%200822.52.8 !25.449.4100%201236.22.0 !—61.8100%201632.74.7 !—62.6100%Highest 25 Percent200413.10.1 !!19.966.9100%200822.20.7 !!18.059.1100%201220.81.4 !!—77.8100%201621.02.7 !—76.3100%Graduate research assistantship amountLowest 25 Percent200418.33.5 !!12.066.1100%200822.32.8 !16.059.0100%201226.52.3 !!—71.1100%201619.7 !0.3 !!—80.0100%Lower Middle 25 Percent200421.62.2 !!5.4 !70.8100%200822.21.6 !!3.7 !72.5100%201222.22.7 !!—75.1100%201627.16.5 !!—66.4100%Upper Middle 25 Percent200415.41.5 !!4.8 !78.3100%200829.30.9 !!0.8 !!68.9100%201214.1#—85.9100%201639.90.8 !!—59.3100%Highest 25 Percent200411.70.9 !!5.0 !82.5100%200812.61.0 !!1.7 !84.7100%20127.2 !0.1 !!—92.6100%20165.1 !!#—94.9100%Graduate teaching assistantship amountLowest 25 Percent200428.55.4 !!4.9 !61.2100%200837.62.0 !!5.355.0100%201240.21.8 !!—57.9100%201632.13.5 !!—64.4100%Lower Middle 25 Percent200428.90.3 !!5.4 !65.4100%200824.41.3 !!3.4 !70.9100%201223.44.1 !!—72.5100%201634.86.7 !!—58.5100%Upper Middle 25 Percent200427.10.5 !!2.1 !!70.3100%200829.21.9 !!2.0 !66.9100%201219.51.5 !!—79.0100%201611.9 !0.1 !!—88.0100%Highest 25 Percent200413.50.9 !!4.6 !81.0100%200812.71.1 !!3.782.5100%201215.21.5 !!—83.3100%201616.7 !1.5 !!—81.8100%Graduate traineeship amountLowest 25 Percent2004‡‡‡‡100%2008‡‡‡‡100%201248.0 !1.4 !!—50.6 !100%2016————100%Lower Middle 25 Percent2004‡‡‡‡100%2008‡‡‡‡100%201224.8 !#—75.2100%2016————100%Upper Middle 25 Percent2004‡‡‡‡100%2008‡‡‡‡100%201215.9 !!#—84.1100%2016————100%Highest 25 Percent2004##3.7 !!96.3100%20082.2 !!#5.2 !!92.6100%201211.3 !!#—88.7100%2016————100%Highest level of education ever expected by Graduate fellowship amount, Graduate research assistantship amount, Graduate teaching assistantship amount and Graduate traineeship amount for years 2004, 2008, 2012 and 2016 Highest level of education ever expectedMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeTotalEstimatesTotal200441.24.212.142.5100%200845.27.69.937.3100%201243.27.1—49.7100%201641.38.8—50.0100%Graduate fellowship amountLowest 25 Percent200428.12.3 !!18.051.6100%200836.64.1 !12.247.1100%201244.53.9 !—51.6100%201643.56.4—50.2100%Lower Middle 25 Percent200428.01.7 !!20.549.8100%200830.22.5 !14.952.4100%201230.47.4 !—62.1100%201639.83.0—57.2100%Upper Middle 25 Percent200421.42.5 !24.251.9100%200822.52.8 !25.449.4100%201236.22.0 !—61.8100%201632.74.7 !—62.6100%Highest 25 Percent200413.10.1 !!19.966.9100%200822.20.7 !!18.059.1100%201220.81.4 !!—77.8100%201621.02.7 !—76.3100%Graduate research assistantship amountLowest 25 Percent200418.33.5 !!12.066.1100%200822.32.8 !16.059.0100%201226.52.3 !!—71.1100%201619.7 !0.3 !!—80.0100%Lower Middle 25 Percent200421.62.2 !!5.4 !70.8100%200822.21.6 !!3.7 !72.5100%201222.22.7 !!—75.1100%201627.16.5 !!—66.4100%Upper Middle 25 Percent200415.41.5 !!4.8 !78.3100%200829.30.9 !!0.8 !!68.9100%201214.1#—85.9100%201639.90.8 !!—59.3100%Highest 25 Percent200411.70.9 !!5.0 !82.5100%200812.61.0 !!1.7 !84.7100%20127.2 !0.1 !!—92.6100%20165.1 !!#—94.9100%Graduate teaching assistantship amountLowest 25 Percent200428.55.4 !!4.9 !61.2100%200837.62.0 !!5.355.0100%201240.21.8 !!—57.9100%201632.13.5 !!—64.4100%Lower Middle 25 Percent200428.90.3 !!5.4 !65.4100%200824.41.3 !!3.4 !70.9100%201223.44.1 !!—72.5100%201634.86.7 !!—58.5100%Upper Middle 25 Percent200427.10.5 !!2.1 !!70.3100%200829.21.9 !!2.0 !66.9100%201219.51.5 !!—79.0100%201611.9 !0.1 !!—88.0100%Highest 25 Percent200413.50.9 !!4.6 !81.0100%200812.71.1 !!3.782.5100%201215.21.5 !!—83.3100%201616.7 !1.5 !!—81.8100%Graduate traineeship amountLowest 25 Percent2004‡‡‡‡100%2008‡‡‡‡100%201248.0 !1.4 !!—50.6 !100%2016————100%Lower Middle 25 Percent2004‡‡‡‡100%2008‡‡‡‡100%201224.8 !#—75.2100%2016————100%Upper Middle 25 Percent2004‡‡‡‡100%2008‡‡‡‡100%201215.9 !!#—84.1100%2016————100%Highest 25 Percent2004##3.7 !!96.3100%20082.2 !!#5.2 !!92.6100%201211.3 !!#—88.7100%2016————100%Standard Error (BRR)Total20040.970.390.500.89 20080.840.480.340.86 20120.620.39†0.63 20160.740.39†0.76 Graduate fellowship amountLowest 25 Percent20044.041.292.624.03 20082.921.451.843.10 20123.561.43†3.49 20162.531.30†2.78 Lower Middle 25 Percent20043.381.192.684.03 20083.180.912.204.00 20123.852.45†4.10 20163.020.72†3.05 Upper Middle 25 Percent20043.091.123.023.78 20082.030.911.852.32 20122.870.83†2.94 20162.791.41†3.03 Highest 25 Percent20042.930.072.743.92 20082.290.461.882.58 20122.710.74†2.64 20162.630.97†2.67 Graduate research assistantship amountLowest 25 Percent20043.242.303.194.09 20083.091.292.503.69 20125.201.77†5.27 20168.610.28†8.59 Lower Middle 25 Percent20044.472.012.374.81 20083.230.981.143.26 20124.261.79†4.37 20168.005.11†7.78 Upper Middle 25 Percent20043.310.922.253.99 20084.010.700.433.82 20123.27††3.27 20168.600.73†8.59 Highest 25 Percent20042.850.651.652.98 20082.730.640.812.79 20122.630.11†2.63 20163.19††3.19 Graduate teaching assistantship amountLowest 25 Percent20044.152.771.854.10 20084.021.141.313.61 20126.481.26†6.31 20168.242.28†7.53 Lower Middle 25 Percent20043.520.241.763.67 20083.300.821.243.22 20124.152.45†4.59 20167.414.26†6.23 Upper Middle 25 Percent20044.580.321.244.52 20084.051.120.954.16 20124.461.25†4.39 20163.940.12†3.96 Highest 25 Percent20042.871.052.163.40 20082.470.801.072.67 20123.891.24†4.10 20165.451.50†5.38 Graduate traineeship amountLowest 25 Percent2004‡‡‡‡ 2008‡‡‡‡ 201218.332.06†17.67 2016†††† Lower Middle 25 Percent2004‡‡‡‡ 2008‡‡‡‡ 201210.19††10.19 2016†††† Upper Middle 25 Percent2004‡‡‡‡ 2008‡‡‡‡ 20129.10††9.10 2016†††† Highest 25 Percent2004††5.675.67 20082.51†5.406.05 20127.69††7.69 2016†††† Relative Standard Error (%)Total20042.359.394.112.09 20081.876.283.442.31 20121.445.45†1.26 20161.794.47†1.53 Graduate fellowship amountLowest 25 Percent200414.3756.4014.537.81 20087.9935.6615.056.57 20128.0036.62†6.76 20165.8320.44†5.53 Lower Middle 25 Percent200412.0769.4713.108.08 200810.5336.8314.787.63 201212.6332.99†6.60 20167.5923.80†5.33 Upper Middle 25 Percent200414.4344.4612.497.29 20089.0432.447.294.69 20127.9442.22†4.76 20168.5330.22†4.85 Highest 25 Percent200422.3685.6413.815.85 200810.3062.9410.464.37 201213.0651.51†3.39 201612.5535.44†3.50 Graduate research assistantship amountLowest 25 Percent200417.6764.7526.526.18 200813.9046.0115.666.26 201219.6075.74†7.42 201643.65106.28†10.74 Lower Middle 25 Percent200420.6690.6643.946.80 200814.5261.5730.854.50 201219.2165.38†5.82 201629.4978.82†11.72 Upper Middle 25 Percent200421.5063.1346.955.09 200813.6774.1251.145.55 201223.17††3.81 201621.5788.23†14.48 Highest 25 Percent200424.3973.4333.383.62 200821.5967.4348.333.30 201236.33102.85†2.83 201662.89††3.37 Graduate teaching assistantship amountLowest 25 Percent200414.5850.9137.446.71 200810.6856.2024.676.55 201216.1069.67†10.88 201625.6764.49†11.71 Lower Middle 25 Percent200412.1973.6332.485.61 200813.5462.5435.934.54 201217.7559.07†6.34 201621.2663.70†10.65 Upper Middle 25 Percent200416.8765.8359.916.42 200813.8659.4748.526.21 201222.8283.08†5.56 201633.11162.35†4.50 Highest 25 Percent200421.19111.5347.384.20 200819.4274.7028.513.24 201225.5385.23†4.92 201632.6098.18†6.58 Graduate traineeship amountLowest 25 Percent2004‡‡‡‡ 2008‡‡‡‡ 201238.19145.27†34.93 2016†††† Lower Middle 25 Percent2004‡‡‡‡ 2008‡‡‡‡ 201241.03††13.55 2016†††† Upper Middle 25 Percent2004‡‡‡‡ 2008‡‡‡‡ 201257.06††10.83 2016†††† Highest 25 Percent2004††151.185.89 2008113.19†104.756.53 201268.29††8.67 2016†††† Weighted Sample Sizes (n/1,000s)Total20042,824.3 20083,492.0 20123,682.2 20163,564.2 Graduate fellowship amountLowest 25 Percent200472.9 200899.0 2012140.5 2016182.1 Lower Middle 25 Percent200482.4 2008106.3 2012127.7 2016172.8 Upper Middle 25 Percent200479.2 2008111.4 2012154.6 2016176.9 Highest 25 Percent200478.6 2008106.5 2012141.8 2016177.0 Graduate research assistantship amountLowest 25 Percent200447.2 200858.8 201248.1 201619.5 Lower Middle 25 Percent200446.1 200859.2 201258.6 201620.0 Upper Middle 25 Percent200450.6 200850.4 201253.4 201619.0 Highest 25 Percent200449.3 200867.7 201253.5 201619.4 Graduate teaching assistantship amountLowest 25 Percent200449.6 200863.5 201251.9 201635.3 Lower Middle 25 Percent200456.2 200863.5 201248.1 201630.9 Upper Middle 25 Percent200453.6 200863.1 201255.7 201630.8 Highest 25 Percent200453.9 200864.2 201252.0 201632.2 Graduate traineeship amountLowest 25 Percent2004‡ 2008‡ 20125.2 2016† Lower Middle 25 Percent2004‡ 2008‡ 20127.0 2016† Upper Middle 25 Percent2004‡ 2008‡ 20126.4 2016† Highest 25 Percent20044.2 20084.4 20126.3 2016† Highest level of education ever expected by Graduate fellowship amount, Graduate research assistantship amount, Graduate teaching assistantship amount and Graduate traineeship amount for years 2004, 2008, 2012 and 2016 Highest level of education ever expectedMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal200441.2[39.27-43.09]4.2[3.48-5.04]12.1[11.15-13.12]42.5[40.80-44.30]100%200845.2[43.57-46.90]7.6[6.74-8.63]9.9[9.22-10.56]37.3[35.59-38.98]100%201243.2[41.94-44.39]7.1[6.40-7.93]—†49.7[48.47-50.94]100%201641.3[39.81-42.72]8.8[8.02-9.56]—†50.0[48.48-51.49]100%Graduate fellowship amountLowest 25 Percent200428.1[20.88-36.74]2.3 !![0.74-6.80]18.0[13.40-23.74]51.6[43.67-59.42]100%200836.6[31.04-42.53]4.1 ![2.00-8.12]12.2[9.02-16.32]47.1[41.09-53.24]100%201244.5[37.64-51.59]3.9 ![1.88-7.92]—†51.6[44.73-58.40]100%201643.5[38.54-48.51]6.4[4.24-9.47]—†50.2[44.72-55.63]100%Lower Middle 25 Percent200428.0[21.85-35.13]1.7 !![0.43-6.54]20.5[15.68-26.25]49.8[41.95-57.70]100%200830.2[24.32-36.82]2.5 ![1.19-5.07]14.9[11.06-19.79]52.4[44.54-60.18]100%201230.4[23.43-38.51]7.4 ![3.82-13.92]—†62.1[53.79-69.83]100%201639.8[34.03-45.88]3.0[1.89-4.82]—†57.2[51.09-63.05]100%Upper Middle 25 Percent200421.4[15.92-28.09]2.5 ![1.04-5.99]24.2[18.74-30.64]51.9[44.44-59.25]100%200822.5[18.72-26.72]2.8 ![1.46-5.25]25.4[21.91-29.20]49.4[44.81-53.92]100%201236.2[30.76-42.05]2.0 ![0.85-4.50]—†61.8[55.86-67.43]100%201632.7[27.48-38.46]4.7 ![2.56-8.40]—†62.6[56.45-68.36]100%Highest 25 Percent200413.1[8.32-20.02]0.1 !![0.02-0.47]19.9[15.00-25.83]66.9[58.82-74.18]100%200822.2[18.02-27.03]0.7 !![0.21-2.50]18.0[14.57-21.99]59.1[53.92-64.07]100%201220.8[15.92-26.61]1.4 !![0.52-3.92]—†77.8[72.18-82.57]100%201621.0[16.24-26.62]2.7 ![1.35-5.45]—†76.3[70.64-81.16]100%Graduate research assistantship amountLowest 25 Percent200418.3[12.78-25.59]3.5 !![0.97-12.15]12.0[7.01-19.83]66.1[57.65-73.65]100%200822.3[16.76-28.96]2.8 ![1.12-6.85]16.0[11.63-21.53]59.0[51.53-65.99]100%201226.5[17.60-37.95]2.3 !![0.52-9.95]—†71.1[59.74-80.34]100%201619.7 ![7.76-41.80]0.3 !![0.03-2.14]—†80.0[58.12-92.02]100%Lower Middle 25 Percent200421.6[14.09-31.69]2.2 !![0.36-12.34]5.4 ![2.23-12.45]70.8[60.49-79.31]100%200822.2[16.50-29.22]1.6 !![0.47-5.27]3.7 ![2.01-6.75]72.5[65.62-78.42]100%201222.2[14.91-31.69]2.7 !![0.74-9.59]—†75.1[65.53-82.68]100%201627.1[14.34-45.24]6.5 !![1.30-26.75]—†66.4[49.85-79.73]100%Upper Middle 25 Percent200415.4[9.93-23.10]1.5 !![0.42-4.99]4.8 ![1.87-11.76]78.3[69.46-85.19]100%200829.3[22.06-37.78]0.9 !![0.22-4.02]0.8 !![0.31-2.30]68.9[60.91-75.90]100%201214.1[8.80-21.85]##—†85.9[78.15-91.20]100%201639.9[24.64-57.36]0.8 !![0.14-4.59]—†59.3[41.94-74.62]100%Highest 25 Percent200411.7[7.12-18.54]0.9 !![0.21-3.70]5.0 ![2.54-9.44]82.5[75.82-87.62]100%200812.6[8.16-19.06]1.0 !![0.25-3.56]1.7 ![0.64-4.29]84.7[78.38-89.48]100%20127.2 ![3.48-14.46]0.1 !![0.01-0.84]—†92.6[85.49-96.42]100%20165.1 !![1.43-16.50]##—†94.9[83.50-98.57]100%Graduate teaching assistantship amountLowest 25 Percent200428.5[21.01-37.28]5.4 !![1.95-14.28]4.9 ![2.34-10.16]61.2[52.83-68.88]100%200837.6[30.10-45.84]2.0 !![0.66-6.02]5.3[3.24-8.54]55.0[47.87-62.00]100%201240.2[28.36-53.40]1.8 !![0.45-6.94]—†57.9[45.27-69.66]100%201632.1[18.33-49.93]3.5 !![0.97-12.06]—†64.4[48.58-77.52]100%Lower Middle 25 Percent200428.9[22.46-36.28]0.3 !![0.08-1.41]5.4 ![2.83-10.13]65.4[57.83-72.20]100%200824.4[18.45-31.42]1.3 !![0.38-4.40]3.4 ![1.68-6.92]70.9[64.17-76.82]100%201223.4[16.20-32.53]4.1 !![1.27-12.73]—†72.5[62.57-80.56]100%201634.8[21.93-50.44]6.7 !![1.83-21.59]—†58.5[45.93-70.01]100%Upper Middle 25 Percent200427.1[19.08-37.01]0.5 !![0.13-1.78]2.1 !![0.63-6.62]70.3[60.72-78.40]100%200829.2[21.91-37.79]1.9 !![0.58-5.99]2.0 ![0.75-5.02]66.9[58.30-74.57]100%201219.5[12.19-29.81]1.5 !![0.29-7.45]—†79.0[69.03-86.34]100%201611.9 ![6.05-22.09]0.1 !![0.00-1.83]—†88.0[77.79-93.91]100%Highest 25 Percent200413.5[8.81-20.26]0.9 !![0.10-8.02]4.6 ![1.76-11.27]81.0[73.34-86.79]100%200812.7[8.60-18.46]1.1 !![0.24-4.58]3.7[2.12-6.51]82.5[76.55-87.12]100%201215.2[9.02-24.53]1.5 !![0.27-7.51]—†83.3[73.64-89.94]100%201616.7 ![8.49-30.28]1.5 !![0.22-9.99]—†81.8[68.75-90.13]100%Graduate traineeship amountLowest 25 Percent2004‡‡‡‡‡‡‡‡100%2008‡‡‡‡‡‡‡‡100%201248.0 ![17.82-79.71]1.4 !![0.08-20.85]—†50.6 ![20.25-80.48]100%2016—†—†—†—†100%Lower Middle 25 Percent2004‡‡‡‡‡‡‡‡100%2008‡‡‡‡‡‡‡‡100%201224.8 ![10.12-49.22]##—†75.2[50.78-89.88]100%2016—†—†—†—†100%Upper Middle 25 Percent2004‡‡‡‡‡‡‡‡100%2008‡‡‡‡‡‡‡‡100%201215.9 !![4.74-41.99]##—†84.1[58.01-95.26]100%2016—†—†—†—†100%Highest 25 Percent2004####3.7 !![0.18-46.29]96.3[53.71-99.82]100%20082.2 !![0.23-18.17]##5.2 !![0.61-32.45]92.6[68.64-98.63]100%201211.3 !![2.71-36.66]##—†88.7[63.34-97.29]100%2016—†—†—†—†100%2004200820122016 Highest level of education ever expectedHighest level of education ever expectedHighest level of education ever expectedHighest level of education ever expected Master's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeEstimatesTotal41.24.212.142.545.27.69.937.343.27.1—49.741.38.8—50.0Graduate fellowship amountLowest 25 Percent28.12.318.051.636.64.112.247.144.53.9—51.643.56.4—50.2Lower Middle 25 Percent28.01.720.549.830.22.514.952.430.47.4—62.139.83.0—57.2Upper Middle 25 Percent21.42.524.251.922.52.825.449.436.22.0—61.832.74.7—62.6Highest 25 Percent13.10.119.966.922.20.718.059.120.81.4—77.821.02.7—76.3Graduate research assistantship amountLowest 25 Percent18.33.512.066.122.32.816.059.026.52.3—71.119.70.3—80.0Lower Middle 25 Percent21.62.25.470.822.21.63.772.522.22.7—75.127.16.5—66.4Upper Middle 25 Percent15.41.54.878.329.30.90.868.914.1#—85.939.90.8—59.3Highest 25 Percent11.70.95.082.512.61.01.784.77.20.1—92.65.1#—94.9Graduate teaching assistantship amountLowest 25 Percent28.55.44.961.237.62.05.355.040.21.8—57.932.13.5—64.4Lower Middle 25 Percent28.90.35.465.424.41.33.470.923.44.1—72.534.86.7—58.5Upper Middle 25 Percent27.10.52.170.329.21.92.066.919.51.5—79.011.90.1—88.0Highest 25 Percent13.50.94.681.012.71.13.782.515.21.5—83.316.71.5—81.8Graduate traineeship amountLowest 25 Percent‡‡‡‡‡‡‡‡48.01.4—50.6————Lower Middle 25 Percent‡‡‡‡‡‡‡‡24.8#—75.2————Upper Middle 25 Percent‡‡‡‡‡‡‡‡15.9#—84.1————Highest 25 Percent##3.796.32.2#5.292.611.3#—88.7————2004200820122016 Highest level of education ever expectedHighest level of education ever expectedHighest level of education ever expectedHighest level of education ever expected Master's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeEstimatesTotal41.24.212.142.545.27.69.937.343.27.1—49.741.38.8—50.0Graduate fellowship amountLowest 25 Percent28.12.318.051.636.64.112.247.144.53.9—51.643.56.4—50.2Lower Middle 25 Percent28.01.720.549.830.22.514.952.430.47.4—62.139.83.0—57.2Upper Middle 25 Percent21.42.524.251.922.52.825.449.436.22.0—61.832.74.7—62.6Highest 25 Percent13.10.119.966.922.20.718.059.120.81.4—77.821.02.7—76.3Graduate research assistantship amountLowest 25 Percent18.33.512.066.122.32.816.059.026.52.3—71.119.70.3—80.0Lower Middle 25 Percent21.62.25.470.822.21.63.772.522.22.7—75.127.16.5—66.4Upper Middle 25 Percent15.41.54.878.329.30.90.868.914.1#—85.939.90.8—59.3Highest 25 Percent11.70.95.082.512.61.01.784.77.20.1—92.65.1#—94.9Graduate teaching assistantship amountLowest 25 Percent28.55.44.961.237.62.05.355.040.21.8—57.932.13.5—64.4Lower Middle 25 Percent28.90.35.465.424.41.33.470.923.44.1—72.534.86.7—58.5Upper Middle 25 Percent27.10.52.170.329.21.92.066.919.51.5—79.011.90.1—88.0Highest 25 Percent13.50.94.681.012.71.13.782.515.21.5—83.316.71.5—81.8Graduate traineeship amountLowest 25 Percent‡‡‡‡‡‡‡‡48.01.4—50.6————Lower Middle 25 Percent‡‡‡‡‡‡‡‡24.8#—75.2————Upper Middle 25 Percent‡‡‡‡‡‡‡‡15.9#—84.1————Highest 25 Percent##3.796.32.2#5.292.611.3#—88.7————Standard Error (BRR)Total0.970.390.500.890.840.480.340.860.620.39†0.630.740.39†0.76Graduate fellowship amountLowest 25 Percent4.041.292.624.032.921.451.843.103.561.43†3.492.531.30†2.78Lower Middle 25 Percent3.381.192.684.033.180.912.204.003.852.45†4.103.020.72†3.05Upper Middle 25 Percent3.091.123.023.782.030.911.852.322.870.83†2.942.791.41†3.03Highest 25 Percent2.930.072.743.922.290.461.882.582.710.74†2.642.630.97†2.67Graduate research assistantship amountLowest 25 Percent3.242.303.194.093.091.292.503.695.201.77†5.278.610.28†8.59Lower Middle 25 Percent4.472.012.374.813.230.981.143.264.261.79†4.378.005.11†7.78Upper Middle 25 Percent3.310.922.253.994.010.700.433.823.27††3.278.600.73†8.59Highest 25 Percent2.850.651.652.982.730.640.812.792.630.11†2.633.19††3.19Graduate teaching assistantship amountLowest 25 Percent4.152.771.854.104.021.141.313.616.481.26†6.318.242.28†7.53Lower Middle 25 Percent3.520.241.763.673.300.821.243.224.152.45†4.597.414.26†6.23Upper Middle 25 Percent4.580.321.244.524.051.120.954.164.461.25†4.393.940.12†3.96Highest 25 Percent2.871.052.163.402.470.801.072.673.891.24†4.105.451.50†5.38Graduate traineeship amountLowest 25 Percent‡‡‡‡‡‡‡‡18.332.06†17.67††††Lower Middle 25 Percent‡‡‡‡‡‡‡‡10.19††10.19††††Upper Middle 25 Percent‡‡‡‡‡‡‡‡9.10††9.10††††Highest 25 Percent††5.675.672.51†5.406.057.69††7.69††††Relative Standard Error (%)Total2.359.394.112.091.876.283.442.311.445.45†1.261.794.47†1.53Graduate fellowship amountLowest 25 Percent14.3756.4014.537.817.9935.6615.056.578.0036.62†6.765.8320.44†5.53Lower Middle 25 Percent12.0769.4713.108.0810.5336.8314.787.6312.6332.99†6.607.5923.80†5.33Upper Middle 25 Percent14.4344.4612.497.299.0432.447.294.697.9442.22†4.768.5330.22†4.85Highest 25 Percent22.3685.6413.815.8510.3062.9410.464.3713.0651.51†3.3912.5535.44†3.50Graduate research assistantship amountLowest 25 Percent17.6764.7526.526.1813.9046.0115.666.2619.6075.74†7.4243.65106.28†10.74Lower Middle 25 Percent20.6690.6643.946.8014.5261.5730.854.5019.2165.38†5.8229.4978.82†11.72Upper Middle 25 Percent21.5063.1346.955.0913.6774.1251.145.5523.17††3.8121.5788.23†14.48Highest 25 Percent24.3973.4333.383.6221.5967.4348.333.3036.33102.85†2.8362.89††3.37Graduate teaching assistantship amountLowest 25 Percent14.5850.9137.446.7110.6856.2024.676.5516.1069.67†10.8825.6764.49†11.71Lower Middle 25 Percent12.1973.6332.485.6113.5462.5435.934.5417.7559.07†6.3421.2663.70†10.65Upper Middle 25 Percent16.8765.8359.916.4213.8659.4748.526.2122.8283.08†5.5633.11162.35†4.50Highest 25 Percent21.19111.5347.384.2019.4274.7028.513.2425.5385.23†4.9232.6098.18†6.58Graduate traineeship amountLowest 25 Percent‡‡‡‡‡‡‡‡38.19145.27†34.93††††Lower Middle 25 Percent‡‡‡‡‡‡‡‡41.03††13.55††††Upper Middle 25 Percent‡‡‡‡‡‡‡‡57.06††10.83††††Highest 25 Percent††151.185.89113.19†104.756.5368.29††8.67††††Weighted Sample Sizes (n/1,000s)Total2,824.3 3,492.0 3,682.2 3,564.2 Graduate fellowship amountLowest 25 Percent72.9 99.0 140.5 182.1 Lower Middle 25 Percent82.4 106.3 127.7 172.8 Upper Middle 25 Percent79.2 111.4 154.6 176.9 Highest 25 Percent78.6 106.5 141.8 177.0 Graduate research assistantship amountLowest 25 Percent47.2 58.8 48.1 19.5 Lower Middle 25 Percent46.1 59.2 58.6 20.0 Upper Middle 25 Percent50.6 50.4 53.4 19.0 Highest 25 Percent49.3 67.7 53.5 19.4 Graduate teaching assistantship amountLowest 25 Percent49.6 63.5 51.9 35.3 Lower Middle 25 Percent56.2 63.5 48.1 30.9 Upper Middle 25 Percent53.6 63.1 55.7 30.8 Highest 25 Percent53.9 64.2 52.0 32.2 Graduate traineeship amountLowest 25 Percent‡ ‡ 5.2 † Lower Middle 25 Percent‡ ‡ 7.0 † Upper Middle 25 Percent‡ ‡ 6.4 † Highest 25 Percent4.2 4.4 6.3 † 2004200820122016 Highest level of education ever expectedHighest level of education ever expectedHighest level of education ever expectedHighest level of education ever expected Master's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degreeMaster's degreePost-BA or post-master certificateFirst-professional degreeDoctoral degree Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal41.2[39.27-43.09]4.2[3.48-5.04]12.1[11.15-13.12]42.5[40.80-44.30]45.2[43.57-46.90]7.6[6.74-8.63]9.9[9.22-10.56]37.3[35.59-38.98]43.2[41.94-44.39]7.1[6.40-7.93]—†49.7[48.47-50.94]41.3[39.81-42.72]8.8[8.02-9.56]—†50.0[48.48-51.49]Graduate fellowship amountLowest 25 Percent28.1[20.88-36.74]2.3 !![0.74-6.80]18.0[13.40-23.74]51.6[43.67-59.42]36.6[31.04-42.53]4.1 ![2.00-8.12]12.2[9.02-16.32]47.1[41.09-53.24]44.5[37.64-51.59]3.9 ![1.88-7.92]—†51.6[44.73-58.40]43.5[38.54-48.51]6.4[4.24-9.47]—†50.2[44.72-55.63]Lower Middle 25 Percent28.0[21.85-35.13]1.7 !![0.43-6.54]20.5[15.68-26.25]49.8[41.95-57.70]30.2[24.32-36.82]2.5 ![1.19-5.07]14.9[11.06-19.79]52.4[44.54-60.18]30.4[23.43-38.51]7.4 ![3.82-13.92]—†62.1[53.79-69.83]39.8[34.03-45.88]3.0[1.89-4.82]—†57.2[51.09-63.05]Upper Middle 25 Percent21.4[15.92-28.09]2.5 ![1.04-5.99]24.2[18.74-30.64]51.9[44.44-59.25]22.5[18.72-26.72]2.8 ![1.46-5.25]25.4[21.91-29.20]49.4[44.81-53.92]36.2[30.76-42.05]2.0 ![0.85-4.50]—†61.8[55.86-67.43]32.7[27.48-38.46]4.7 ![2.56-8.40]—†62.6[56.45-68.36]Highest 25 Percent13.1[8.32-20.02]0.1 !![0.02-0.47]19.9[15.00-25.83]66.9[58.82-74.18]22.2[18.02-27.03]0.7 !![0.21-2.50]18.0[14.57-21.99]59.1[53.92-64.07]20.8[15.92-26.61]1.4 !![0.52-3.92]—†77.8[72.18-82.57]21.0[16.24-26.62]2.7 ![1.35-5.45]—†76.3[70.64-81.16]Graduate research assistantship amountLowest 25 Percent18.3[12.78-25.59]3.5 !![0.97-12.15]12.0[7.01-19.83]66.1[57.65-73.65]22.3[16.76-28.96]2.8 ![1.12-6.85]16.0[11.63-21.53]59.0[51.53-65.99]26.5[17.60-37.95]2.3 !![0.52-9.95]—†71.1[59.74-80.34]19.7 ![7.76-41.80]0.3 !![0.03-2.14]—†80.0[58.12-92.02]Lower Middle 25 Percent21.6[14.09-31.69]2.2 !![0.36-12.34]5.4 ![2.23-12.45]70.8[60.49-79.31]22.2[16.50-29.22]1.6 !![0.47-5.27]3.7 ![2.01-6.75]72.5[65.62-78.42]22.2[14.91-31.69]2.7 !![0.74-9.59]—†75.1[65.53-82.68]27.1[14.34-45.24]6.5 !![1.30-26.75]—†66.4[49.85-79.73]Upper Middle 25 Percent15.4[9.93-23.10]1.5 !![0.42-4.99]4.8 ![1.87-11.76]78.3[69.46-85.19]29.3[22.06-37.78]0.9 !![0.22-4.02]0.8 !![0.31-2.30]68.9[60.91-75.90]14.1[8.80-21.85]##—†85.9[78.15-91.20]39.9[24.64-57.36]0.8 !![0.14-4.59]—†59.3[41.94-74.62]Highest 25 Percent11.7[7.12-18.54]0.9 !![0.21-3.70]5.0 ![2.54-9.44]82.5[75.82-87.62]12.6[8.16-19.06]1.0 !![0.25-3.56]1.7 ![0.64-4.29]84.7[78.38-89.48]7.2 ![3.48-14.46]0.1 !![0.01-0.84]—†92.6[85.49-96.42]5.1 !![1.43-16.50]##—†94.9[83.50-98.57]Graduate teaching assistantship amountLowest 25 Percent28.5[21.01-37.28]5.4 !![1.95-14.28]4.9 ![2.34-10.16]61.2[52.83-68.88]37.6[30.10-45.84]2.0 !![0.66-6.02]5.3[3.24-8.54]55.0[47.87-62.00]40.2[28.36-53.40]1.8 !![0.45-6.94]—†57.9[45.27-69.66]32.1[18.33-49.93]3.5 !![0.97-12.06]—†64.4[48.58-77.52]Lower Middle 25 Percent28.9[22.46-36.28]0.3 !![0.08-1.41]5.4 ![2.83-10.13]65.4[57.83-72.20]24.4[18.45-31.42]1.3 !![0.38-4.40]3.4 ![1.68-6.92]70.9[64.17-76.82]23.4[16.20-32.53]4.1 !![1.27-12.73]—†72.5[62.57-80.56]34.8[21.93-50.44]6.7 !![1.83-21.59]—†58.5[45.93-70.01]Upper Middle 25 Percent27.1[19.08-37.01]0.5 !![0.13-1.78]2.1 !![0.63-6.62]70.3[60.72-78.40]29.2[21.91-37.79]1.9 !![0.58-5.99]2.0 ![0.75-5.02]66.9[58.30-74.57]19.5[12.19-29.81]1.5 !![0.29-7.45]—†79.0[69.03-86.34]11.9 ![6.05-22.09]0.1 !![0.00-1.83]—†88.0[77.79-93.91]Highest 25 Percent13.5[8.81-20.26]0.9 !![0.10-8.02]4.6 ![1.76-11.27]81.0[73.34-86.79]12.7[8.60-18.46]1.1 !![0.24-4.58]3.7[2.12-6.51]82.5[76.55-87.12]15.2[9.02-24.53]1.5 !![0.27-7.51]—†83.3[73.64-89.94]16.7 ![8.49-30.28]1.5 !![0.22-9.99]—†81.8[68.75-90.13]Graduate traineeship amountLowest 25 Percent‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡48.0 ![17.82-79.71]1.4 !![0.08-20.85]—†50.6 ![20.25-80.48]—†—†—†—†Lower Middle 25 Percent‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡24.8 ![10.12-49.22]##—†75.2[50.78-89.88]—†—†—†—†Upper Middle 25 Percent‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡‡15.9 !![4.74-41.99]##—†84.1[58.01-95.26]—†—†—†—†Highest 25 Percent####3.7 !![0.18-46.29]96.3[53.71-99.82]2.2 !![0.23-18.17]##5.2 !![0.61-32.45]92.6[68.64-98.63]11.3 !![2.71-36.66]##—†88.7[63.34-97.29]—†—†—†—†— Not available.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.— Not available.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.STDERR-SOURCE-END— Not available.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.‡ Reporting standards not met.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: HIGHLVEX and GRTRNAMT.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: HIGHLVEX, GRINFEL, GRRESAMT, GRTEAAMT and GRTRNAMT. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: HIGHLVEX (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), GRINFEL (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), GRRESAMT (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016), GRTEAAMT (NPSAS:2004, NPSAS:2008, NPSAS:2012, NPSAS:2016) and GRTRNAMT (NPSAS:2004, NPSAS:2008, NPSAS:2012).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2003-04 National Postsecondary Student Aid Study (NPSAS:04), 2007-08 National Postsecondary Student Aid Study (NPSAS:08), 2011-12 National Postsecondary Student Aid Study (NPSAS:12) and 2015-16 National Postsecondary Student Aid Study (NPSAS:16).Computation by NCES TrendStats on 5/15/2018.bfebkphc3bfebkphc31Disciplinary occurrences: Racial/ethnic tensions with (Percent<3), Disciplinary occurrences: Student bullying with (Percent<3), Disciplinary occurrences: Sexual harassment of students with (Percent<3), Disciplinary occurrences: Student verbal abuse of teachers with (Percent<3) and Disciplinary occurrences: Widespread disorder in classrooms with (Percent<3) by School Level for years 2006, 2008, 2010 and 2016 Disciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classrooms(%<3)(%<3)(%<3)(%<3)(%<3)EstimatesTotal20062.824.53.59.52.320083.725.33.06.04.020102.823.13.24.82.520161.711.91.04.82.3School LevelPrimary20061.520.61.66.10.9 !20082.620.51.3 !3.73.120102.119.61.8 !3.41.9 !20161.2 !8.1#3.61.6 !Middle20066.043.08.616.05.320085.643.56.59.86.620105.438.66.16.84.120163.221.82.18.24.9High school20065.022.36.217.34.820085.321.75.712.14.820103.319.83.28.64.420162.314.72.57.62.6Combined20061.1 !!14.60.7 !!5.7 !2.1 !!20084.3 !24.93.2 !!2.9 !3.8 !!20101.1 !!18.67.5 !3.4 !!#20161.0 !!11.03.5 !!#1.0 !!Disciplinary occurrences: Racial/ethnic tensions with (Percent<3), Disciplinary occurrences: Student bullying with (Percent<3), Disciplinary occurrences: Sexual harassment of students with (Percent<3), Disciplinary occurrences: Student verbal abuse of teachers with (Percent<3) and Disciplinary occurrences: Widespread disorder in classrooms with (Percent<3) by School Level for years 2006, 2008, 2010 and 2016 Disciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classrooms(%<3)(%<3)(%<3)(%<3)(%<3)EstimatesTotal20062.824.53.59.52.320083.725.33.06.04.020102.823.13.24.82.520161.711.91.04.82.3School LevelPrimary20061.520.61.66.10.9 !20082.620.51.3 !3.73.120102.119.61.8 !3.41.9 !20161.2 !8.1#3.61.6 !Middle20066.043.08.616.05.320085.643.56.59.86.620105.438.66.16.84.120163.221.82.18.24.9High school20065.022.36.217.34.820085.321.75.712.14.820103.319.83.28.64.420162.314.72.57.62.6Combined20061.1 !!14.60.7 !!5.7 !2.1 !!20084.3 !24.93.2 !!2.9 !3.8 !!20101.1 !!18.67.5 !3.4 !!#20161.0 !!11.03.5 !!#1.0 !!Standard Error (BRR)Total20060.311.140.400.610.2420080.491.110.390.480.4520100.391.120.550.490.3720160.330.790.190.510.38School LevelPrimary20060.411.740.460.890.3120080.721.690.520.730.5920100.621.750.700.670.6020160.481.04†0.740.59Middle20060.801.940.921.210.6120080.751.390.761.010.9020100.811.600.890.830.6720160.691.590.441.130.67High school20060.661.630.781.150.8120080.691.450.781.200.7620100.561.410.581.000.8020160.641.370.551.240.52Combined20060.823.480.652.221.6020082.023.911.681.402.0820100.804.382.921.84†20161.033.172.13†1.03Relative Standard Error (%)Total200611.194.6611.346.4610.44200813.264.3712.998.0811.11201013.934.8617.1010.2714.60201619.256.6317.7810.6016.34School LevelPrimary200627.268.4428.3514.6336.11200827.648.2241.4819.3519.35201029.858.9139.6219.9432.56201639.5612.84†20.7137.12Middle200613.404.5010.647.5411.66200813.223.1911.7810.3813.66201015.064.1514.4912.2316.19201621.807.2920.9513.7813.68High school200613.217.3012.676.6116.76200812.976.7113.599.9915.77201017.017.1418.2811.5918.04201628.349.3021.6816.2520.25Combined200676.3123.8589.4939.0377.88200846.5215.6952.5948.3254.88201070.8923.5139.1054.06†2016101.7628.8360.46†101.60Weighted Sample Sizes (n/1,000s)Total200683.283.283.283.283.2200883.083.083.083.083.0201082.882.882.882.882.8201683.683.683.683.683.6School LevelPrimary200648.648.648.648.648.6200849.249.249.249.249.2201048.948.948.948.948.9201649.149.149.149.149.1Middle200615.515.515.515.515.5200815.315.315.315.315.3201015.315.315.315.315.3201615.615.615.615.615.6High school200611.711.711.711.711.7200811.911.911.911.911.9201012.212.212.212.212.2201612.812.812.812.812.8Combined20067.47.47.47.47.420086.66.66.66.66.620106.46.46.46.46.420166.26.26.26.26.2Disciplinary occurrences: Racial/ethnic tensions with (Percent<3), Disciplinary occurrences: Student bullying with (Percent<3), Disciplinary occurrences: Sexual harassment of students with (Percent<3), Disciplinary occurrences: Student verbal abuse of teachers with (Percent<3) and Disciplinary occurrences: Widespread disorder in classrooms with (Percent<3) by School Level for years 2006, 2008, 2010 and 2016 Disciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classrooms(%<3)(%<3)(%<3)(%<3)(%<3)Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal20062.8[2.15-3.40]24.5[22.19-26.78]3.5[2.70-4.29]9.5[8.26-10.72]2.3[1.85-2.83]20083.7[2.71-4.68]25.3[23.05-27.49]3.0[2.23-3.81]6.0[5.01-6.95]4.0[3.13-4.92]20102.8[2.02-3.58]23.1[20.81-25.31]3.2[2.11-4.32]4.8[3.79-5.76]2.5[1.77-3.25]20161.7[1.05-2.38]11.9[10.30-13.47]1.0[0.67-1.41]4.8[3.77-5.81]2.3[1.55-3.07]School LevelPrimary20061.5[0.67-2.31]20.6[17.08-24.06]1.6[0.70-2.57]6.1[4.29-7.86]0.9 ![0.23-1.47]20082.6[1.16-4.06]20.5[17.12-23.89]1.3 ![0.21-2.32]3.7[2.29-5.20]3.1[1.87-4.24]20102.1[0.83-3.33]19.6[16.08-23.10]1.8 ![0.36-3.16]3.4[2.01-4.69]1.9 ![0.64-3.07]20161.2 ![0.25-2.17]8.1[6.02-10.21]##3.6[2.09-5.06]1.6 ![0.41-2.79]Middle20066.0[4.37-7.59]43.0[39.13-46.91]8.6[6.77-10.46]16.0[13.61-18.46]5.3[4.02-6.48]20085.6[4.14-7.14]43.5[40.72-46.30]6.5[4.95-8.02]9.8[7.72-11.78]6.6[4.79-8.41]20105.4[3.77-7.03]38.6[35.43-41.86]6.1[4.35-7.92]6.8[5.14-8.49]4.1[2.78-5.45]20163.2[1.78-4.55]21.8[18.61-25.00]2.1[1.22-3.00]8.2[5.91-10.44]4.9[3.54-6.22]High school20065.0[3.65-6.28]22.3[19.07-25.62]6.2[4.61-7.75]17.3[15.04-19.65]4.8[3.19-6.43]20085.3[3.92-6.68]21.7[18.76-24.60]5.7[4.17-7.31]12.1[9.64-14.48]4.8[3.31-6.38]20103.3[2.17-4.41]19.8[16.96-22.63]3.2[2.01-4.34]8.6[6.63-10.65]4.4[2.82-6.02]20162.3[0.97-3.54]14.7[11.95-17.44]2.5[1.43-3.63]7.6[5.14-10.12]2.6[1.51-3.59]Combined20061.1 !![-0.58-2.73]14.6[7.61-21.61]0.7 !![-0.58-2.04]5.7 ![1.23-10.17]2.1 !![-1.16-5.27]20084.3 ![0.28-8.40]24.9[17.05-32.76]3.2 !![-0.18-6.57]2.9 ![0.08-5.72]3.8 !![-0.39-7.96]20101.1 !![-0.48-2.75]18.6[9.83-27.43]7.5 ![1.60-13.32]3.4 !![-0.29-7.11]##20161.0 !![-1.06-3.09]11.0[4.62-17.35]3.5 !![-0.76-7.82]##1.0 !![-1.06-3.09]2006200820102016 Disciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classroomsDisciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classroomsDisciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classroomsDisciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classrooms (%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)EstimatesTotal2.824.53.59.52.33.725.33.06.04.02.823.13.24.82.51.711.91.04.82.3School LevelPrimary1.520.61.66.10.9 !2.620.51.3 !3.73.12.119.61.8 !3.41.9 !1.2 !8.1#3.61.6 !Middle6.043.08.616.05.35.643.56.59.86.65.438.66.16.84.13.221.82.18.24.9High school5.022.36.217.34.85.321.75.712.14.83.319.83.28.64.42.314.72.57.62.6Combined1.1 !!14.60.7 !!5.7 !2.1 !!4.3 !24.93.2 !!2.9 !3.8 !!1.1 !!18.67.5 !3.4 !!#1.0 !!11.03.5 !!#1.0 !!2006200820102016 Disciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classroomsDisciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classroomsDisciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classroomsDisciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classrooms (%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)EstimatesTotal2.824.53.59.52.33.725.33.06.04.02.823.13.24.82.51.711.91.04.82.3School LevelPrimary1.520.61.66.10.9 !2.620.51.3 !3.73.12.119.61.8 !3.41.9 !1.2 !8.1#3.61.6 !Middle6.043.08.616.05.35.643.56.59.86.65.438.66.16.84.13.221.82.18.24.9High school5.022.36.217.34.85.321.75.712.14.83.319.83.28.64.42.314.72.57.62.6Combined1.1 !!14.60.7 !!5.7 !2.1 !!4.3 !24.93.2 !!2.9 !3.8 !!1.1 !!18.67.5 !3.4 !!#1.0 !!11.03.5 !!#1.0 !!Standard Error (BRR)Total0.311.140.400.610.240.491.110.390.480.450.391.120.550.490.370.330.790.190.510.38School LevelPrimary0.411.740.460.890.310.721.690.520.730.590.621.750.700.670.600.481.04†0.740.59Middle0.801.940.921.210.610.751.390.761.010.900.811.600.890.830.670.691.590.441.130.67High school0.661.630.781.150.810.691.450.781.200.760.561.410.581.000.800.641.370.551.240.52Combined0.823.480.652.221.602.023.911.681.402.080.804.382.921.84†1.033.172.13†1.03Relative Standard Error (%)Total11.194.6611.346.4610.4413.264.3712.998.0811.1113.934.8617.1010.2714.6019.256.6317.7810.6016.34School LevelPrimary27.268.4428.3514.6336.1127.648.2241.4819.3519.3529.858.9139.6219.9432.5639.5612.84†20.7137.12Middle13.404.5010.647.5411.6613.223.1911.7810.3813.6615.064.1514.4912.2316.1921.807.2920.9513.7813.68High school13.217.3012.676.6116.7612.976.7113.599.9915.7717.017.1418.2811.5918.0428.349.3021.6816.2520.25Combined76.3123.8589.4939.0377.8846.5215.6952.5948.3254.8870.8923.5139.1054.06†101.7628.8360.46†101.60Weighted Sample Sizes (n/1,000s)Total83.283.283.283.283.283.083.083.083.083.082.882.882.882.882.883.683.683.683.683.6School LevelPrimary48.648.648.648.648.649.249.249.249.249.248.948.948.948.948.949.149.149.149.149.1Middle15.515.515.515.515.515.315.315.315.315.315.315.315.315.315.315.615.615.615.615.6High school11.711.711.711.711.711.911.911.911.911.912.212.212.212.212.212.812.812.812.812.8Combined7.47.47.47.47.46.66.66.66.66.66.46.46.46.46.46.26.26.26.26.22006200820102016 Disciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classroomsDisciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classroomsDisciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classroomsDisciplinary occurrences: Racial/ethnic tensionsDisciplinary occurrences: Student bullyingDisciplinary occurrences: Sexual harassment of studentsDisciplinary occurrences: Student verbal abuse of teachersDisciplinary occurrences: Widespread disorder in classrooms (%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3)(%<3) Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal2.8[2.15-3.40]24.5[22.19-26.78]3.5[2.70-4.29]9.5[8.26-10.72]2.3[1.85-2.83]3.7[2.71-4.68]25.3[23.05-27.49]3.0[2.23-3.81]6.0[5.01-6.95]4.0[3.13-4.92]2.8[2.02-3.58]23.1[20.81-25.31]3.2[2.11-4.32]4.8[3.79-5.76]2.5[1.77-3.25]1.7[1.05-2.38]11.9[10.30-13.47]1.0[0.67-1.41]4.8[3.77-5.81]2.3[1.55-3.07]School LevelPrimary1.5[0.67-2.31]20.6[17.08-24.06]1.6[0.70-2.57]6.1[4.29-7.86]0.9 ![0.23-1.47]2.6[1.16-4.06]20.5[17.12-23.89]1.3 ![0.21-2.32]3.7[2.29-5.20]3.1[1.87-4.24]2.1[0.83-3.33]19.6[16.08-23.10]1.8 ![0.36-3.16]3.4[2.01-4.69]1.9 ![0.64-3.07]1.2 ![0.25-2.17]8.1[6.02-10.21]##3.6[2.09-5.06]1.6 ![0.41-2.79]Middle6.0[4.37-7.59]43.0[39.13-46.91]8.6[6.77-10.46]16.0[13.61-18.46]5.3[4.02-6.48]5.6[4.14-7.14]43.5[40.72-46.30]6.5[4.95-8.02]9.8[7.72-11.78]6.6[4.79-8.41]5.4[3.77-7.03]38.6[35.43-41.86]6.1[4.35-7.92]6.8[5.14-8.49]4.1[2.78-5.45]3.2[1.78-4.55]21.8[18.61-25.00]2.1[1.22-3.00]8.2[5.91-10.44]4.9[3.54-6.22]High school5.0[3.65-6.28]22.3[19.07-25.62]6.2[4.61-7.75]17.3[15.04-19.65]4.8[3.19-6.43]5.3[3.92-6.68]21.7[18.76-24.60]5.7[4.17-7.31]12.1[9.64-14.48]4.8[3.31-6.38]3.3[2.17-4.41]19.8[16.96-22.63]3.2[2.01-4.34]8.6[6.63-10.65]4.4[2.82-6.02]2.3[0.97-3.54]14.7[11.95-17.44]2.5[1.43-3.63]7.6[5.14-10.12]2.6[1.51-3.59]Combined1.1 !![-0.58-2.73]14.6[7.61-21.61]0.7 !![-0.58-2.04]5.7 ![1.23-10.17]2.1 !![-1.16-5.27]4.3 ![0.28-8.40]24.9[17.05-32.76]3.2 !![-0.18-6.57]2.9 ![0.08-5.72]3.8 !![-0.39-7.96]1.1 !![-0.48-2.75]18.6[9.83-27.43]7.5 ![1.60-13.32]3.4 !![-0.29-7.11]##1.0 !![-1.06-3.09]11.0[4.62-17.35]3.5 !![-0.76-7.82]##1.0 !![-1.06-3.09]# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: C0374, C0376, C0378, C0380, C0382 and FR_LVEL. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: C0374 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), C0376 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), C0378 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), C0380 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), C0382 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016) and FR_LVEL (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006, 2007–08 School Survey on Crime and Safety (SSOCS), 2008, 2009-10 School Survey on Crime and Safety (SSOCS), 2010 and 2015-16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES TrendStats on 7/9/2018.mgbkcha1mgbkcha12School has written plans for responding to at least one crisis situation with (Percent=1), School practice: Written plan for shootings with (Percent=1), School practice: Written plan for natural disasters with (Percent=1), School practice: Written plan for bomb threats with (Percent=1) and School practice: Written crisis plan for hostages with (Percent=1) by School Level, Enrollment Size and Locale for years 2006, 2008, 2010 and 2016 School has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostages(%=1)(%=1)(%=1)(%=1)(%=1)EstimatesTotal200698.079.395.094.573.1200899.283.095.893.871.3201099.084.395.193.574.32016——96.194.160.5School LevelPrimary200697.874.594.693.571.1200899.479.996.393.469.8201098.980.695.192.472.42016——96.492.557.1Middle200699.484.296.696.775.4200899.088.396.196.776.3201099.488.195.795.577.02016——96.396.562.6High school200698.886.995.596.677.2200899.290.694.396.076.0201099.291.494.696.577.42016——95.597.367.3Combined200694.888.493.492.975.0200897.580.194.686.362.7201098.489.294.891.876.42016——93.594.568.4Enrollment SizeLess than 300200694.174.089.589.167.8200897.775.793.688.361.5201097.383.393.390.474.22016——93.188.958.7300 - 499200699.277.896.996.076.0200899.581.196.393.770.6201099.981.196.694.772.52016——96.594.859.7500 - 999200699.282.097.196.472.9200899.987.096.996.976.5201099.286.094.694.075.22016——97.695.360.51,000 or more200699.486.395.697.078.3200899.090.395.695.676.7201099.389.496.295.476.32016——95.398.967.1LocaleCity200698.176.393.994.466.3200899.083.095.194.969.4201098.581.093.592.871.72016——96.693.663.3Suburb200699.581.296.597.177.3200899.484.996.396.974.7201099.283.494.093.773.72016——95.594.957.3Town200698.281.495.095.869.1200899.385.396.894.473.9201099.986.598.296.077.92016——96.696.254.5Rural200696.279.194.291.575.4200899.080.395.789.868.7201098.886.896.192.975.32016——95.992.864.7School has written plans for responding to at least one crisis situation with (Percent=1), School practice: Written plan for shootings with (Percent=1), School practice: Written plan for natural disasters with (Percent=1), School practice: Written plan for bomb threats with (Percent=1) and School practice: Written crisis plan for hostages with (Percent=1) by School Level, Enrollment Size and Locale for years 2006, 2008, 2010 and 2016 School has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostages(%=1)(%=1)(%=1)(%=1)(%=1)EstimatesTotal200698.079.395.094.573.1200899.283.095.893.871.3201099.084.395.193.574.32016——96.194.160.5School LevelPrimary200697.874.594.693.571.1200899.479.996.393.469.8201098.980.695.192.472.42016——96.492.557.1Middle200699.484.296.696.775.4200899.088.396.196.776.3201099.488.195.795.577.02016——96.396.562.6High school200698.886.995.596.677.2200899.290.694.396.076.0201099.291.494.696.577.42016——95.597.367.3Combined200694.888.493.492.975.0200897.580.194.686.362.7201098.489.294.891.876.42016——93.594.568.4Enrollment SizeLess than 300200694.174.089.589.167.8200897.775.793.688.361.5201097.383.393.390.474.22016——93.188.958.7300 - 499200699.277.896.996.076.0200899.581.196.393.770.6201099.981.196.694.772.52016——96.594.859.7500 - 999200699.282.097.196.472.9200899.987.096.996.976.5201099.286.094.694.075.22016——97.695.360.51,000 or more200699.486.395.697.078.3200899.090.395.695.676.7201099.389.496.295.476.32016——95.398.967.1LocaleCity200698.176.393.994.466.3200899.083.095.194.969.4201098.581.093.592.871.72016——96.693.663.3Suburb200699.581.296.597.177.3200899.484.996.396.974.7201099.283.494.093.773.72016——95.594.957.3Town200698.281.495.095.869.1200899.385.396.894.473.9201099.986.598.296.077.92016——96.696.254.5Rural200696.279.194.291.575.4200899.080.395.789.868.7201098.886.896.192.975.32016——95.992.864.7Standard Error (BRR)Total20060.431.310.650.651.1220080.291.310.480.651.2620100.291.100.540.661.202016††0.570.871.30School LevelPrimary20060.692.161.091.021.9820080.362.070.750.972.0620100.441.680.821.041.782016††0.861.362.07Middle20060.261.270.610.551.5320080.401.210.790.671.4120100.271.060.940.781.372016††0.790.871.73High school20060.511.390.760.881.4420080.461.070.790.901.5620100.351.160.921.061.692016††0.790.761.79Combined20062.283.532.322.313.2820081.824.552.184.225.3120101.614.162.532.954.412016††2.992.765.96Enrollment SizeLess than 30020061.643.442.162.363.0520081.023.401.742.473.8120101.142.711.711.822.832016††1.822.743.55300 - 49920060.352.050.810.992.1320080.402.270.951.622.5420100.102.250.801.092.412016††1.011.312.97500 - 99920060.331.420.520.691.8520080.081.360.650.721.8020100.331.330.870.891.492016††0.741.062.181,000 or more20060.481.670.950.951.7720080.601.440.871.032.1020100.551.530.861.132.092016††0.990.372.40LocaleCity20060.732.341.241.132.1220080.532.031.161.172.6420100.732.481.091.372.552016††1.031.832.93Suburb20060.271.630.820.731.5820080.411.880.930.821.9120100.411.941.121.382.112016††1.001.292.56Town20061.323.392.051.833.5820080.402.561.271.893.0020100.152.770.671.733.062016††1.481.553.87Rural20061.102.311.221.702.1420080.632.701.111.782.4420100.662.031.111.412.682016††1.231.792.84Relative Standard Error (%)Total20060.441.660.680.691.5420080.291.570.500.701.7620100.291.310.570.711.612016††0.590.922.14School LevelPrimary20060.702.901.151.092.7820080.362.590.781.042.9520100.442.090.861.132.462016††0.891.473.63Middle20060.261.510.630.572.0320080.411.370.820.691.8520100.271.200.990.811.782016††0.820.902.76High school20060.521.600.800.911.8720080.461.180.830.942.0520100.351.270.971.102.182016††0.830.782.66Combined20062.403.992.482.494.3720081.875.692.304.898.4720101.634.672.663.215.772016††3.202.928.70Enrollment SizeLess than 30020061.744.642.412.654.5020081.044.501.862.796.1920101.183.251.832.023.822016††1.953.086.05300 - 49920060.352.640.831.042.8120080.402.800.981.733.5920100.102.780.831.153.322016††1.041.394.97500 - 99920060.331.730.540.712.5420080.081.560.680.742.3520100.341.550.920.941.982016††0.761.113.601,000 or more20060.481.930.990.982.2620080.611.600.911.082.7420100.551.710.891.182.732016††1.040.383.57LocaleCity20060.743.061.321.193.2020080.542.441.221.233.8120100.753.061.161.473.562016††1.061.964.63Suburb20060.272.010.850.752.0420080.412.220.960.842.5620100.412.331.191.472.872016††1.051.364.46Town20061.344.162.161.915.1720080.403.001.312.004.0620100.153.200.681.803.932016††1.541.617.10Rural20061.142.931.301.862.8420080.633.361.161.993.5520100.672.341.151.523.562016††1.281.924.39Weighted Sample Sizes (n/1,000s)Total200683.283.283.283.283.2200883.083.083.083.083.0201082.882.882.882.882.82016††83.683.683.6School LevelPrimary200648.648.648.648.648.6200849.249.249.249.249.2201048.948.948.948.948.92016††49.149.149.1Middle200615.515.515.515.515.5200815.315.315.315.315.3201015.315.315.315.315.32016††15.615.615.6High school200611.711.711.711.711.7200811.911.911.911.911.9201012.212.212.212.212.22016††12.812.812.8Combined20067.47.47.47.47.420086.66.66.66.66.620106.46.46.46.46.42016††6.26.26.2Enrollment SizeLess than 300200620.820.820.820.820.8200819.219.219.219.219.2201018.918.918.918.918.92016††18.218.218.2300 - 499200623.823.823.823.823.8200824.324.324.324.324.3201025.225.225.225.225.22016††25.025.025.0500 - 999200629.329.329.329.329.3200830.230.230.230.230.2201029.829.829.829.829.82016††31.731.731.71,000 or more20069.39.39.39.39.320089.39.39.39.39.320108.98.98.98.98.92016††8.78.78.7LocaleCity200621.021.021.021.021.0200821.321.321.321.321.3201021.521.521.521.521.52016††22.822.822.8Suburb200627.627.627.627.627.6200823.923.923.923.923.9201023.823.823.823.823.82016††27.427.427.4Town20068.28.28.28.28.2200811.811.811.811.811.8201012.112.112.112.112.12016††11.011.011.0Rural200626.426.426.426.426.4200826.026.026.026.026.0201025.325.325.325.325.32016††22.522.522.5School has written plans for responding to at least one crisis situation with (Percent=1), School practice: Written plan for shootings with (Percent=1), School practice: Written plan for natural disasters with (Percent=1), School practice: Written plan for bomb threats with (Percent=1) and School practice: Written crisis plan for hostages with (Percent=1) by School Level, Enrollment Size and Locale for years 2006, 2008, 2010 and 2016 School has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostages(%=1)(%=1)(%=1)(%=1)(%=1)Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal200698.0[97.09-98.83]79.3[76.67-81.95]95.0[93.65-96.26]94.5[93.18-95.80]73.1[70.87-75.38]200899.2[98.58-99.74]83.0[80.41-85.66]95.8[94.87-96.80]93.8[92.50-95.13]71.3[68.81-73.87]201099.0[98.41-99.56]84.3[82.05-86.49]95.1[94.02-96.19]93.5[92.21-94.87]74.3[71.86-76.67]2016—†—†96.1[94.92-97.19]94.1[92.39-95.89]60.5[57.94-63.15]School LevelPrimary200697.8[96.39-99.15]74.5[70.20-78.87]94.6[92.37-96.73]93.5[91.46-95.57]71.1[67.15-75.11]200899.4[98.71-100.13]79.9[75.78-84.09]96.3[94.79-97.80]93.4[91.45-95.36]69.8[65.69-73.95]201098.9[97.98-99.74]80.6[77.26-84.03]95.1[93.44-96.73]92.4[90.34-94.54]72.4[68.77-75.93]2016—†—†96.4[94.70-98.17]92.5[89.80-95.26]57.1[52.96-61.29]Middle200699.4[98.90-99.94]84.2[81.64-86.74]96.6[95.35-97.78]96.7[95.63-97.84]75.4[72.36-78.51]200899.0[98.22-99.85]88.3[85.89-90.75]96.1[94.49-97.66]96.7[95.36-98.05]76.3[73.49-79.15]201099.4[98.87-99.96]88.1[85.97-90.24]95.7[93.77-97.56]95.5[93.90-97.02]77.0[74.27-79.77]2016—†—†96.3[94.73-97.90]96.5[94.72-98.22]62.6[59.17-66.11]High school200698.8[97.79-99.85]86.9[84.09-89.68]95.5[93.93-96.98]96.6[94.82-98.34]77.2[74.25-80.06]200899.2[98.24-100.08]90.6[88.49-92.81]94.3[92.76-95.91]96.0[94.17-97.78]76.0[72.88-79.16]201099.2[98.54-99.93]91.4[89.08-93.73]94.6[92.73-96.44]96.5[94.35-98.60]77.4[74.01-80.80]2016—†—†95.5[93.92-97.11]97.3[95.74-98.79]67.3[63.74-70.94]Combined200694.8[90.22-99.37]88.4[81.32-95.51]93.4[88.77-98.08]92.9[88.22-97.50]75.0[68.43-81.60]200897.5[93.85-101.18]80.1[70.94-89.24]94.6[90.20-98.94]86.3[77.83-94.78]62.7[52.00-73.32]201098.4[95.18-101.64]89.2[80.81-97.54]94.8[89.77-99.93]91.8[85.88-97.72]76.4[67.52-85.23]2016—†—†93.5[87.48-99.50]94.5[89.00-100.09]68.4[56.46-80.39]Enrollment SizeLess than 300200694.1[90.84-97.42]74.0[67.12-80.93]89.5[85.14-93.81]89.1[84.31-93.79]67.8[61.69-73.96]200897.7[95.66-99.74]75.7[68.85-82.52]93.6[90.08-97.08]88.3[83.30-93.21]61.5[53.88-69.19]201097.3[94.99-99.59]83.3[77.86-88.75]93.3[89.87-96.75]90.4[86.72-94.04]74.2[68.55-79.93]2016—†—†93.1[89.46-96.77]88.9[83.39-94.38]58.7[51.56-65.81]300 - 499200699.2[98.54-99.93]77.8[73.72-81.97]96.9[95.26-98.50]96.0[93.96-97.95]76.0[71.68-80.25]200899.5[98.67-100.26]81.1[76.56-85.68]96.3[94.45-98.25]93.7[90.48-97.00]70.6[65.52-75.71]201099.9[99.70-100.10]81.1[76.57-85.62]96.6[94.97-98.19]94.7[92.54-96.91]72.5[67.63-77.31]2016—†—†96.5[94.47-98.52]94.8[92.14-97.41]59.7[53.75-65.66]500 - 999200699.2[98.53-99.85]82.0[79.17-84.88]97.1[96.05-98.14]96.4[94.99-97.74]72.9[69.22-76.67]200899.9[99.74-100.05]87.0[84.27-89.73]96.9[95.60-98.24]96.9[95.42-98.31]76.5[72.91-80.13]201099.2[98.52-99.86]86.0[83.36-88.70]94.6[92.90-96.39]94.0[92.21-95.77]75.2[72.19-78.19]2016—†—†97.6[96.10-99.07]95.3[93.20-97.46]60.5[56.10-64.85]1,000 or more200699.4[98.46-100.39]86.3[83.00-89.69]95.6[93.67-97.49]97.0[95.11-98.93]78.3[74.76-81.86]200899.0[97.79-100.21]90.3[87.43-93.23]95.6[93.89-97.37]95.6[93.54-97.67]76.7[72.45-80.89]201099.3[98.19-100.38]89.4[86.31-92.44]96.2[94.51-97.95]95.4[93.14-97.67]76.3[72.13-80.51]2016—†—†95.3[93.36-97.33]98.9[98.18-99.68]67.1[62.31-71.95]LocaleCity200698.1[96.59-99.51]76.3[71.64-81.04]93.9[91.39-96.38]94.4[92.09-96.62]66.3[62.03-70.55]200899.0[97.97-100.11]83.0[78.97-87.11]95.1[92.74-97.40]94.9[92.56-97.26]69.4[64.06-74.67]201098.5[96.98-99.93]81.0[76.01-85.98]93.5[91.31-95.68]92.8[90.03-95.53]71.7[66.58-76.83]2016—†—†96.6[94.56-98.70]93.6[89.89-97.26]63.3[57.37-69.15]Suburb200699.5[98.97-100.05]81.2[77.91-84.45]96.5[94.83-98.12]97.1[95.59-98.53]77.3[74.18-80.52]200899.4[98.59-100.22]84.9[81.12-88.69]96.3[94.40-98.12]96.9[95.27-98.56]74.7[70.90-78.59]201099.2[98.38-100.02]83.4[79.49-87.28]94.0[91.72-96.21]93.7[90.93-96.47]73.7[69.47-77.96]2016—†—†95.5[93.45-97.47]94.9[92.28-97.45]57.3[52.21-62.48]Town200698.2[95.55-100.84]81.4[74.62-88.24]95.0[90.88-99.14]95.8[92.10-99.45]69.1[61.95-76.32]200899.3[98.55-100.15]85.3[80.21-90.49]96.8[94.21-99.30]94.4[90.58-98.15]73.9[67.84-79.89]201099.9[99.55-100.15]86.5[80.95-92.07]98.2[96.81-99.51]96.0[92.51-99.45]77.9[71.71-84.01]2016—†—†96.6[93.61-99.57]96.2[93.05-99.27]54.5[46.71-62.26]Rural200696.2[93.99-98.40]79.1[74.42-83.71]94.2[91.75-96.66]91.5[88.10-94.93]75.4[71.09-79.69]200899.0[97.70-100.21]80.3[74.84-85.67]95.7[93.43-97.89]89.8[86.24-93.41]68.7[63.79-73.59]201098.8[97.48-100.15]86.8[82.73-90.89]96.1[93.85-98.30]92.9[90.05-95.72]75.3[69.87-80.64]2016—†—†95.9[93.46-98.41]92.8[89.24-96.42]64.7[58.95-70.36]2006200820102016 School has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostagesSchool has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostagesSchool has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostagesSchool has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostages (%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)EstimatesTotal98.079.395.094.573.199.283.095.893.871.399.084.395.193.574.3——96.194.160.5School LevelPrimary97.874.594.693.571.199.479.996.393.469.898.980.695.192.472.4——96.492.557.1Middle99.484.296.696.775.499.088.396.196.776.399.488.195.795.577.0——96.396.562.6High school98.886.995.596.677.299.290.694.396.076.099.291.494.696.577.4——95.597.367.3Combined94.888.493.492.975.097.580.194.686.362.798.489.294.891.876.4——93.594.568.4Enrollment SizeLess than 30094.174.089.589.167.897.775.793.688.361.597.383.393.390.474.2——93.188.958.7300 - 49999.277.896.996.076.099.581.196.393.770.699.981.196.694.772.5——96.594.859.7500 - 99999.282.097.196.472.999.987.096.996.976.599.286.094.694.075.2——97.695.360.51,000 or more99.486.395.697.078.399.090.395.695.676.799.389.496.295.476.3——95.398.967.1LocaleCity98.176.393.994.466.399.083.095.194.969.498.581.093.592.871.7——96.693.663.3Suburb99.581.296.597.177.399.484.996.396.974.799.283.494.093.773.7——95.594.957.3Town98.281.495.095.869.199.385.396.894.473.999.986.598.296.077.9——96.696.254.5Rural96.279.194.291.575.499.080.395.789.868.798.886.896.192.975.3——95.992.864.72006200820102016 School has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostagesSchool has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostagesSchool has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostagesSchool has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostages (%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)EstimatesTotal98.079.395.094.573.199.283.095.893.871.399.084.395.193.574.3——96.194.160.5School LevelPrimary97.874.594.693.571.199.479.996.393.469.898.980.695.192.472.4——96.492.557.1Middle99.484.296.696.775.499.088.396.196.776.399.488.195.795.577.0——96.396.562.6High school98.886.995.596.677.299.290.694.396.076.099.291.494.696.577.4——95.597.367.3Combined94.888.493.492.975.097.580.194.686.362.798.489.294.891.876.4——93.594.568.4Enrollment SizeLess than 30094.174.089.589.167.897.775.793.688.361.597.383.393.390.474.2——93.188.958.7300 - 49999.277.896.996.076.099.581.196.393.770.699.981.196.694.772.5——96.594.859.7500 - 99999.282.097.196.472.999.987.096.996.976.599.286.094.694.075.2——97.695.360.51,000 or more99.486.395.697.078.399.090.395.695.676.799.389.496.295.476.3——95.398.967.1LocaleCity98.176.393.994.466.399.083.095.194.969.498.581.093.592.871.7——96.693.663.3Suburb99.581.296.597.177.399.484.996.396.974.799.283.494.093.773.7——95.594.957.3Town98.281.495.095.869.199.385.396.894.473.999.986.598.296.077.9——96.696.254.5Rural96.279.194.291.575.499.080.395.789.868.798.886.896.192.975.3——95.992.864.7Standard Error (BRR)Total0.431.310.650.651.120.291.310.480.651.260.291.100.540.661.20††0.570.871.30School LevelPrimary0.692.161.091.021.980.362.070.750.972.060.441.680.821.041.78††0.861.362.07Middle0.261.270.610.551.530.401.210.790.671.410.271.060.940.781.37††0.790.871.73High school0.511.390.760.881.440.461.070.790.901.560.351.160.921.061.69††0.790.761.79Combined2.283.532.322.313.281.824.552.184.225.311.614.162.532.954.41††2.992.765.96Enrollment SizeLess than 3001.643.442.162.363.051.023.401.742.473.811.142.711.711.822.83††1.822.743.55300 - 4990.352.050.810.992.130.402.270.951.622.540.102.250.801.092.41††1.011.312.97500 - 9990.331.420.520.691.850.081.360.650.721.800.331.330.870.891.49††0.741.062.181,000 or more0.481.670.950.951.770.601.440.871.032.100.551.530.861.132.09††0.990.372.40LocaleCity0.732.341.241.132.120.532.031.161.172.640.732.481.091.372.55††1.031.832.93Suburb0.271.630.820.731.580.411.880.930.821.910.411.941.121.382.11††1.001.292.56Town1.323.392.051.833.580.402.561.271.893.000.152.770.671.733.06††1.481.553.87Rural1.102.311.221.702.140.632.701.111.782.440.662.031.111.412.68††1.231.792.84Relative Standard Error (%)Total0.441.660.680.691.540.291.570.500.701.760.291.310.570.711.61††0.590.922.14School LevelPrimary0.702.901.151.092.780.362.590.781.042.950.442.090.861.132.46††0.891.473.63Middle0.261.510.630.572.030.411.370.820.691.850.271.200.990.811.78††0.820.902.76High school0.521.600.800.911.870.461.180.830.942.050.351.270.971.102.18††0.830.782.66Combined2.403.992.482.494.371.875.692.304.898.471.634.672.663.215.77††3.202.928.70Enrollment SizeLess than 3001.744.642.412.654.501.044.501.862.796.191.183.251.832.023.82††1.953.086.05300 - 4990.352.640.831.042.810.402.800.981.733.590.102.780.831.153.32††1.041.394.97500 - 9990.331.730.540.712.540.081.560.680.742.350.341.550.920.941.98††0.761.113.601,000 or more0.481.930.990.982.260.611.600.911.082.740.551.710.891.182.73††1.040.383.57LocaleCity0.743.061.321.193.200.542.441.221.233.810.753.061.161.473.56††1.061.964.63Suburb0.272.010.850.752.040.412.220.960.842.560.412.331.191.472.87††1.051.364.46Town1.344.162.161.915.170.403.001.312.004.060.153.200.681.803.93††1.541.617.10Rural1.142.931.301.862.840.633.361.161.993.550.672.341.151.523.56††1.281.924.39Weighted Sample Sizes (n/1,000s)Total83.283.283.283.283.283.083.083.083.083.082.882.882.882.882.8††83.683.683.6School LevelPrimary48.648.648.648.648.649.249.249.249.249.248.948.948.948.948.9††49.149.149.1Middle15.515.515.515.515.515.315.315.315.315.315.315.315.315.315.3††15.615.615.6High school11.711.711.711.711.711.911.911.911.911.912.212.212.212.212.2††12.812.812.8Combined7.47.47.47.47.46.66.66.66.66.66.46.46.46.46.4††6.26.26.2Enrollment SizeLess than 30020.820.820.820.820.819.219.219.219.219.218.918.918.918.918.9††18.218.218.2300 - 49923.823.823.823.823.824.324.324.324.324.325.225.225.225.225.2††25.025.025.0500 - 99929.329.329.329.329.330.230.230.230.230.229.829.829.829.829.8††31.731.731.71,000 or more9.39.39.39.39.39.39.39.39.39.38.98.98.98.98.9††8.78.78.7LocaleCity21.021.021.021.021.021.321.321.321.321.321.521.521.521.521.5††22.822.822.8Suburb27.627.627.627.627.623.923.923.923.923.923.823.823.823.823.8††27.427.427.4Town8.28.28.28.28.211.811.811.811.811.812.112.112.112.112.1††11.011.011.0Rural26.426.426.426.426.426.026.026.026.026.025.325.325.325.325.3††22.522.522.52006200820102016 School has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostagesSchool has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostagesSchool has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostagesSchool has written plans for responding to at least one crisis situationSchool practice: Written plan for shootingsSchool practice: Written plan for natural disastersSchool practice: Written plan for bomb threatsSchool practice: Written crisis plan for hostages (%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1)(%=1) Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal98.0[97.09-98.83]79.3[76.67-81.95]95.0[93.65-96.26]94.5[93.18-95.80]73.1[70.87-75.38]99.2[98.58-99.74]83.0[80.41-85.66]95.8[94.87-96.80]93.8[92.50-95.13]71.3[68.81-73.87]99.0[98.41-99.56]84.3[82.05-86.49]95.1[94.02-96.19]93.5[92.21-94.87]74.3[71.86-76.67]—†—†96.1[94.92-97.19]94.1[92.39-95.89]60.5[57.94-63.15]School LevelPrimary97.8[96.39-99.15]74.5[70.20-78.87]94.6[92.37-96.73]93.5[91.46-95.57]71.1[67.15-75.11]99.4[98.71-100.13]79.9[75.78-84.09]96.3[94.79-97.80]93.4[91.45-95.36]69.8[65.69-73.95]98.9[97.98-99.74]80.6[77.26-84.03]95.1[93.44-96.73]92.4[90.34-94.54]72.4[68.77-75.93]—†—†96.4[94.70-98.17]92.5[89.80-95.26]57.1[52.96-61.29]Middle99.4[98.90-99.94]84.2[81.64-86.74]96.6[95.35-97.78]96.7[95.63-97.84]75.4[72.36-78.51]99.0[98.22-99.85]88.3[85.89-90.75]96.1[94.49-97.66]96.7[95.36-98.05]76.3[73.49-79.15]99.4[98.87-99.96]88.1[85.97-90.24]95.7[93.77-97.56]95.5[93.90-97.02]77.0[74.27-79.77]—†—†96.3[94.73-97.90]96.5[94.72-98.22]62.6[59.17-66.11]High school98.8[97.79-99.85]86.9[84.09-89.68]95.5[93.93-96.98]96.6[94.82-98.34]77.2[74.25-80.06]99.2[98.24-100.08]90.6[88.49-92.81]94.3[92.76-95.91]96.0[94.17-97.78]76.0[72.88-79.16]99.2[98.54-99.93]91.4[89.08-93.73]94.6[92.73-96.44]96.5[94.35-98.60]77.4[74.01-80.80]—†—†95.5[93.92-97.11]97.3[95.74-98.79]67.3[63.74-70.94]Combined94.8[90.22-99.37]88.4[81.32-95.51]93.4[88.77-98.08]92.9[88.22-97.50]75.0[68.43-81.60]97.5[93.85-101.18]80.1[70.94-89.24]94.6[90.20-98.94]86.3[77.83-94.78]62.7[52.00-73.32]98.4[95.18-101.64]89.2[80.81-97.54]94.8[89.77-99.93]91.8[85.88-97.72]76.4[67.52-85.23]—†—†93.5[87.48-99.50]94.5[89.00-100.09]68.4[56.46-80.39]Enrollment SizeLess than 30094.1[90.84-97.42]74.0[67.12-80.93]89.5[85.14-93.81]89.1[84.31-93.79]67.8[61.69-73.96]97.7[95.66-99.74]75.7[68.85-82.52]93.6[90.08-97.08]88.3[83.30-93.21]61.5[53.88-69.19]97.3[94.99-99.59]83.3[77.86-88.75]93.3[89.87-96.75]90.4[86.72-94.04]74.2[68.55-79.93]—†—†93.1[89.46-96.77]88.9[83.39-94.38]58.7[51.56-65.81]300 - 49999.2[98.54-99.93]77.8[73.72-81.97]96.9[95.26-98.50]96.0[93.96-97.95]76.0[71.68-80.25]99.5[98.67-100.26]81.1[76.56-85.68]96.3[94.45-98.25]93.7[90.48-97.00]70.6[65.52-75.71]99.9[99.70-100.10]81.1[76.57-85.62]96.6[94.97-98.19]94.7[92.54-96.91]72.5[67.63-77.31]—†—†96.5[94.47-98.52]94.8[92.14-97.41]59.7[53.75-65.66]500 - 99999.2[98.53-99.85]82.0[79.17-84.88]97.1[96.05-98.14]96.4[94.99-97.74]72.9[69.22-76.67]99.9[99.74-100.05]87.0[84.27-89.73]96.9[95.60-98.24]96.9[95.42-98.31]76.5[72.91-80.13]99.2[98.52-99.86]86.0[83.36-88.70]94.6[92.90-96.39]94.0[92.21-95.77]75.2[72.19-78.19]—†—†97.6[96.10-99.07]95.3[93.20-97.46]60.5[56.10-64.85]1,000 or more99.4[98.46-100.39]86.3[83.00-89.69]95.6[93.67-97.49]97.0[95.11-98.93]78.3[74.76-81.86]99.0[97.79-100.21]90.3[87.43-93.23]95.6[93.89-97.37]95.6[93.54-97.67]76.7[72.45-80.89]99.3[98.19-100.38]89.4[86.31-92.44]96.2[94.51-97.95]95.4[93.14-97.67]76.3[72.13-80.51]—†—†95.3[93.36-97.33]98.9[98.18-99.68]67.1[62.31-71.95]LocaleCity98.1[96.59-99.51]76.3[71.64-81.04]93.9[91.39-96.38]94.4[92.09-96.62]66.3[62.03-70.55]99.0[97.97-100.11]83.0[78.97-87.11]95.1[92.74-97.40]94.9[92.56-97.26]69.4[64.06-74.67]98.5[96.98-99.93]81.0[76.01-85.98]93.5[91.31-95.68]92.8[90.03-95.53]71.7[66.58-76.83]—†—†96.6[94.56-98.70]93.6[89.89-97.26]63.3[57.37-69.15]Suburb99.5[98.97-100.05]81.2[77.91-84.45]96.5[94.83-98.12]97.1[95.59-98.53]77.3[74.18-80.52]99.4[98.59-100.22]84.9[81.12-88.69]96.3[94.40-98.12]96.9[95.27-98.56]74.7[70.90-78.59]99.2[98.38-100.02]83.4[79.49-87.28]94.0[91.72-96.21]93.7[90.93-96.47]73.7[69.47-77.96]—†—†95.5[93.45-97.47]94.9[92.28-97.45]57.3[52.21-62.48]Town98.2[95.55-100.84]81.4[74.62-88.24]95.0[90.88-99.14]95.8[92.10-99.45]69.1[61.95-76.32]99.3[98.55-100.15]85.3[80.21-90.49]96.8[94.21-99.30]94.4[90.58-98.15]73.9[67.84-79.89]99.9[99.55-100.15]86.5[80.95-92.07]98.2[96.81-99.51]96.0[92.51-99.45]77.9[71.71-84.01]—†—†96.6[93.61-99.57]96.2[93.05-99.27]54.5[46.71-62.26]Rural96.2[93.99-98.40]79.1[74.42-83.71]94.2[91.75-96.66]91.5[88.10-94.93]75.4[71.09-79.69]99.0[97.70-100.21]80.3[74.84-85.67]95.7[93.43-97.89]89.8[86.24-93.41]68.7[63.79-73.59]98.8[97.48-100.15]86.8[82.73-90.89]96.1[93.85-98.30]92.9[90.05-95.72]75.3[69.87-80.64]—†—†95.9[93.46-98.41]92.8[89.24-96.42]64.7[58.95-70.36]— Not available.— Not available.† Not applicable.STDERR-SOURCE-END— Not available.† Not applicable.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: ANYWRITTEN.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: ANYWRITTEN, C0154, C0158, C0166, C0162, FR_LVEL, FR_SIZE and FR_URBAN. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: ANYWRITTEN (SSOCS:2006, SSOCS:2008, SSOCS:2010), C0154 (SSOCS:2006, SSOCS:2008, SSOCS:2010), C0158 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), C0166 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), C0162 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_LVEL (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_SIZE (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_LOC4 (SSOCS:2006) and FR_URBAN (SSOCS:2008, SSOCS:2010, SSOCS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006, 2007–08 School Survey on Crime and Safety (SSOCS), 2008, 2009-10 School Survey on Crime and Safety (SSOCS), 2010 and 2015-16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES TrendStats on 7/9/2018.mgbkcf1mgbkcf13Average Average number of full-time security guards, SROs, or sworn law enforcement officers and Average Average number of part-time security guards, SROs, or sworn law enforcement officers by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School) and Urbanicity - Based on Urban-centric location of school - from CCD (School) for years 2008, 2010 and 2016 Average number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officers(Avg)(Avg)EstimatesTotal20081.91.220100.70.520160.80.5School grades offered - based on CCD frame variables (School)Primary20081.1 !1.220100.20.320160.30.4Middle20081.41.220101.00.720161.00.6High school20083.81.220102.41.020162.61.0Combined20081.40.920100.50.420160.50.3School size categories - based on CCD frame variables (School)Less than 30020081.21.420100.20.220160.30.4300 - 49920081.21.020100.30.420160.40.4500 - 99920081.71.120100.60.620160.80.61,000 or more20083.41.420103.01.220163.00.9Urbanicity - Based on Urban-centric location of school - from CCD (School)City20082.71.220101.20.620161.20.6Suburb20081.81.320100.80.620160.80.6Town20081.21.220100.40.420160.70.6Rural20081.41.020100.40.420160.40.3Average Average number of full-time security guards, SROs, or sworn law enforcement officers and Average Average number of part-time security guards, SROs, or sworn law enforcement officers by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School) and Urbanicity - Based on Urban-centric location of school - from CCD (School) for years 2008, 2010 and 2016 Average number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officers(Avg)(Avg)EstimatesTotal20081.91.220100.70.520160.80.5School grades offered - based on CCD frame variables (School)Primary20081.1 !1.220100.20.320160.30.4Middle20081.41.220101.00.720161.00.6High school20083.81.220102.41.020162.61.0Combined20081.40.920100.50.420160.50.3School size categories - based on CCD frame variables (School)Less than 30020081.21.420100.20.220160.30.4300 - 49920081.21.020100.30.420160.40.4500 - 99920081.71.120100.60.620160.80.61,000 or more20083.41.420103.01.220163.00.9Urbanicity - Based on Urban-centric location of school - from CCD (School)City20082.71.220101.20.620161.20.6Suburb20081.81.320100.80.620160.80.6Town20081.21.220100.40.420160.70.6Rural20081.41.020100.40.420160.40.3Standard Error (BRR)Total20080.170.0620100.030.0320160.050.03School grades offered - based on CCD frame variables (School)Primary20080.350.0920100.030.0320160.040.03Middle20080.080.1420100.080.0920160.100.06High school20080.360.0720100.120.1120160.210.13Combined20080.220.1620100.090.1120160.100.06School size categories - based on CCD frame variables (School)Less than 30020080.210.2920100.040.0620160.060.08300 - 49920080.360.1020100.040.0420160.050.04500 - 99920080.360.0820100.050.0720160.090.051,000 or more20080.150.1120100.130.0920160.130.09Urbanicity - Based on Urban-centric location of school - from CCD (School)City20080.490.1120100.070.0620160.130.06Suburb20080.110.1520100.060.0620160.060.06Town20080.170.1120100.040.0420160.160.08Rural20080.340.0820100.050.0820160.050.04Relative Standard Error (%)Total20088.924.7320103.865.6220166.125.33School grades offered - based on CCD frame variables (School)Primary200830.697.48201011.309.62201612.978.14Middle20085.5211.3820108.5912.36201610.0510.20High school20089.575.9720105.2110.9220168.1312.79Combined200816.1117.96201018.7926.58201618.8120.41School size categories - based on CCD frame variables (School)Less than 300200818.1420.64201015.9923.94201618.7023.28300 - 499200829.0910.92201012.9811.98201611.949.91500 - 999200821.176.8120108.0010.69201611.537.551,000 or more20084.317.5820104.137.3720164.2810.36Urbanicity - Based on Urban-centric location of school - from CCD (School)City200818.068.9520105.3510.63201611.0210.26Suburb20086.4510.9520107.3310.0620167.6810.08Town200814.029.3120109.8212.12201622.9914.86Rural200824.897.99201013.3118.25201611.6811.30Weighted Sample Sizes (n/1,000s)Total200838.438.4201082.882.8201683.683.6School grades offered - based on CCD frame variables (School)Primary200816.316.3201048.948.9201649.149.1Middle200810.010.0201015.315.3201615.615.6High school20089.59.5201012.212.2201612.812.8Combined20082.62.620106.46.420166.26.2School size categories - based on CCD frame variables (School)Less than 30020085.35.3201018.918.9201618.218.2300 - 49920088.88.8201025.225.2201625.025.0500 - 999200815.915.9201029.829.8201631.731.71,000 or more20088.48.420108.98.920168.78.7Urbanicity - Based on Urban-centric location of school - from CCD (School)City200812.212.2201021.521.5201622.822.8Suburb200810.910.9201023.823.8201627.427.4Town20086.06.0201012.112.1201611.011.0Rural20089.49.4201025.325.3201622.522.5Average Average number of full-time security guards, SROs, or sworn law enforcement officers and Average Average number of part-time security guards, SROs, or sworn law enforcement officers by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School) and Urbanicity - Based on Urban-centric location of school - from CCD (School) for years 2008, 2010 and 2016 Average number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officers(Avg)(Avg)Amt.95% CIAmt.95% CIEstimatesTotal20081.9[1.54-2.22]1.2[1.07-1.29]20100.7[0.65-0.76]0.5[0.46-0.57]20160.8[0.70-0.89]0.5[0.47-0.58]School grades offered - based on CCD frame variables (School)Primary20081.1 ![0.44-1.85]1.2[1.00-1.36]20100.2[0.18-0.29]0.3[0.28-0.41]20160.3[0.20-0.35]0.4[0.34-0.47]Middle20081.4[1.26-1.57]1.2[0.93-1.48]20101.0[0.82-1.16]0.7[0.55-0.91]20161.0[0.82-1.24]0.6[0.47-0.71]High school20083.8[3.05-4.50]1.2[1.09-1.39]20102.4[2.13-2.63]1.0[0.76-1.18]20162.6[2.21-3.07]1.0[0.74-1.25]Combined20081.4[0.92-1.80]0.9[0.56-1.20]20100.5[0.30-0.65]0.4[0.19-0.62]20160.5[0.33-0.73]0.3[0.17-0.41]School size categories - based on CCD frame variables (School)Less than 30020081.2[0.73-1.57]1.4[0.82-1.97]20100.2[0.16-0.31]0.2[0.12-0.35]20160.3[0.21-0.45]0.4[0.19-0.53]300 - 49920081.2[0.52-1.97]1.0[0.75-1.16]20100.3[0.24-0.41]0.4[0.28-0.46]20160.4[0.31-0.50]0.4[0.32-0.49]500 - 99920081.7[0.97-2.40]1.1[0.97-1.27]20100.6[0.53-0.73]0.6[0.48-0.75]20160.8[0.59-0.94]0.6[0.51-0.70]1,000 or more20083.4[3.07-3.66]1.4[1.19-1.61]20103.0[2.78-3.29]1.2[0.99-1.33]20163.0[2.75-3.27]0.9[0.70-1.06]Urbanicity - Based on Urban-centric location of school - from CCD (School)City20082.7[1.73-3.70]1.2[0.98-1.42]20101.2[1.09-1.36]0.6[0.48-0.74]20161.2[0.93-1.46]0.6[0.44-0.67]Suburb20081.8[1.53-1.98]1.3[1.05-1.64]20100.8[0.67-0.90]0.6[0.47-0.70]20160.8[0.70-0.96]0.6[0.49-0.74]Town20081.2[0.85-1.51]1.2[0.94-1.37]20100.4[0.29-0.44]0.4[0.27-0.44]20160.7[0.38-1.05]0.6[0.40-0.74]Rural20081.4[0.69-2.08]1.0[0.83-1.15]20100.4[0.26-0.45]0.4[0.28-0.61]20160.4[0.30-0.48]0.3[0.27-0.42]200820102016 Average number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officersAverage number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officersAverage number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officers (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)EstimatesTotal1.91.20.70.50.80.5School grades offered - based on CCD frame variables (School)Primary1.1 !1.20.20.30.30.4Middle1.41.21.00.71.00.6High school3.81.22.41.02.61.0Combined1.40.90.50.40.50.3School size categories - based on CCD frame variables (School)Less than 3001.21.40.20.20.30.4300 - 4991.21.00.30.40.40.4500 - 9991.71.10.60.60.80.61,000 or more3.41.43.01.23.00.9Urbanicity - Based on Urban-centric location of school - from CCD (School)City2.71.21.20.61.20.6Suburb1.81.30.80.60.80.6Town1.21.20.40.40.70.6Rural1.41.00.40.40.40.3200820102016 Average number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officersAverage number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officersAverage number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officers (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)EstimatesTotal1.91.20.70.50.80.5School grades offered - based on CCD frame variables (School)Primary1.1 !1.20.20.30.30.4Middle1.41.21.00.71.00.6High school3.81.22.41.02.61.0Combined1.40.90.50.40.50.3School size categories - based on CCD frame variables (School)Less than 3001.21.40.20.20.30.4300 - 4991.21.00.30.40.40.4500 - 9991.71.10.60.60.80.61,000 or more3.41.43.01.23.00.9Urbanicity - Based on Urban-centric location of school - from CCD (School)City2.71.21.20.61.20.6Suburb1.81.30.80.60.80.6Town1.21.20.40.40.70.6Rural1.41.00.40.40.40.3Standard Error (BRR)Total0.170.060.030.030.050.03School grades offered - based on CCD frame variables (School)Primary0.350.090.030.030.040.03Middle0.080.140.080.090.100.06High school0.360.070.120.110.210.13Combined0.220.160.090.110.100.06School size categories - based on CCD frame variables (School)Less than 3000.210.290.040.060.060.08300 - 4990.360.100.040.040.050.04500 - 9990.360.080.050.070.090.051,000 or more0.150.110.130.090.130.09Urbanicity - Based on Urban-centric location of school - from CCD (School)City0.490.110.070.060.130.06Suburb0.110.150.060.060.060.06Town0.170.110.040.040.160.08Rural0.340.080.050.080.050.04Relative Standard Error (%)Total8.924.733.865.626.125.33School grades offered - based on CCD frame variables (School)Primary30.697.4811.309.6212.978.14Middle5.5211.388.5912.3610.0510.20High school9.575.975.2110.928.1312.79Combined16.1117.9618.7926.5818.8120.41School size categories - based on CCD frame variables (School)Less than 30018.1420.6415.9923.9418.7023.28300 - 49929.0910.9212.9811.9811.949.91500 - 99921.176.818.0010.6911.537.551,000 or more4.317.584.137.374.2810.36Urbanicity - Based on Urban-centric location of school - from CCD (School)City18.068.955.3510.6311.0210.26Suburb6.4510.957.3310.067.6810.08Town14.029.319.8212.1222.9914.86Rural24.897.9913.3118.2511.6811.30Weighted Sample Sizes (n/1,000s)Total38.438.482.882.883.683.6School grades offered - based on CCD frame variables (School)Primary16.316.348.948.949.149.1Middle10.010.015.315.315.615.6High school9.59.512.212.212.812.8Combined2.62.66.46.46.26.2School size categories - based on CCD frame variables (School)Less than 3005.35.318.918.918.218.2300 - 4998.88.825.225.225.025.0500 - 99915.915.929.829.831.731.71,000 or more8.48.48.98.98.78.7Urbanicity - Based on Urban-centric location of school - from CCD (School)City12.212.221.521.522.822.8Suburb10.910.923.823.827.427.4Town6.06.012.112.111.011.0Rural9.49.425.325.322.522.5200820102016 Average number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officersAverage number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officersAverage number of full-time security guards, SROs, or sworn law enforcement officersAverage number of part-time security guards, SROs, or sworn law enforcement officers (Avg)(Avg)(Avg)(Avg)(Avg)(Avg) Amt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIEstimatesTotal1.9[1.54-2.22]1.2[1.07-1.29]0.7[0.65-0.76]0.5[0.46-0.57]0.8[0.70-0.89]0.5[0.47-0.58]School grades offered - based on CCD frame variables (School)Primary1.1 ![0.44-1.85]1.2[1.00-1.36]0.2[0.18-0.29]0.3[0.28-0.41]0.3[0.20-0.35]0.4[0.34-0.47]Middle1.4[1.26-1.57]1.2[0.93-1.48]1.0[0.82-1.16]0.7[0.55-0.91]1.0[0.82-1.24]0.6[0.47-0.71]High school3.8[3.05-4.50]1.2[1.09-1.39]2.4[2.13-2.63]1.0[0.76-1.18]2.6[2.21-3.07]1.0[0.74-1.25]Combined1.4[0.92-1.80]0.9[0.56-1.20]0.5[0.30-0.65]0.4[0.19-0.62]0.5[0.33-0.73]0.3[0.17-0.41]School size categories - based on CCD frame variables (School)Less than 3001.2[0.73-1.57]1.4[0.82-1.97]0.2[0.16-0.31]0.2[0.12-0.35]0.3[0.21-0.45]0.4[0.19-0.53]300 - 4991.2[0.52-1.97]1.0[0.75-1.16]0.3[0.24-0.41]0.4[0.28-0.46]0.4[0.31-0.50]0.4[0.32-0.49]500 - 9991.7[0.97-2.40]1.1[0.97-1.27]0.6[0.53-0.73]0.6[0.48-0.75]0.8[0.59-0.94]0.6[0.51-0.70]1,000 or more3.4[3.07-3.66]1.4[1.19-1.61]3.0[2.78-3.29]1.2[0.99-1.33]3.0[2.75-3.27]0.9[0.70-1.06]Urbanicity - Based on Urban-centric location of school - from CCD (School)City2.7[1.73-3.70]1.2[0.98-1.42]1.2[1.09-1.36]0.6[0.48-0.74]1.2[0.93-1.46]0.6[0.44-0.67]Suburb1.8[1.53-1.98]1.3[1.05-1.64]0.8[0.67-0.90]0.6[0.47-0.70]0.8[0.70-0.96]0.6[0.49-0.74]Town1.2[0.85-1.51]1.2[0.94-1.37]0.4[0.29-0.44]0.4[0.27-0.44]0.7[0.38-1.05]0.6[0.40-0.74]Rural1.4[0.69-2.08]1.0[0.83-1.15]0.4[0.26-0.45]0.4[0.28-0.61]0.4[0.30-0.48]0.3[0.27-0.42]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: SEC_FT and SEC_PT.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: SEC_FT, SEC_PT, FR_LVEL, FR_SIZE and FR_URBAN. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: SEC_FT (SSOCS:2008), SEC_PT (SSOCS:2008), FR_LVEL (SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_SIZE (SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_URBAN (SSOCS:2008, SSOCS:2010, SSOCS:2016), SEC_FT10 (SSOCS:2010), SEC_PT10 (SSOCS:2010), SEC_FT16 (SSOCS:2016) and SEC_PT16 (SSOCS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2007–08 School Survey on Crime and Safety (SSOCS), 2008, 2009-10 School Survey on Crime and Safety (SSOCS), 2010 and 2015-16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES TrendStats on 7/9/2018.mgbkcgaamgbkcgaa4Total number of violent incidents recorded with (Percent>0.5) by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Total number of violent incidents recorded(%>0.5)EstimatesTotal200677.7200875.5201073.8201668.9School grades offered - based on CCD frame variables (School)Primary200667.3200865.1201064.4201657.2Middle200694.4200894.3201090.5201688.0High school200695.2200894.0201090.9201689.8Combined200683.5200875.5201073.7201671.1School size categories - based on CCD frame variables (School)Less than 300200663.7200860.6201062.8201652.6300 - 499200677.3200869.1201071.3201663.0500 - 999200682.1200883.4201076.4201676.01,000 or more200696.5200897.0201095.4201694.5Urbanicity - Based on Urban-centric location of school - from CCD (School)City200682.3200882.1201074.9201674.0Suburb200678.2200873.7201073.5201666.4Town200682.2200880.0201080.3201677.7Rural200672.3200869.5201070.2201662.7Level of crime where students liveHigh level of crime200688.2200885.3201091.5201679.8Moderate level of crime200685.0200882.6201080.3201678.7Low level of crime200672.7200870.8201068.4201664.6Students come from areas with very different levels of crime200684.3200879.5201077.3201664.9Total number of violent incidents recorded with (Percent>0.5) by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Total number of violent incidents recorded(%>0.5)EstimatesTotal200677.7200875.5201073.8201668.9School grades offered - based on CCD frame variables (School)Primary200667.3200865.1201064.4201657.2Middle200694.4200894.3201090.5201688.0High school200695.2200894.0201090.9201689.8Combined200683.5200875.5201073.7201671.1School size categories - based on CCD frame variables (School)Less than 300200663.7200860.6201062.8201652.6300 - 499200677.3200869.1201071.3201663.0500 - 999200682.1200883.4201076.4201676.01,000 or more200696.5200897.0201095.4201694.5Urbanicity - Based on Urban-centric location of school - from CCD (School)City200682.3200882.1201074.9201674.0Suburb200678.2200873.7201073.5201666.4Town200682.2200880.0201080.3201677.7Rural200672.3200869.5201070.2201662.7Level of crime where students liveHigh level of crime200688.2200885.3201091.5201679.8Moderate level of crime200685.0200882.6201080.3201678.7Low level of crime200672.7200870.8201068.4201664.6Students come from areas with very different levels of crime200684.3200879.5201077.3201664.9Standard Error (BRR)Total20061.1120081.0920101.0720161.30School grades offered - based on CCD frame variables (School)Primary20061.7520081.6420101.6320162.04Middle20060.8520080.8820101.1020161.15High school20060.9220081.0720101.2120161.53Combined20063.6420084.5020105.3320165.52School size categories - based on CCD frame variables (School)Less than 30020063.2920083.5320103.2520163.81300 - 49920062.0820082.7520102.3420162.96500 - 99920061.3820081.6920101.7520162.031,000 or more20061.0320081.0820101.2220161.37Urbanicity - Based on Urban-centric location of school - from CCD (School)City20061.7720082.0120102.1220162.71Suburb20061.8720082.1720102.2120162.47Town20063.5020082.7920103.1420163.69Rural20062.6920082.1320101.9120162.82Level of crime where students liveHigh level of crime20063.6020083.9020103.2020164.48Moderate level of crime20062.3620082.9320102.6720162.94Low level of crime20061.6420081.6820101.5420161.93Students come from areas with very different levels of crime20062.4120083.7520102.9120164.03Relative Standard Error (%)Total20061.4320081.4420101.4420161.89School grades offered - based on CCD frame variables (School)Primary20062.6020082.5320102.5320163.57Middle20060.9020080.9320101.2220161.31High school20060.9620081.1320101.3320161.70Combined20064.3520085.9620107.2320167.76School size categories - based on CCD frame variables (School)Less than 30020065.1720085.8220105.1820167.25300 - 49920062.7020083.9820103.2820164.70500 - 99920061.6820082.0220102.2920162.671,000 or more20061.0620081.1120101.2820161.46Urbanicity - Based on Urban-centric location of school - from CCD (School)City20062.1520082.4520102.8320163.67Suburb20062.3920082.9520103.0120163.72Town20064.2520083.4920103.9120164.75Rural20063.7220083.0720102.7220164.49Level of crime where students liveHigh level of crime20064.0820084.5720103.5020165.61Moderate level of crime20062.7720083.5520103.3320163.74Low level of crime20062.2620082.3720102.2520162.98Students come from areas with very different levels of crime20062.8620084.7120103.7720166.20Weighted Sample Sizes (n/1,000s)Total200683.2200883.0201082.8201683.6School grades offered - based on CCD frame variables (School)Primary200648.6200849.2201048.9201649.1Middle200615.5200815.3201015.3201615.6High school200611.7200811.9201012.2201612.8Combined20067.420086.620106.420166.2School size categories - based on CCD frame variables (School)Less than 300200620.8200819.2201018.9201618.2300 - 499200623.8200824.3201025.2201625.0500 - 999200629.3200830.2201029.8201631.71,000 or more20069.320089.320108.920168.7Urbanicity - Based on Urban-centric location of school - from CCD (School)City200621.0200821.3201021.5201622.8Suburb200627.6200823.9201023.8201627.4Town20068.2200811.8201012.1201611.0Rural200626.4200826.0201025.3201622.5Level of crime where students liveHigh level of crime20066.520086.220105.920167.4Moderate level of crime200615.9200817.1201018.4201617.5Low level of crime200650.3200849.2201047.7201648.4Students come from areas with very different levels of crime200610.5200810.5201010.7201610.4Total number of violent incidents recorded with (Percent>0.5) by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Total number of violent incidents recorded(%>0.5)Pct.95% CIEstimatesTotal200677.7[75.43-79.89]200875.5[73.21-77.57]201073.8[71.64-75.92]201668.9[66.26-71.49]School grades offered - based on CCD frame variables (School)Primary200667.3[63.71-70.73]200865.1[61.71-68.31]201064.4[61.04-67.58]201657.2[53.04-61.22]Middle200694.4[92.42-95.89]200894.3[92.28-95.86]201090.5[88.08-92.53]201688.0[85.48-90.11]High school200695.2[92.95-96.71]200894.0[91.44-95.80]201090.9[88.14-93.02]201689.8[86.26-92.46]Combined200683.5[74.89-89.61]200875.5[65.40-83.42]201073.7[61.75-82.97]201671.1[58.95-80.86]School size categories - based on CCD frame variables (School)Less than 300200663.7[56.85-70.01]200860.6[53.37-67.47]201062.8[56.09-69.08]201652.6[44.92-60.12]300 - 499200677.3[72.87-81.24]200869.1[63.30-74.30]201071.3[66.43-75.81]201663.0[56.88-68.73]500 - 999200682.1[79.17-84.72]200883.4[79.71-86.50]201076.4[72.74-79.78]201676.0[71.69-79.83]1,000 or more200696.5[93.72-98.05]200897.0[93.90-98.55]201095.4[92.25-97.33]201694.5[90.95-96.65]Urbanicity - Based on Urban-centric location of school - from CCD (School)City200682.3[78.47-85.61]200882.1[77.74-85.84]201074.9[70.40-78.92]201674.0[68.18-79.07]Suburb200678.2[74.17-81.68]200873.7[69.10-77.81]201073.5[68.78-77.66]201666.4[61.25-71.15]Town200682.2[74.10-88.21]200880.0[73.83-85.05]201080.3[73.25-85.89]201677.7[69.43-84.23]Rural200672.3[66.55-77.33]200869.5[65.10-73.65]201070.2[66.23-73.90]201662.7[56.85-68.13]Level of crime where students liveHigh level of crime200688.2[78.85-93.72]200885.3[75.67-91.59]201091.5[82.49-96.12]201679.8[69.35-87.38]Moderate level of crime200685.0[79.60-89.11]200882.6[75.94-87.76]201080.3[74.35-85.10]201678.7[72.17-83.98]Low level of crime200672.7[69.31-75.90]200870.8[67.36-74.10]201068.4[65.22-71.38]201664.6[60.65-68.38]Students come from areas with very different levels of crime200684.3[78.80-88.52]200879.5[70.94-86.00]201077.3[70.93-82.63]201664.9[56.48-72.55]2006200820102016 Total number of violent incidents recordedTotal number of violent incidents recordedTotal number of violent incidents recordedTotal number of violent incidents recorded (%>0.5)(%>0.5)(%>0.5)(%>0.5)EstimatesTotal77.775.573.868.9School grades offered - based on CCD frame variables (School)Primary67.365.164.457.2Middle94.494.390.588.0High school95.294.090.989.8Combined83.575.573.771.1School size categories - based on CCD frame variables (School)Less than 30063.760.662.852.6300 - 49977.369.171.363.0500 - 99982.183.476.476.01,000 or more96.597.095.494.5Urbanicity - Based on Urban-centric location of school - from CCD (School)City82.382.174.974.0Suburb78.273.773.566.4Town82.280.080.377.7Rural72.369.570.262.7Level of crime where students liveHigh level of crime88.285.391.579.8Moderate level of crime85.082.680.378.7Low level of crime72.770.868.464.6Students come from areas with very different levels of crime84.379.577.364.92006200820102016 Total number of violent incidents recordedTotal number of violent incidents recordedTotal number of violent incidents recordedTotal number of violent incidents recorded (%>0.5)(%>0.5)(%>0.5)(%>0.5)EstimatesTotal77.775.573.868.9School grades offered - based on CCD frame variables (School)Primary67.365.164.457.2Middle94.494.390.588.0High school95.294.090.989.8Combined83.575.573.771.1School size categories - based on CCD frame variables (School)Less than 30063.760.662.852.6300 - 49977.369.171.363.0500 - 99982.183.476.476.01,000 or more96.597.095.494.5Urbanicity - Based on Urban-centric location of school - from CCD (School)City82.382.174.974.0Suburb78.273.773.566.4Town82.280.080.377.7Rural72.369.570.262.7Level of crime where students liveHigh level of crime88.285.391.579.8Moderate level of crime85.082.680.378.7Low level of crime72.770.868.464.6Students come from areas with very different levels of crime84.379.577.364.9Standard Error (BRR)Total1.111.091.071.30School grades offered - based on CCD frame variables (School)Primary1.751.641.632.04Middle0.850.881.101.15High school0.921.071.211.53Combined3.644.505.335.52School size categories - based on CCD frame variables (School)Less than 3003.293.533.253.81300 - 4992.082.752.342.96500 - 9991.381.691.752.031,000 or more1.031.081.221.37Urbanicity - Based on Urban-centric location of school - from CCD (School)City1.772.012.122.71Suburb1.872.172.212.47Town3.502.793.143.69Rural2.692.131.912.82Level of crime where students liveHigh level of crime3.603.903.204.48Moderate level of crime2.362.932.672.94Low level of crime1.641.681.541.93Students come from areas with very different levels of crime2.413.752.914.03Relative Standard Error (%)Total1.431.441.441.89School grades offered - based on CCD frame variables (School)Primary2.602.532.533.57Middle0.900.931.221.31High school0.961.131.331.70Combined4.355.967.237.76School size categories - based on CCD frame variables (School)Less than 3005.175.825.187.25300 - 4992.703.983.284.70500 - 9991.682.022.292.671,000 or more1.061.111.281.46Urbanicity - Based on Urban-centric location of school - from CCD (School)City2.152.452.833.67Suburb2.392.953.013.72Town4.253.493.914.75Rural3.723.072.724.49Level of crime where students liveHigh level of crime4.084.573.505.61Moderate level of crime2.773.553.333.74Low level of crime2.262.372.252.98Students come from areas with very different levels of crime2.864.713.776.20Weighted Sample Sizes (n/1,000s)Total83.283.082.883.6School grades offered - based on CCD frame variables (School)Primary48.649.248.949.1Middle15.515.315.315.6High school11.711.912.212.8Combined7.46.66.46.2School size categories - based on CCD frame variables (School)Less than 30020.819.218.918.2300 - 49923.824.325.225.0500 - 99929.330.229.831.71,000 or more9.39.38.98.7Urbanicity - Based on Urban-centric location of school - from CCD (School)City21.021.321.522.8Suburb27.623.923.827.4Town8.211.812.111.0Rural26.426.025.322.5Level of crime where students liveHigh level of crime6.56.25.97.4Moderate level of crime15.917.118.417.5Low level of crime50.349.247.748.4Students come from areas with very different levels of crime10.510.510.710.42006200820102016 Total number of violent incidents recordedTotal number of violent incidents recordedTotal number of violent incidents recordedTotal number of violent incidents recorded (%>0.5)(%>0.5)(%>0.5)(%>0.5) Pct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal77.7[75.43-79.89]75.5[73.21-77.57]73.8[71.64-75.92]68.9[66.26-71.49]School grades offered - based on CCD frame variables (School)Primary67.3[63.71-70.73]65.1[61.71-68.31]64.4[61.04-67.58]57.2[53.04-61.22]Middle94.4[92.42-95.89]94.3[92.28-95.86]90.5[88.08-92.53]88.0[85.48-90.11]High school95.2[92.95-96.71]94.0[91.44-95.80]90.9[88.14-93.02]89.8[86.26-92.46]Combined83.5[74.89-89.61]75.5[65.40-83.42]73.7[61.75-82.97]71.1[58.95-80.86]School size categories - based on CCD frame variables (School)Less than 30063.7[56.85-70.01]60.6[53.37-67.47]62.8[56.09-69.08]52.6[44.92-60.12]300 - 49977.3[72.87-81.24]69.1[63.30-74.30]71.3[66.43-75.81]63.0[56.88-68.73]500 - 99982.1[79.17-84.72]83.4[79.71-86.50]76.4[72.74-79.78]76.0[71.69-79.83]1,000 or more96.5[93.72-98.05]97.0[93.90-98.55]95.4[92.25-97.33]94.5[90.95-96.65]Urbanicity - Based on Urban-centric location of school - from CCD (School)City82.3[78.47-85.61]82.1[77.74-85.84]74.9[70.40-78.92]74.0[68.18-79.07]Suburb78.2[74.17-81.68]73.7[69.10-77.81]73.5[68.78-77.66]66.4[61.25-71.15]Town82.2[74.10-88.21]80.0[73.83-85.05]80.3[73.25-85.89]77.7[69.43-84.23]Rural72.3[66.55-77.33]69.5[65.10-73.65]70.2[66.23-73.90]62.7[56.85-68.13]Level of crime where students liveHigh level of crime88.2[78.85-93.72]85.3[75.67-91.59]91.5[82.49-96.12]79.8[69.35-87.38]Moderate level of crime85.0[79.60-89.11]82.6[75.94-87.76]80.3[74.35-85.10]78.7[72.17-83.98]Low level of crime72.7[69.31-75.90]70.8[67.36-74.10]68.4[65.22-71.38]64.6[60.65-68.38]Students come from areas with very different levels of crime84.3[78.80-88.52]79.5[70.94-86.00]77.3[70.93-82.63]64.9[56.48-72.55]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: VIOINC, FR_LVEL, FR_SIZE, FR_URBAN and C0560. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: VIOINC06 (SSOCS:2006), FR_LVEL (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_SIZE (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_LOC4 (SSOCS:2006), C0560 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), VIOINC08 (SSOCS:2008), FR_URBAN (SSOCS:2008, SSOCS:2010, SSOCS:2016), VIOINC10 (SSOCS:2010) and VIOINC16 (SSOCS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006, 2007–08 School Survey on Crime and Safety (SSOCS), 2008, 2009-10 School Survey on Crime and Safety (SSOCS), 2010 and 2015-16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES TrendStats on 7/9/2018.mgbkcf15mgbkcf155Average Total number of disciplinary actions recorded for distribution, possession, or use of alcohol and Average Total number of disciplinary actions recorded for distribution, possession, or use of illegal drugs by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Total number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugs(Avg)(Avg)EstimatesTotal20060.61.620080.51.420100.51.720160.41.5School grades offered - based on CCD frame variables (School)Primary2006#0.1 !!2008#0.1 !2010#0.12016#0.1 !Middle20060.52.020080.41.920100.52.020160.31.4High school20063.07.420082.36.720102.57.920161.77.3Combined20060.91.720080.41.120100.41.620160.5 !1.2School size categories - based on CCD frame variables (School)Less than 30020060.30.420080.10.4 !!20100.10.320160.1 !0.3300 - 49920060.20.320080.10.320100.20.520160.10.5500 - 99920060.31.320080.41.020100.41.220160.31.01,000 or more20063.18.620082.57.920102.99.720161.98.8Urbanicity - Based on Urban-centric location of school - from CCD (School)City20060.72.120080.61.820100.72.520160.41.9Suburb20060.61.820080.51.420100.61.820160.41.7Town20060.51.520080.51.220100.51.520160.41.7Rural20060.51.120080.41.320100.41.020160.30.7Level of crime where students liveHigh level of crime20060.41.820080.52.7 !20100.73.120160.52.5Moderate level of crime20060.62.020080.51.720100.62.020160.32.2Low level of crime20060.61.320080.41.120100.41.220160.41.1Students come from areas with very different levels of crime20060.82.320080.61.820100.72.420160.41.8Average Total number of disciplinary actions recorded for distribution, possession, or use of alcohol and Average Total number of disciplinary actions recorded for distribution, possession, or use of illegal drugs by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Total number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugs(Avg)(Avg)EstimatesTotal20060.61.620080.51.420100.51.720160.41.5School grades offered - based on CCD frame variables (School)Primary2006#0.1 !!2008#0.1 !2010#0.12016#0.1 !Middle20060.52.020080.41.920100.52.020160.31.4High school20063.07.420082.36.720102.57.920161.77.3Combined20060.91.720080.41.120100.41.620160.5 !1.2School size categories - based on CCD frame variables (School)Less than 30020060.30.420080.10.4 !!20100.10.320160.1 !0.3300 - 49920060.20.320080.10.320100.20.520160.10.5500 - 99920060.31.320080.41.020100.41.220160.31.01,000 or more20063.18.620082.57.920102.99.720161.98.8Urbanicity - Based on Urban-centric location of school - from CCD (School)City20060.72.120080.61.820100.72.520160.41.9Suburb20060.61.820080.51.420100.61.820160.41.7Town20060.51.520080.51.220100.51.520160.41.7Rural20060.51.120080.41.320100.41.020160.30.7Level of crime where students liveHigh level of crime20060.41.820080.52.7 !20100.73.120160.52.5Moderate level of crime20060.62.020080.51.720100.62.020160.32.2Low level of crime20060.61.320080.41.120100.41.220160.41.1Students come from areas with very different levels of crime20060.82.320080.61.820100.72.420160.41.8Standard Error (BRR)Total20060.030.0720080.020.0820100.030.0720160.020.08School grades offered - based on CCD frame variables (School)Primary2006†0.042008†0.022010†0.012016†0.02Middle20060.040.1220080.040.3320100.050.1520160.030.11High school20060.140.3320080.120.2820100.180.3820160.110.54Combined20060.180.2720080.110.2020100.090.2820160.170.20School size categories - based on CCD frame variables (School)Less than 30020060.070.0820080.020.2720100.030.0620160.040.09300 - 49920060.050.0520080.030.0420100.030.0620160.030.06500 - 99920060.030.1020080.040.0720100.060.1020160.030.081,000 or more20060.180.3520080.160.3420100.220.5420160.150.70Urbanicity - Based on Urban-centric location of school - from CCD (School)City20060.070.1420080.040.1320100.070.2220160.040.13Suburb20060.040.0820080.050.0820100.050.1020160.030.20Town20060.060.1420080.060.1220100.100.1520160.060.14Rural20060.070.0920080.030.2020100.050.0820160.050.07Level of crime where students liveHigh level of crime20060.100.2620080.080.9020100.120.5420160.110.48Moderate level of crime20060.070.1620080.060.1520100.090.2220160.040.30Low level of crime20060.050.0920080.030.0620100.040.0820160.030.07Students come from areas with very different levels of crime20060.130.2420080.080.1820100.110.2520160.080.32Relative Standard Error (%)Total20065.294.0920084.485.3020105.694.0820165.775.31School grades offered - based on CCD frame variables (School)Primary2006†55.152008†31.812010†26.942016†30.10Middle20069.115.8120088.0916.9120109.697.51201610.017.76High school20064.564.4220085.314.1620107.174.8320166.307.37Combined200619.5615.67200826.4217.10201022.5117.67201632.0316.81School size categories - based on CCD frame variables (School)Less than 300200625.0419.61200822.4362.15201027.6820.08201630.5428.32300 - 499200625.1513.73200826.2911.49201017.4513.92201618.0611.70500 - 99920068.648.2320089.726.58201014.747.9720169.907.451,000 or more20065.784.0420086.424.3220107.545.5820167.907.98Urbanicity - Based on Urban-centric location of school - from CCD (School)City200610.106.7820087.916.9220109.558.83201610.946.81Suburb20066.934.7220088.905.7620108.845.6920169.0111.17Town200612.349.05200812.2610.42201018.929.70201614.458.25Rural200612.888.0620088.6015.89201014.608.41201615.869.47Level of crime where students liveHigh level of crime200627.6714.61200818.7233.03201017.8317.37201622.9819.44Moderate level of crime200612.947.99200810.488.40201014.4010.82201613.1913.38Low level of crime20068.076.6420086.345.8320108.256.7220169.716.16Students come from areas with very different levels of crime200615.0110.57200813.8210.06201014.5510.44201618.5718.40Weighted Sample Sizes (n/1,000s)Total200683.283.2200883.083.0201082.882.8201683.683.6School grades offered - based on CCD frame variables (School)Primary200648.648.6200849.249.2201048.948.9201649.149.1Middle200615.515.5200815.315.3201015.315.3201615.615.6High school200611.711.7200811.911.9201012.212.2201612.812.8Combined20067.47.420086.66.620106.46.420166.26.2School size categories - based on CCD frame variables (School)Less than 300200620.820.8200819.219.2201018.918.9201618.218.2300 - 499200623.823.8200824.324.3201025.225.2201625.025.0500 - 999200629.329.3200830.230.2201029.829.8201631.731.71,000 or more20069.39.320089.39.320108.98.920168.78.7Urbanicity - Based on Urban-centric location of school - from CCD (School)City200621.021.0200821.321.3201021.521.5201622.822.8Suburb200627.627.6200823.923.9201023.823.8201627.427.4Town20068.28.2200811.811.8201012.112.1201611.011.0Rural200626.426.4200826.026.0201025.325.3201622.522.5Level of crime where students liveHigh level of crime20066.56.520086.26.220105.95.920167.47.4Moderate level of crime200615.915.9200817.117.1201018.418.4201617.517.5Low level of crime200650.350.3200849.249.2201047.747.7201648.448.4Students come from areas with very different levels of crime200610.510.5200810.510.5201010.710.7201610.410.4Average Total number of disciplinary actions recorded for distribution, possession, or use of alcohol and Average Total number of disciplinary actions recorded for distribution, possession, or use of illegal drugs by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Total number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugs(Avg)(Avg)Amt.95% CIAmt.95% CIEstimatesTotal20060.6[0.53-0.66]1.6[1.48-1.75]20080.5[0.43-0.52]1.4[1.29-1.60]20100.5[0.47-0.59]1.7[1.55-1.83]20160.4[0.32-0.41]1.5[1.36-1.68]School grades offered - based on CCD frame variables (School)Primary2006#[0.00-0.02]0.1 !![-0.01-0.15]2008#[0.02-0.07]0.1 ![0.02-0.08]2010#[0.01-0.07]0.1[0.03-0.08]2016#[0.00-0.03]0.1 ![0.03-0.12]Middle20060.5[0.37-0.53]2.0[1.79-2.26]20080.4[0.36-0.50]1.9[1.27-2.58]20100.5[0.43-0.64]2.0[1.71-2.32]20160.3[0.25-0.38]1.4[1.22-1.67]High school20063.0[2.74-3.29]7.4[6.77-8.09]20082.3[2.09-2.58]6.7[6.16-7.28]20102.5[2.18-2.91]7.9[7.09-8.62]20161.7[1.50-1.93]7.3[6.24-8.41]Combined20060.9[0.56-1.29]1.7[1.17-2.25]20080.4[0.19-0.61]1.1[0.75-1.54]20100.4[0.21-0.57]1.6[1.03-2.16]20160.5 ![0.19-0.86]1.2[0.77-1.56]School size categories - based on CCD frame variables (School)Less than 30020060.3[0.14-0.44]0.4[0.25-0.57]20080.1[0.05-0.14]0.4 !![-0.11-0.97]20100.1[0.05-0.16]0.3[0.16-0.39]20160.1 ![0.05-0.21]0.3[0.14-0.50]300 - 49920060.2[0.09-0.28]0.3[0.25-0.45]20080.1[0.05-0.16]0.3[0.24-0.39]20100.2[0.11-0.23]0.5[0.33-0.59]20160.1[0.09-0.20]0.5[0.38-0.62]500 - 99920060.3[0.29-0.41]1.3[1.06-1.48]20080.4[0.31-0.46]1.0[0.86-1.13]20100.4[0.27-0.50]1.2[1.01-1.40]20160.3[0.21-0.32]1.0[0.86-1.16]1,000 or more20063.1[2.77-3.50]8.6[7.94-9.35]20082.5[2.18-2.82]7.9[7.24-8.62]20102.9[2.48-3.36]9.7[8.63-10.81]20161.9[1.58-2.17]8.8[7.40-10.23]Urbanicity - Based on Urban-centric location of school - from CCD (School)City20060.7[0.54-0.81]2.1[1.84-2.42]20080.6[0.47-0.65]1.8[1.57-2.08]20100.7[0.55-0.82]2.5[2.02-2.89]20160.4[0.30-0.47]1.9[1.66-2.19]Suburb20060.6[0.55-0.73]1.8[1.60-1.93]20080.5[0.42-0.60]1.4[1.24-1.57]20100.6[0.49-0.70]1.8[1.60-2.01]20160.4[0.29-0.41]1.7[1.35-2.14]Town20060.5[0.36-0.60]1.5[1.22-1.77]20080.5[0.35-0.57]1.2[0.95-1.45]20100.5[0.31-0.70]1.5[1.24-1.84]20160.4[0.30-0.55]1.7[1.41-1.97]Rural20060.5[0.39-0.66]1.1[0.91-1.26]20080.4[0.31-0.44]1.3[0.87-1.68]20100.4[0.25-0.46]1.0[0.83-1.16]20160.3[0.23-0.45]0.7[0.60-0.89]Level of crime where students liveHigh level of crime20060.4[0.16-0.57]1.8[1.24-2.27]20080.5[0.28-0.62]2.7 ![0.91-4.52]20100.7[0.43-0.90]3.1[2.04-4.22]20160.5[0.25-0.68]2.5[1.50-3.43]Moderate level of crime20060.6[0.41-0.70]2.0[1.69-2.33]20080.5[0.43-0.66]1.7[1.45-2.04]20100.6[0.45-0.82]2.0[1.57-2.45]20160.3[0.24-0.41]2.2[1.63-2.83]Low level of crime20060.6[0.49-0.68]1.3[1.16-1.51]20080.4[0.37-0.48]1.1[0.98-1.24]20100.4[0.36-0.50]1.2[1.07-1.40]20160.4[0.29-0.43]1.1[0.93-1.20]Students come from areas with very different levels of crime20060.8[0.59-1.10]2.3[1.79-2.76]20080.6[0.42-0.75]1.8[1.40-2.12]20100.7[0.52-0.95]2.4[1.87-2.86]20160.4[0.26-0.56]1.8[1.11-2.41]2006200820102016 Total number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugsTotal number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugsTotal number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugsTotal number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugs (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)EstimatesTotal0.61.60.51.40.51.70.41.5School grades offered - based on CCD frame variables (School)Primary#0.1 !!#0.1 !#0.1#0.1 !Middle0.52.00.41.90.52.00.31.4High school3.07.42.36.72.57.91.77.3Combined0.91.70.41.10.41.60.5 !1.2School size categories - based on CCD frame variables (School)Less than 3000.30.40.10.4 !!0.10.30.1 !0.3300 - 4990.20.30.10.30.20.50.10.5500 - 9990.31.30.41.00.41.20.31.01,000 or more3.18.62.57.92.99.71.98.8Urbanicity - Based on Urban-centric location of school - from CCD (School)City0.72.10.61.80.72.50.41.9Suburb0.61.80.51.40.61.80.41.7Town0.51.50.51.20.51.50.41.7Rural0.51.10.41.30.41.00.30.7Level of crime where students liveHigh level of crime0.41.80.52.7 !0.73.10.52.5Moderate level of crime0.62.00.51.70.62.00.32.2Low level of crime0.61.30.41.10.41.20.41.1Students come from areas with very different levels of crime0.82.30.61.80.72.40.41.82006200820102016 Total number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugsTotal number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugsTotal number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugsTotal number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugs (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)EstimatesTotal0.61.60.51.40.51.70.41.5School grades offered - based on CCD frame variables (School)Primary#0.1 !!#0.1 !#0.1#0.1 !Middle0.52.00.41.90.52.00.31.4High school3.07.42.36.72.57.91.77.3Combined0.91.70.41.10.41.60.5 !1.2School size categories - based on CCD frame variables (School)Less than 3000.30.40.10.4 !!0.10.30.1 !0.3300 - 4990.20.30.10.30.20.50.10.5500 - 9990.31.30.41.00.41.20.31.01,000 or more3.18.62.57.92.99.71.98.8Urbanicity - Based on Urban-centric location of school - from CCD (School)City0.72.10.61.80.72.50.41.9Suburb0.61.80.51.40.61.80.41.7Town0.51.50.51.20.51.50.41.7Rural0.51.10.41.30.41.00.30.7Level of crime where students liveHigh level of crime0.41.80.52.7 !0.73.10.52.5Moderate level of crime0.62.00.51.70.62.00.32.2Low level of crime0.61.30.41.10.41.20.41.1Students come from areas with very different levels of crime0.82.30.61.80.72.40.41.8Standard Error (BRR)Total0.030.070.020.080.030.070.020.08School grades offered - based on CCD frame variables (School)Primary†0.04†0.02†0.01†0.02Middle0.040.120.040.330.050.150.030.11High school0.140.330.120.280.180.380.110.54Combined0.180.270.110.200.090.280.170.20School size categories - based on CCD frame variables (School)Less than 3000.070.080.020.270.030.060.040.09300 - 4990.050.050.030.040.030.060.030.06500 - 9990.030.100.040.070.060.100.030.081,000 or more0.180.350.160.340.220.540.150.70Urbanicity - Based on Urban-centric location of school - from CCD (School)City0.070.140.040.130.070.220.040.13Suburb0.040.080.050.080.050.100.030.20Town0.060.140.060.120.100.150.060.14Rural0.070.090.030.200.050.080.050.07Level of crime where students liveHigh level of crime0.100.260.080.900.120.540.110.48Moderate level of crime0.070.160.060.150.090.220.040.30Low level of crime0.050.090.030.060.040.080.030.07Students come from areas with very different levels of crime0.130.240.080.180.110.250.080.32Relative Standard Error (%)Total5.294.094.485.305.694.085.775.31School grades offered - based on CCD frame variables (School)Primary†55.15†31.81†26.94†30.10Middle9.115.818.0916.919.697.5110.017.76High school4.564.425.314.167.174.836.307.37Combined19.5615.6726.4217.1022.5117.6732.0316.81School size categories - based on CCD frame variables (School)Less than 30025.0419.6122.4362.1527.6820.0830.5428.32300 - 49925.1513.7326.2911.4917.4513.9218.0611.70500 - 9998.648.239.726.5814.747.979.907.451,000 or more5.784.046.424.327.545.587.907.98Urbanicity - Based on Urban-centric location of school - from CCD (School)City10.106.787.916.929.558.8310.946.81Suburb6.934.728.905.768.845.699.0111.17Town12.349.0512.2610.4218.929.7014.458.25Rural12.888.068.6015.8914.608.4115.869.47Level of crime where students liveHigh level of crime27.6714.6118.7233.0317.8317.3722.9819.44Moderate level of crime12.947.9910.488.4014.4010.8213.1913.38Low level of crime8.076.646.345.838.256.729.716.16Students come from areas with very different levels of crime15.0110.5713.8210.0614.5510.4418.5718.40Weighted Sample Sizes (n/1,000s)Total83.283.283.083.082.882.883.683.6School grades offered - based on CCD frame variables (School)Primary48.648.649.249.248.948.949.149.1Middle15.515.515.315.315.315.315.615.6High school11.711.711.911.912.212.212.812.8Combined7.47.46.66.66.46.46.26.2School size categories - based on CCD frame variables (School)Less than 30020.820.819.219.218.918.918.218.2300 - 49923.823.824.324.325.225.225.025.0500 - 99929.329.330.230.229.829.831.731.71,000 or more9.39.39.39.38.98.98.78.7Urbanicity - Based on Urban-centric location of school - from CCD (School)City21.021.021.321.321.521.522.822.8Suburb27.627.623.923.923.823.827.427.4Town8.28.211.811.812.112.111.011.0Rural26.426.426.026.025.325.322.522.5Level of crime where students liveHigh level of crime6.56.56.26.25.95.97.47.4Moderate level of crime15.915.917.117.118.418.417.517.5Low level of crime50.350.349.249.247.747.748.448.4Students come from areas with very different levels of crime10.510.510.510.510.710.710.410.42006200820102016 Total number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugsTotal number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugsTotal number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugsTotal number of disciplinary actions recorded for distribution, possession, or use of alcoholTotal number of disciplinary actions recorded for distribution, possession, or use of illegal drugs (Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg)(Avg) Amt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIEstimatesTotal0.6[0.53-0.66]1.6[1.48-1.75]0.5[0.43-0.52]1.4[1.29-1.60]0.5[0.47-0.59]1.7[1.55-1.83]0.4[0.32-0.41]1.5[1.36-1.68]School grades offered - based on CCD frame variables (School)Primary#[0.00-0.02]0.1 !![-0.01-0.15]#[0.02-0.07]0.1 ![0.02-0.08]#[0.01-0.07]0.1[0.03-0.08]#[0.00-0.03]0.1 ![0.03-0.12]Middle0.5[0.37-0.53]2.0[1.79-2.26]0.4[0.36-0.50]1.9[1.27-2.58]0.5[0.43-0.64]2.0[1.71-2.32]0.3[0.25-0.38]1.4[1.22-1.67]High school3.0[2.74-3.29]7.4[6.77-8.09]2.3[2.09-2.58]6.7[6.16-7.28]2.5[2.18-2.91]7.9[7.09-8.62]1.7[1.50-1.93]7.3[6.24-8.41]Combined0.9[0.56-1.29]1.7[1.17-2.25]0.4[0.19-0.61]1.1[0.75-1.54]0.4[0.21-0.57]1.6[1.03-2.16]0.5 ![0.19-0.86]1.2[0.77-1.56]School size categories - based on CCD frame variables (School)Less than 3000.3[0.14-0.44]0.4[0.25-0.57]0.1[0.05-0.14]0.4 !![-0.11-0.97]0.1[0.05-0.16]0.3[0.16-0.39]0.1 ![0.05-0.21]0.3[0.14-0.50]300 - 4990.2[0.09-0.28]0.3[0.25-0.45]0.1[0.05-0.16]0.3[0.24-0.39]0.2[0.11-0.23]0.5[0.33-0.59]0.1[0.09-0.20]0.5[0.38-0.62]500 - 9990.3[0.29-0.41]1.3[1.06-1.48]0.4[0.31-0.46]1.0[0.86-1.13]0.4[0.27-0.50]1.2[1.01-1.40]0.3[0.21-0.32]1.0[0.86-1.16]1,000 or more3.1[2.77-3.50]8.6[7.94-9.35]2.5[2.18-2.82]7.9[7.24-8.62]2.9[2.48-3.36]9.7[8.63-10.81]1.9[1.58-2.17]8.8[7.40-10.23]Urbanicity - Based on Urban-centric location of school - from CCD (School)City0.7[0.54-0.81]2.1[1.84-2.42]0.6[0.47-0.65]1.8[1.57-2.08]0.7[0.55-0.82]2.5[2.02-2.89]0.4[0.30-0.47]1.9[1.66-2.19]Suburb0.6[0.55-0.73]1.8[1.60-1.93]0.5[0.42-0.60]1.4[1.24-1.57]0.6[0.49-0.70]1.8[1.60-2.01]0.4[0.29-0.41]1.7[1.35-2.14]Town0.5[0.36-0.60]1.5[1.22-1.77]0.5[0.35-0.57]1.2[0.95-1.45]0.5[0.31-0.70]1.5[1.24-1.84]0.4[0.30-0.55]1.7[1.41-1.97]Rural0.5[0.39-0.66]1.1[0.91-1.26]0.4[0.31-0.44]1.3[0.87-1.68]0.4[0.25-0.46]1.0[0.83-1.16]0.3[0.23-0.45]0.7[0.60-0.89]Level of crime where students liveHigh level of crime0.4[0.16-0.57]1.8[1.24-2.27]0.5[0.28-0.62]2.7 ![0.91-4.52]0.7[0.43-0.90]3.1[2.04-4.22]0.5[0.25-0.68]2.5[1.50-3.43]Moderate level of crime0.6[0.41-0.70]2.0[1.69-2.33]0.5[0.43-0.66]1.7[1.45-2.04]0.6[0.45-0.82]2.0[1.57-2.45]0.3[0.24-0.41]2.2[1.63-2.83]Low level of crime0.6[0.49-0.68]1.3[1.16-1.51]0.4[0.37-0.48]1.1[0.98-1.24]0.4[0.36-0.50]1.2[1.07-1.40]0.4[0.29-0.43]1.1[0.93-1.20]Students come from areas with very different levels of crime0.8[0.59-1.10]2.3[1.79-2.76]0.6[0.42-0.75]1.8[1.40-2.12]0.7[0.52-0.95]2.4[1.87-2.86]0.4[0.26-0.56]1.8[1.11-2.41]# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: DISALC, DISDRUG, FR_LVEL, FR_SIZE, FR_URBAN and C0560. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: DISALC06 (SSOCS:2006), DISDRUG06 (SSOCS:2006), FR_LVEL (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_SIZE (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_LOC4 (SSOCS:2006), C0560 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), DISALC08 (SSOCS:2008), DISDRUG08 (SSOCS:2008), FR_URBAN (SSOCS:2008, SSOCS:2010, SSOCS:2016), DISALC10 (SSOCS:2010), DISDRUG10 (SSOCS:2010), DISALC16 (SSOCS:2016) and DISDRUG16 (SSOCS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006, 2007–08 School Survey on Crime and Safety (SSOCS), 2008, 2009-10 School Survey on Crime and Safety (SSOCS), 2010 and 2015-16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES TrendStats on 7/9/2018.mgbkc39mgbkc391Parent participates in open house or back to school night by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Parent participates in open house or back to school night0-25%26-50%51-75%76-100%School does not offerTotalEstimatesTotal20064.715.228.848.52.8100%20085.217.227.447.92.3100%20104.613.830.649.11.9100%20163.417.128.150.50.8 !100%School grades offered - based on CCD frame variables (School)Primary20061.2 !7.026.264.21.4 !100%20082.610.824.161.90.7 !!100%20101.3 !7.027.963.20.6 !!100%20160.3 !!10.625.763.10.3 !!100%Middle20064.221.737.536.10.5 !100%20084.221.336.437.01.0 !100%20103.917.337.740.11.1 !100%20163.817.834.244.00.2 !!100%High school200614.633.530.014.47.5100%200814.734.329.217.44.4100%201016.629.433.515.05.5100%201613.337.630.115.43.7100%Combined200613.026.525.626.09.0 !100%20089.5 !25.328.223.613.3100%20108.5 !26.929.028.07.6 !100%20166.4 !25.628.040.0#100%School size categories - based on CCD frame variables (School)Less than 30020067.011.728.745.86.8100%20085.816.122.749.46.0100%20106.411.130.447.74.4100%20163.017.726.550.32.5 !100%300 - 49920062.912.927.755.01.5100%20085.315.822.554.52.0 !100%20102.810.931.453.91.1 !100%20162.511.429.756.30.2 !!100%500 - 99920063.314.529.551.31.4 !100%20083.815.631.149.00.5 !!100%20103.714.128.952.01.2100%20162.918.326.552.10.3 !100%1,000 or more20068.431.129.329.51.7100%20088.128.838.223.51.4100%20108.926.334.728.71.3100%20168.528.432.929.11.0 !100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City20065.417.432.844.20.3 !!100%20086.119.130.543.80.5 !100%20104.317.933.244.00.7 !100%20163.820.231.144.10.9 !!100%Suburb20062.812.125.858.11.1100%20083.011.327.257.41.1 !!100%20102.49.131.756.40.5 !!100%20162.713.427.656.10.1 !!100%Town20064.4 !17.231.342.34.7 !100%20085.122.025.545.71.7100%20105.916.731.544.02.0100%20164.121.127.147.20.6 !100%Rural20066.216.027.944.05.9100%20086.519.126.043.45.1100%20106.313.327.149.14.2100%20163.516.626.352.01.6 !100%Level of crime where students liveHigh level of crime20069.5 !26.827.735.30.8 !!100%20089.334.523.831.21.2 !!100%201011.123.531.833.10.4 !!100%20169.135.428.427.1#100%Moderate level of crime20065.719.232.839.92.5 !100%20086.622.033.835.62.0 !100%20104.219.043.132.51.2 !100%20162.921.835.439.30.6 !!100%Low level of crime20062.912.527.553.73.4100%20084.113.724.755.02.5100%20103.610.225.857.92.5100%20162.112.526.058.70.7100%Students come from areas with very different levels of crime20068.814.929.445.31.6 !!100%20085.216.031.944.32.6 !100%20106.115.130.047.41.4 !100%20166.017.825.648.22.4 !!100%Parent participates in open house or back to school night by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Parent participates in open house or back to school night0-25%26-50%51-75%76-100%School does not offerTotalEstimatesTotal20064.715.228.848.52.8100%20085.217.227.447.92.3100%20104.613.830.649.11.9100%20163.417.128.150.50.8 !100%School grades offered - based on CCD frame variables (School)Primary20061.2 !7.026.264.21.4 !100%20082.610.824.161.90.7 !!100%20101.3 !7.027.963.20.6 !!100%20160.3 !!10.625.763.10.3 !!100%Middle20064.221.737.536.10.5 !100%20084.221.336.437.01.0 !100%20103.917.337.740.11.1 !100%20163.817.834.244.00.2 !!100%High school200614.633.530.014.47.5100%200814.734.329.217.44.4100%201016.629.433.515.05.5100%201613.337.630.115.43.7100%Combined200613.026.525.626.09.0 !100%20089.5 !25.328.223.613.3100%20108.5 !26.929.028.07.6 !100%20166.4 !25.628.040.0#100%School size categories - based on CCD frame variables (School)Less than 30020067.011.728.745.86.8100%20085.816.122.749.46.0100%20106.411.130.447.74.4100%20163.017.726.550.32.5 !100%300 - 49920062.912.927.755.01.5100%20085.315.822.554.52.0 !100%20102.810.931.453.91.1 !100%20162.511.429.756.30.2 !!100%500 - 99920063.314.529.551.31.4 !100%20083.815.631.149.00.5 !!100%20103.714.128.952.01.2100%20162.918.326.552.10.3 !100%1,000 or more20068.431.129.329.51.7100%20088.128.838.223.51.4100%20108.926.334.728.71.3100%20168.528.432.929.11.0 !100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City20065.417.432.844.20.3 !!100%20086.119.130.543.80.5 !100%20104.317.933.244.00.7 !100%20163.820.231.144.10.9 !!100%Suburb20062.812.125.858.11.1100%20083.011.327.257.41.1 !!100%20102.49.131.756.40.5 !!100%20162.713.427.656.10.1 !!100%Town20064.4 !17.231.342.34.7 !100%20085.122.025.545.71.7100%20105.916.731.544.02.0100%20164.121.127.147.20.6 !100%Rural20066.216.027.944.05.9100%20086.519.126.043.45.1100%20106.313.327.149.14.2100%20163.516.626.352.01.6 !100%Level of crime where students liveHigh level of crime20069.5 !26.827.735.30.8 !!100%20089.334.523.831.21.2 !!100%201011.123.531.833.10.4 !!100%20169.135.428.427.1#100%Moderate level of crime20065.719.232.839.92.5 !100%20086.622.033.835.62.0 !100%20104.219.043.132.51.2 !100%20162.921.835.439.30.6 !!100%Low level of crime20062.912.527.553.73.4100%20084.113.724.755.02.5100%20103.610.225.857.92.5100%20162.112.526.058.70.7100%Students come from areas with very different levels of crime20068.814.929.445.31.6 !!100%20085.216.031.944.32.6 !100%20106.115.130.047.41.4 !100%20166.017.825.648.22.4 !!100%Standard Error (BRR)Total20060.440.781.261.290.46 20080.540.961.221.390.41 20100.470.731.241.270.35 20160.330.951.151.330.25 School grades offered - based on CCD frame variables (School)Primary20060.520.841.932.040.58 20080.691.231.752.050.42 20100.490.861.671.810.33 20160.221.371.892.090.35 Middle20060.801.281.631.680.24 20080.681.201.721.690.43 20100.701.321.521.600.39 20160.731.541.811.760.25 High school20061.221.581.621.420.99 20081.391.501.671.360.93 20101.371.461.791.350.88 20161.502.181.781.510.94 Combined20063.365.094.024.982.93 20083.065.055.235.253.75 20103.504.794.864.493.30 20162.575.415.236.21† School size categories - based on CCD frame variables (School)Less than 30020061.271.582.723.271.65 20081.522.232.803.311.46 20101.431.923.183.521.19 20160.782.943.243.671.08 300 - 49920060.702.312.652.740.43 20081.171.932.522.400.69 20100.581.332.592.530.48 20160.551.432.572.860.14 500 - 99920060.481.081.391.560.42 20080.571.251.741.840.28 20100.631.401.821.650.30 20160.521.441.652.160.11 1,000 or more20060.981.621.782.190.39 20081.171.892.281.840.38 20101.052.011.461.790.38 20161.282.041.872.000.39 Urbanicity - Based on Urban-centric location of school - from CCD (School)City20060.891.572.212.290.17 20081.052.162.642.800.26 20100.782.012.252.180.33 20160.632.312.712.700.75 Suburb20060.510.921.881.870.31 20080.701.072.602.580.59 20100.440.922.332.510.27 20160.571.352.232.370.07 Town20061.712.133.834.031.41 20081.032.672.992.990.49 20101.072.273.133.310.57 20160.762.613.173.830.30 Rural20061.021.831.972.471.16 20081.332.002.182.301.12 20101.131.522.122.601.09 20160.781.852.482.750.49 Level of crime where students liveHigh level of crime20063.003.764.024.750.81 20082.455.565.134.730.84 20102.584.314.314.620.32 20162.054.574.764.61† Moderate level of crime20061.111.543.023.081.09 20081.172.142.702.900.75 20100.742.063.022.850.54 20160.652.112.593.140.33 Low level of crime20060.401.011.301.600.60 20080.761.201.411.900.63 20100.520.811.471.610.57 20160.370.981.571.660.19 Students come from areas with very different levels of crime20061.941.983.333.870.90 20081.162.392.603.511.22 20101.732.283.143.730.69 20161.762.943.864.311.69 Relative Standard Error (%)Total20069.345.174.382.6516.35 200810.375.554.452.9117.72 201010.185.294.062.5918.04 20169.895.544.092.6331.09 School grades offered - based on CCD frame variables (School)Primary200643.3512.097.353.1841.09 200826.7011.397.273.3158.41 201037.7612.245.972.8759.15 201672.5512.957.373.31100.52 Middle200619.065.924.344.6744.21 200816.005.644.714.5742.36 201017.937.664.033.9835.46 201619.358.665.294.00100.34 High school20068.414.715.409.8413.11 20089.464.365.747.8420.91 20108.244.965.338.9616.08 201611.315.815.909.7925.70 Combined200625.8619.2615.7219.1432.74 200832.2319.9618.5322.2428.11 201041.2717.8016.7716.0243.76 201640.2621.1518.6815.51† School size categories - based on CCD frame variables (School)Less than 300200618.0213.509.477.1424.21 200826.3113.8612.336.7024.41 201022.3717.2710.457.3926.93 201625.8916.6112.227.2943.39 300 - 499200624.2317.899.534.9829.48 200822.1612.2111.224.4135.19 201020.9312.238.254.7045.58 201622.0712.548.675.0970.48 500 - 999200614.487.474.713.0430.43 200814.968.035.603.7652.47 201016.859.936.323.1624.06 201618.347.906.224.1542.88 1,000 or more200611.565.216.097.4323.45 200814.466.575.977.8127.34 201011.747.624.206.2329.91 201615.027.185.696.8738.09 Urbanicity - Based on Urban-centric location of school - from CCD (School)City200616.649.046.755.1954.49 200817.2811.338.676.4048.01 201018.0111.266.794.9748.10 201616.9011.458.736.1282.55 Suburb200618.177.657.293.2127.76 200823.269.509.574.5052.10 201018.4210.117.374.4551.62 201621.1710.068.074.2356.61 Town200638.6212.3512.219.5430.16 200820.2612.1611.746.5528.68 201018.1613.619.967.5328.32 201618.7012.4011.728.1149.23 Rural200616.4811.477.055.6119.73 200820.6710.478.405.3021.92 201018.0011.457.845.3025.57 201622.3911.149.455.2930.05 Level of crime where students liveHigh level of crime200631.7614.0414.5013.46100.44 200826.3816.1321.5615.1569.99 201023.1718.3313.5713.9370.87 201622.4112.9316.7717.00† Moderate level of crime200619.668.029.227.7144.48 200817.619.718.008.1538.11 201017.6010.847.028.7746.66 201622.869.687.327.9858.73 Low level of crime200613.668.144.732.9717.73 200818.448.795.723.4525.10 201014.537.945.692.7922.64 201617.257.806.032.8228.09 Students come from areas with very different levels of crime200622.1813.2911.308.5456.61 200822.2814.968.177.9246.44 201028.1915.0910.487.8749.36 201629.3816.5415.078.9469.41 Weighted Sample Sizes (n/1,000s)Total200683.2 200883.0 201082.8 201683.6 School grades offered - based on CCD frame variables (School)Primary200648.6 200849.2 201048.9 201649.1 Middle200615.5 200815.3 201015.3 201615.6 High school200611.7 200811.9 201012.2 201612.8 Combined20067.4 20086.6 20106.4 20166.2 School size categories - based on CCD frame variables (School)Less than 300200620.8 200819.2 201018.9 201618.2 300 - 499200623.8 200824.3 201025.2 201625.0 500 - 999200629.3 200830.2 201029.8 201631.7 1,000 or more20069.3 20089.3 20108.9 20168.7 Urbanicity - Based on Urban-centric location of school - from CCD (School)City200621.0 200821.3 201021.5 201622.8 Suburb200627.6 200823.9 201023.8 201627.4 Town20068.2 200811.8 201012.1 201611.0 Rural200626.4 200826.0 201025.3 201622.5 Level of crime where students liveHigh level of crime20066.5 20086.2 20105.9 20167.4 Moderate level of crime200615.9 200817.1 201018.4 201617.5 Low level of crime200650.3 200849.2 201047.7 201648.4 Students come from areas with very different levels of crime200610.5 200810.5 201010.7 201610.4 Parent participates in open house or back to school night by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Parent participates in open house or back to school night0-25%26-50%51-75%76-100%School does not offerTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal20064.7[3.88-5.65]15.2[13.67-16.83]28.8[26.33-31.39]48.5[45.97-51.14]2.8[2.01-3.87]100%20085.2[4.20-6.37]17.2[15.41-19.26]27.4[25.03-29.93]47.9[45.06-50.66]2.3[1.62-3.29]100%20104.6[3.74-5.64]13.8[12.36-15.29]30.6[28.19-33.18]49.1[46.55-51.65]1.9[1.33-2.75]100%20163.4[2.77-4.12]17.1[15.32-19.14]28.1[25.88-30.49]50.5[47.87-53.21]0.8 ![0.43-1.50]100%School grades offered - based on CCD frame variables (School)Primary20061.2 ![0.50-2.86]7.0[5.46-8.87]26.2[22.53-30.26]64.2[60.00-68.18]1.4 ![0.62-3.22]100%20082.6[1.50-4.38]10.8[8.53-13.47]24.1[20.74-27.77]61.9[57.69-65.90]0.7 !![0.22-2.30]100%20101.3 ![0.61-2.78]7.0[5.49-8.97]27.9[24.71-31.41]63.2[59.46-66.73]0.6 !![0.17-1.82]100%20160.3 !![0.07-1.27]10.6[8.10-13.63]25.7[22.09-29.69]63.1[58.82-67.19]0.3 !![0.05-2.56]100%Middle20064.2[2.85-6.12]21.7[19.22-24.38]37.5[34.29-40.81]36.1[32.79-39.55]0.5 ![0.22-1.30]100%20084.2[3.06-5.83]21.3[19.02-23.85]36.4[33.03-39.91]37.0[33.69-40.48]1.0 ![0.43-2.35]100%20103.9[2.71-5.57]17.3[14.77-20.09]37.7[34.66-40.74]40.1[36.93-43.34]1.1 ![0.53-2.22]100%20163.8[2.56-5.56]17.8[14.93-21.14]34.2[30.64-37.89]44.0[40.48-47.53]0.2 !![0.03-1.82]100%High school200614.6[12.27-17.20]33.5[30.38-36.71]30.0[26.83-33.32]14.4[11.82-17.54]7.5[5.78-9.79]100%200814.7[12.14-17.74]34.3[31.38-37.39]29.2[25.91-32.63]17.4[14.80-20.27]4.4[2.91-6.72]100%201016.6[14.04-19.54]29.4[26.57-32.43]33.5[30.01-37.18]15.0[12.50-17.92]5.5[3.95-7.53]100%201613.3[10.54-16.60]37.6[33.28-42.03]30.1[26.66-33.79]15.4[12.61-18.68]3.7[2.17-6.10]100%Combined200613.0[7.60-21.35]26.5[17.53-37.84]25.6[18.35-34.42]26.0[17.30-37.18]9.0 ![4.56-16.85]100%20089.5 ![4.88-17.68]25.3[16.53-36.69]28.2[18.98-39.79]23.6[14.70-35.70]13.3[7.42-22.80]100%20108.5 ![3.61-18.66]26.9[18.43-37.54]29.0[20.25-39.62]28.0[19.95-37.88]7.6 ![3.06-17.45]100%20166.4 ![2.79-13.91]25.6[16.26-37.82]28.0[18.76-39.58]40.0[28.43-52.89]##100%School size categories - based on CCD frame variables (School)Less than 30020067.0[4.88-10.06]11.7[8.85-15.22]28.7[23.58-34.49]45.8[39.31-52.37]6.8[4.15-10.95]100%20085.8[3.38-9.70]16.1[12.11-21.11]22.7[17.56-28.78]49.4[42.83-56.06]6.0[3.65-9.71]100%20106.4[4.05-9.93]11.1[7.79-15.58]30.4[24.40-37.10]47.7[40.72-54.78]4.4[2.56-7.55]100%20163.0[1.79-5.07]17.7[12.56-24.43]26.5[20.52-33.49]50.3[42.94-57.56]2.5 ![1.03-5.86]100%300 - 49920062.9[1.76-4.66]12.9[8.93-18.28]27.7[22.76-33.36]55.0[49.47-60.44]1.5[0.81-2.63]100%20085.3[3.37-8.21]15.8[12.30-20.07]22.5[17.81-27.94]54.5[49.62-59.24]2.0 ![0.96-3.95]100%20102.8[1.82-4.20]10.9[8.46-13.82]31.4[26.46-36.86]53.9[48.78-58.92]1.1 ![0.42-2.63]100%20162.5[1.61-3.90]11.4[8.80-14.56]29.7[24.77-35.07]56.3[50.45-61.90]0.2 !![0.05-0.82]100%500 - 99920063.3[2.46-4.40]14.5[12.45-16.81]29.5[26.80-32.38]51.3[48.19-54.46]1.4 ![0.74-2.52]100%20083.8[2.82-5.13]15.6[13.23-18.26]31.1[27.68-34.66]49.0[45.33-52.72]0.5 !![0.19-1.52]100%20103.7[2.64-5.20]14.1[11.53-17.17]28.9[25.37-32.69]52.0[48.72-55.32]1.2[0.77-2.02]100%20162.9[1.97-4.12]18.3[15.54-21.35]26.5[23.37-29.99]52.1[47.72-56.37]0.3 ![0.11-0.63]100%1,000 or more20068.4[6.68-10.62]31.1[27.90-34.39]29.3[25.87-33.03]29.5[25.31-34.11]1.7[1.04-2.66]100%20088.1[6.03-10.77]28.8[25.14-32.74]38.2[33.75-42.89]23.5[20.03-27.41]1.4[0.80-2.39]100%20108.9[7.03-11.25]26.3[22.51-30.57]34.7[31.85-37.71]28.7[25.28-32.46]1.3[0.70-2.33]100%20168.5[6.27-11.45]28.4[24.52-32.72]32.9[29.24-36.75]29.1[25.28-33.31]1.0 ![0.48-2.20]100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City20065.4[3.83-7.46]17.4[14.47-20.81]32.8[28.48-37.34]44.2[39.62-48.81]0.3 !![0.10-0.91]100%20086.1[4.29-8.58]19.1[15.12-23.82]30.5[25.44-36.03]43.8[38.28-49.49]0.5 ![0.20-1.39]100%20104.3[3.01-6.21]17.9[14.17-22.25]33.2[28.80-37.82]44.0[39.64-48.40]0.7 ![0.26-1.78]100%20163.8[2.67-5.26]20.2[15.95-25.25]31.1[25.89-36.75]44.1[38.74-49.53]0.9 !![0.17-4.67]100%Suburb20062.8[1.94-4.03]12.1[10.35-14.07]25.8[22.24-29.79]58.1[54.35-61.84]1.1[0.65-1.97]100%20083.0[1.88-4.79]11.3[9.30-13.62]27.2[22.30-32.74]57.4[52.12-62.45]1.1 !![0.40-3.21]100%20102.4[1.63-3.42]9.1[7.41-11.12]31.7[27.17-36.52]56.4[51.27-61.31]0.5 !![0.18-1.46]100%20162.7[1.77-4.14]13.4[10.94-16.39]27.6[23.39-32.33]56.1[51.29-60.79]0.1 !![0.04-0.37]100%Town20064.4 ![2.02-9.46]17.2[13.38-21.96]31.3[24.20-39.48]42.3[34.46-50.53]4.7 ![2.54-8.50]100%20085.1[3.38-7.62]22.0[17.09-27.82]25.5[19.93-31.91]45.7[39.81-51.78]1.7[0.97-3.06]100%20105.9[4.07-8.43]16.7[12.60-21.75]31.5[25.55-38.09]44.0[37.46-50.67]2.0[1.13-3.52]100%20164.1[2.79-5.91]21.1[16.30-26.79]27.1[21.18-33.88]47.2[39.63-54.89]0.6 ![0.22-1.62]100%Rural20066.2[4.44-8.60]16.0[12.65-20.04]27.9[24.15-32.05]44.0[39.08-48.97]5.9[3.95-8.72]100%20086.5[4.24-9.71]19.1[15.39-23.42]26.0[21.83-30.59]43.4[38.84-48.06]5.1[3.27-7.88]100%20106.3[4.37-9.00]13.3[10.51-16.64]27.1[23.06-31.59]49.1[43.87-54.27]4.2[2.53-7.05]100%20163.5[2.21-5.42]16.6[13.24-20.70]26.3[21.59-31.55]52.0[46.46-57.46]1.6 ![0.89-2.99]100%Level of crime where students liveHigh level of crime20069.5 ![4.91-17.44]26.8[19.92-34.96]27.7[20.38-36.43]35.3[26.41-45.27]0.8 !![0.11-5.88]100%20089.3[5.41-15.54]34.5[24.30-46.33]23.8[15.03-35.55]31.2[22.57-41.40]1.2 !![0.29-4.79]100%201011.1[6.90-17.44]23.5[15.98-33.25]31.8[23.80-40.99]33.1[24.58-42.95]0.4 !![0.11-1.84]100%20169.1[5.77-14.16]35.4[26.80-44.99]28.4[19.85-38.82]27.1[18.89-37.29]##100%Moderate level of crime20065.7[3.80-8.35]19.2[16.29-22.48]32.8[27.01-39.10]39.9[33.92-46.23]2.5 ![1.00-5.92]100%20086.6[4.63-9.39]22.0[18.01-26.59]33.8[28.61-39.43]35.6[30.00-41.62]2.0 ![0.91-4.21]100%20104.2[2.96-5.99]19.0[15.21-23.49]43.1[37.14-49.23]32.5[27.08-38.50]1.2 ![0.45-2.93]100%20162.9[1.80-4.52]21.8[17.88-26.36]35.4[30.41-40.80]39.3[33.22-45.77]0.6 !![0.17-1.83]100%Low level of crime20062.9[2.21-3.83]12.5[10.57-14.66]27.5[25.00-30.23]53.7[50.46-56.87]3.4[2.38-4.84]100%20084.1[2.85-5.99]13.7[11.44-16.28]24.7[21.96-27.63]55.0[51.15-58.76]2.5[1.51-4.12]100%20103.6[2.67-4.79]10.2[8.69-11.96]25.8[22.99-28.89]57.9[54.59-61.06]2.5[1.59-3.95]100%20162.1[1.50-3.00]12.5[10.69-14.62]26.0[22.96-29.26]58.7[55.33-61.98]0.7[0.38-1.18]100%Students come from areas with very different levels of crime20068.8[5.56-13.52]14.9[11.33-19.32]29.4[23.22-36.54]45.3[37.72-53.14]1.6 !![0.51-4.90]100%20085.2[3.31-8.09]16.0[11.73-21.36]31.9[26.87-37.31]44.3[37.44-51.46]2.6 ![1.03-6.58]100%20106.1[3.46-10.70]15.1[11.08-20.29]30.0[24.06-36.64]47.4[40.00-54.86]1.4 ![0.51-3.71]100%20166.0[3.29-10.67]17.8[12.61-24.46]25.6[18.65-34.09]48.2[39.68-56.81]2.4 !![0.59-9.43]100%2006200820102016 Parent participates in open house or back to school nightParent participates in open house or back to school nightParent participates in open house or back to school nightParent participates in open house or back to school night 0-25%26-50%51-75%76-100%School does not offer0-25%26-50%51-75%76-100%School does not offer0-25%26-50%51-75%76-100%School does not offer0-25%26-50%51-75%76-100%School does not offerEstimatesTotal4.715.228.848.52.85.217.227.447.92.34.613.830.649.11.93.417.128.150.50.8School grades offered - based on CCD frame variables (School)Primary1.27.026.264.21.42.610.824.161.90.71.37.027.963.20.60.310.625.763.10.3Middle4.221.737.536.10.54.221.336.437.01.03.917.337.740.11.13.817.834.244.00.2High school14.633.530.014.47.514.734.329.217.44.416.629.433.515.05.513.337.630.115.43.7Combined13.026.525.626.09.09.525.328.223.613.38.526.929.028.07.66.425.628.040.0#School size categories - based on CCD frame variables (School)Less than 3007.011.728.745.86.85.816.122.749.46.06.411.130.447.74.43.017.726.550.32.5300 - 4992.912.927.755.01.55.315.822.554.52.02.810.931.453.91.12.511.429.756.30.2500 - 9993.314.529.551.31.43.815.631.149.00.53.714.128.952.01.22.918.326.552.10.31,000 or more8.431.129.329.51.78.128.838.223.51.48.926.334.728.71.38.528.432.929.11.0Urbanicity - Based on Urban-centric location of school - from CCD (School)City5.417.432.844.20.36.119.130.543.80.54.317.933.244.00.73.820.231.144.10.9Suburb2.812.125.858.11.13.011.327.257.41.12.49.131.756.40.52.713.427.656.10.1Town4.417.231.342.34.75.122.025.545.71.75.916.731.544.02.04.121.127.147.20.6Rural6.216.027.944.05.96.519.126.043.45.16.313.327.149.14.23.516.626.352.01.6Level of crime where students liveHigh level of crime9.526.827.735.30.89.334.523.831.21.211.123.531.833.10.49.135.428.427.1#Moderate level of crime5.719.232.839.92.56.622.033.835.62.04.219.043.132.51.22.921.835.439.30.6Low level of crime2.912.527.553.73.44.113.724.755.02.53.610.225.857.92.52.112.526.058.70.7Students come from areas with very different levels of crime8.814.929.445.31.65.216.031.944.32.66.115.130.047.41.46.017.825.648.22.42006200820102016 Parent participates in open house or back to school nightParent participates in open house or back to school nightParent participates in open house or back to school nightParent participates in open house or back to school night 0-25%26-50%51-75%76-100%School does not offer0-25%26-50%51-75%76-100%School does not offer0-25%26-50%51-75%76-100%School does not offer0-25%26-50%51-75%76-100%School does not offerEstimatesTotal4.715.228.848.52.85.217.227.447.92.34.613.830.649.11.93.417.128.150.50.8School grades offered - based on CCD frame variables (School)Primary1.27.026.264.21.42.610.824.161.90.71.37.027.963.20.60.310.625.763.10.3Middle4.221.737.536.10.54.221.336.437.01.03.917.337.740.11.13.817.834.244.00.2High school14.633.530.014.47.514.734.329.217.44.416.629.433.515.05.513.337.630.115.43.7Combined13.026.525.626.09.09.525.328.223.613.38.526.929.028.07.66.425.628.040.0#School size categories - based on CCD frame variables (School)Less than 3007.011.728.745.86.85.816.122.749.46.06.411.130.447.74.43.017.726.550.32.5300 - 4992.912.927.755.01.55.315.822.554.52.02.810.931.453.91.12.511.429.756.30.2500 - 9993.314.529.551.31.43.815.631.149.00.53.714.128.952.01.22.918.326.552.10.31,000 or more8.431.129.329.51.78.128.838.223.51.48.926.334.728.71.38.528.432.929.11.0Urbanicity - Based on Urban-centric location of school - from CCD (School)City5.417.432.844.20.36.119.130.543.80.54.317.933.244.00.73.820.231.144.10.9Suburb2.812.125.858.11.13.011.327.257.41.12.49.131.756.40.52.713.427.656.10.1Town4.417.231.342.34.75.122.025.545.71.75.916.731.544.02.04.121.127.147.20.6Rural6.216.027.944.05.96.519.126.043.45.16.313.327.149.14.23.516.626.352.01.6Level of crime where students liveHigh level of crime9.526.827.735.30.89.334.523.831.21.211.123.531.833.10.49.135.428.427.1#Moderate level of crime5.719.232.839.92.56.622.033.835.62.04.219.043.132.51.22.921.835.439.30.6Low level of crime2.912.527.553.73.44.113.724.755.02.53.610.225.857.92.52.112.526.058.70.7Students come from areas with very different levels of crime8.814.929.445.31.65.216.031.944.32.66.115.130.047.41.46.017.825.648.22.4Standard Error (BRR)Total0.440.781.261.290.460.540.961.221.390.410.470.731.241.270.350.330.951.151.330.25School grades offered - based on CCD frame variables (School)Primary0.520.841.932.040.580.691.231.752.050.420.490.861.671.810.330.221.371.892.090.35Middle0.801.281.631.680.240.681.201.721.690.430.701.321.521.600.390.731.541.811.760.25High school1.221.581.621.420.991.391.501.671.360.931.371.461.791.350.881.502.181.781.510.94Combined3.365.094.024.982.933.065.055.235.253.753.504.794.864.493.302.575.415.236.21†School size categories - based on CCD frame variables (School)Less than 3001.271.582.723.271.651.522.232.803.311.461.431.923.183.521.190.782.943.243.671.08300 - 4990.702.312.652.740.431.171.932.522.400.690.581.332.592.530.480.551.432.572.860.14500 - 9990.481.081.391.560.420.571.251.741.840.280.631.401.821.650.300.521.441.652.160.111,000 or more0.981.621.782.190.391.171.892.281.840.381.052.011.461.790.381.282.041.872.000.39Urbanicity - Based on Urban-centric location of school - from CCD (School)City0.891.572.212.290.171.052.162.642.800.260.782.012.252.180.330.632.312.712.700.75Suburb0.510.921.881.870.310.701.072.602.580.590.440.922.332.510.270.571.352.232.370.07Town1.712.133.834.031.411.032.672.992.990.491.072.273.133.310.570.762.613.173.830.30Rural1.021.831.972.471.161.332.002.182.301.121.131.522.122.601.090.781.852.482.750.49Level of crime where students liveHigh level of crime3.003.764.024.750.812.455.565.134.730.842.584.314.314.620.322.054.574.764.61†Moderate level of crime1.111.543.023.081.091.172.142.702.900.750.742.063.022.850.540.652.112.593.140.33Low level of crime0.401.011.301.600.600.761.201.411.900.630.520.811.471.610.570.370.981.571.660.19Students come from areas with very different levels of crime1.941.983.333.870.901.162.392.603.511.221.732.283.143.730.691.762.943.864.311.69Relative Standard Error (%)Total9.345.174.382.6516.3510.375.554.452.9117.7210.185.294.062.5918.049.895.544.092.6331.09School grades offered - based on CCD frame variables (School)Primary43.3512.097.353.1841.0926.7011.397.273.3158.4137.7612.245.972.8759.1572.5512.957.373.31100.52Middle19.065.924.344.6744.2116.005.644.714.5742.3617.937.664.033.9835.4619.358.665.294.00100.34High school8.414.715.409.8413.119.464.365.747.8420.918.244.965.338.9616.0811.315.815.909.7925.70Combined25.8619.2615.7219.1432.7432.2319.9618.5322.2428.1141.2717.8016.7716.0243.7640.2621.1518.6815.51†School size categories - based on CCD frame variables (School)Less than 30018.0213.509.477.1424.2126.3113.8612.336.7024.4122.3717.2710.457.3926.9325.8916.6112.227.2943.39300 - 49924.2317.899.534.9829.4822.1612.2111.224.4135.1920.9312.238.254.7045.5822.0712.548.675.0970.48500 - 99914.487.474.713.0430.4314.968.035.603.7652.4716.859.936.323.1624.0618.347.906.224.1542.881,000 or more11.565.216.097.4323.4514.466.575.977.8127.3411.747.624.206.2329.9115.027.185.696.8738.09Urbanicity - Based on Urban-centric location of school - from CCD (School)City16.649.046.755.1954.4917.2811.338.676.4048.0118.0111.266.794.9748.1016.9011.458.736.1282.55Suburb18.177.657.293.2127.7623.269.509.574.5052.1018.4210.117.374.4551.6221.1710.068.074.2356.61Town38.6212.3512.219.5430.1620.2612.1611.746.5528.6818.1613.619.967.5328.3218.7012.4011.728.1149.23Rural16.4811.477.055.6119.7320.6710.478.405.3021.9218.0011.457.845.3025.5722.3911.149.455.2930.05Level of crime where students liveHigh level of crime31.7614.0414.5013.46100.4426.3816.1321.5615.1569.9923.1718.3313.5713.9370.8722.4112.9316.7717.00†Moderate level of crime19.668.029.227.7144.4817.619.718.008.1538.1117.6010.847.028.7746.6622.869.687.327.9858.73Low level of crime13.668.144.732.9717.7318.448.795.723.4525.1014.537.945.692.7922.6417.257.806.032.8228.09Students come from areas with very different levels of crime22.1813.2911.308.5456.6122.2814.968.177.9246.4428.1915.0910.487.8749.3629.3816.5415.078.9469.41Weighted Sample Sizes (n/1,000s)Total83.2 83.0 82.8 83.6 School grades offered - based on CCD frame variables (School)Primary48.6 49.2 48.9 49.1 Middle15.5 15.3 15.3 15.6 High school11.7 11.9 12.2 12.8 Combined7.4 6.6 6.4 6.2 School size categories - based on CCD frame variables (School)Less than 30020.8 19.2 18.9 18.2 300 - 49923.8 24.3 25.2 25.0 500 - 99929.3 30.2 29.8 31.7 1,000 or more9.3 9.3 8.9 8.7 Urbanicity - Based on Urban-centric location of school - from CCD (School)City21.0 21.3 21.5 22.8 Suburb27.6 23.9 23.8 27.4 Town8.2 11.8 12.1 11.0 Rural26.4 26.0 25.3 22.5 Level of crime where students liveHigh level of crime6.5 6.2 5.9 7.4 Moderate level of crime15.9 17.1 18.4 17.5 Low level of crime50.3 49.2 47.7 48.4 Students come from areas with very different levels of crime10.5 10.5 10.7 10.4 2006200820102016 Parent participates in open house or back to school nightParent participates in open house or back to school nightParent participates in open house or back to school nightParent participates in open house or back to school night 0-25%26-50%51-75%76-100%School does not offer0-25%26-50%51-75%76-100%School does not offer0-25%26-50%51-75%76-100%School does not offer0-25%26-50%51-75%76-100%School does not offer Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal4.7[3.88-5.65]15.2[13.67-16.83]28.8[26.33-31.39]48.5[45.97-51.14]2.8[2.01-3.87]5.2[4.20-6.37]17.2[15.41-19.26]27.4[25.03-29.93]47.9[45.06-50.66]2.3[1.62-3.29]4.6[3.74-5.64]13.8[12.36-15.29]30.6[28.19-33.18]49.1[46.55-51.65]1.9[1.33-2.75]3.4[2.77-4.12]17.1[15.32-19.14]28.1[25.88-30.49]50.5[47.87-53.21]0.8 ![0.43-1.50]School grades offered - based on CCD frame variables (School)Primary1.2 ![0.50-2.86]7.0[5.46-8.87]26.2[22.53-30.26]64.2[60.00-68.18]1.4 ![0.62-3.22]2.6[1.50-4.38]10.8[8.53-13.47]24.1[20.74-27.77]61.9[57.69-65.90]0.7 !![0.22-2.30]1.3 ![0.61-2.78]7.0[5.49-8.97]27.9[24.71-31.41]63.2[59.46-66.73]0.6 !![0.17-1.82]0.3 !![0.07-1.27]10.6[8.10-13.63]25.7[22.09-29.69]63.1[58.82-67.19]0.3 !![0.05-2.56]Middle4.2[2.85-6.12]21.7[19.22-24.38]37.5[34.29-40.81]36.1[32.79-39.55]0.5 ![0.22-1.30]4.2[3.06-5.83]21.3[19.02-23.85]36.4[33.03-39.91]37.0[33.69-40.48]1.0 ![0.43-2.35]3.9[2.71-5.57]17.3[14.77-20.09]37.7[34.66-40.74]40.1[36.93-43.34]1.1 ![0.53-2.22]3.8[2.56-5.56]17.8[14.93-21.14]34.2[30.64-37.89]44.0[40.48-47.53]0.2 !![0.03-1.82]High school14.6[12.27-17.20]33.5[30.38-36.71]30.0[26.83-33.32]14.4[11.82-17.54]7.5[5.78-9.79]14.7[12.14-17.74]34.3[31.38-37.39]29.2[25.91-32.63]17.4[14.80-20.27]4.4[2.91-6.72]16.6[14.04-19.54]29.4[26.57-32.43]33.5[30.01-37.18]15.0[12.50-17.92]5.5[3.95-7.53]13.3[10.54-16.60]37.6[33.28-42.03]30.1[26.66-33.79]15.4[12.61-18.68]3.7[2.17-6.10]Combined13.0[7.60-21.35]26.5[17.53-37.84]25.6[18.35-34.42]26.0[17.30-37.18]9.0 ![4.56-16.85]9.5 ![4.88-17.68]25.3[16.53-36.69]28.2[18.98-39.79]23.6[14.70-35.70]13.3[7.42-22.80]8.5 ![3.61-18.66]26.9[18.43-37.54]29.0[20.25-39.62]28.0[19.95-37.88]7.6 ![3.06-17.45]6.4 ![2.79-13.91]25.6[16.26-37.82]28.0[18.76-39.58]40.0[28.43-52.89]##School size categories - based on CCD frame variables (School)Less than 3007.0[4.88-10.06]11.7[8.85-15.22]28.7[23.58-34.49]45.8[39.31-52.37]6.8[4.15-10.95]5.8[3.38-9.70]16.1[12.11-21.11]22.7[17.56-28.78]49.4[42.83-56.06]6.0[3.65-9.71]6.4[4.05-9.93]11.1[7.79-15.58]30.4[24.40-37.10]47.7[40.72-54.78]4.4[2.56-7.55]3.0[1.79-5.07]17.7[12.56-24.43]26.5[20.52-33.49]50.3[42.94-57.56]2.5 ![1.03-5.86]300 - 4992.9[1.76-4.66]12.9[8.93-18.28]27.7[22.76-33.36]55.0[49.47-60.44]1.5[0.81-2.63]5.3[3.37-8.21]15.8[12.30-20.07]22.5[17.81-27.94]54.5[49.62-59.24]2.0 ![0.96-3.95]2.8[1.82-4.20]10.9[8.46-13.82]31.4[26.46-36.86]53.9[48.78-58.92]1.1 ![0.42-2.63]2.5[1.61-3.90]11.4[8.80-14.56]29.7[24.77-35.07]56.3[50.45-61.90]0.2 !![0.05-0.82]500 - 9993.3[2.46-4.40]14.5[12.45-16.81]29.5[26.80-32.38]51.3[48.19-54.46]1.4 ![0.74-2.52]3.8[2.82-5.13]15.6[13.23-18.26]31.1[27.68-34.66]49.0[45.33-52.72]0.5 !![0.19-1.52]3.7[2.64-5.20]14.1[11.53-17.17]28.9[25.37-32.69]52.0[48.72-55.32]1.2[0.77-2.02]2.9[1.97-4.12]18.3[15.54-21.35]26.5[23.37-29.99]52.1[47.72-56.37]0.3 ![0.11-0.63]1,000 or more8.4[6.68-10.62]31.1[27.90-34.39]29.3[25.87-33.03]29.5[25.31-34.11]1.7[1.04-2.66]8.1[6.03-10.77]28.8[25.14-32.74]38.2[33.75-42.89]23.5[20.03-27.41]1.4[0.80-2.39]8.9[7.03-11.25]26.3[22.51-30.57]34.7[31.85-37.71]28.7[25.28-32.46]1.3[0.70-2.33]8.5[6.27-11.45]28.4[24.52-32.72]32.9[29.24-36.75]29.1[25.28-33.31]1.0 ![0.48-2.20]Urbanicity - Based on Urban-centric location of school - from CCD (School)City5.4[3.83-7.46]17.4[14.47-20.81]32.8[28.48-37.34]44.2[39.62-48.81]0.3 !![0.10-0.91]6.1[4.29-8.58]19.1[15.12-23.82]30.5[25.44-36.03]43.8[38.28-49.49]0.5 ![0.20-1.39]4.3[3.01-6.21]17.9[14.17-22.25]33.2[28.80-37.82]44.0[39.64-48.40]0.7 ![0.26-1.78]3.8[2.67-5.26]20.2[15.95-25.25]31.1[25.89-36.75]44.1[38.74-49.53]0.9 !![0.17-4.67]Suburb2.8[1.94-4.03]12.1[10.35-14.07]25.8[22.24-29.79]58.1[54.35-61.84]1.1[0.65-1.97]3.0[1.88-4.79]11.3[9.30-13.62]27.2[22.30-32.74]57.4[52.12-62.45]1.1 !![0.40-3.21]2.4[1.63-3.42]9.1[7.41-11.12]31.7[27.17-36.52]56.4[51.27-61.31]0.5 !![0.18-1.46]2.7[1.77-4.14]13.4[10.94-16.39]27.6[23.39-32.33]56.1[51.29-60.79]0.1 !![0.04-0.37]Town4.4 ![2.02-9.46]17.2[13.38-21.96]31.3[24.20-39.48]42.3[34.46-50.53]4.7 ![2.54-8.50]5.1[3.38-7.62]22.0[17.09-27.82]25.5[19.93-31.91]45.7[39.81-51.78]1.7[0.97-3.06]5.9[4.07-8.43]16.7[12.60-21.75]31.5[25.55-38.09]44.0[37.46-50.67]2.0[1.13-3.52]4.1[2.79-5.91]21.1[16.30-26.79]27.1[21.18-33.88]47.2[39.63-54.89]0.6 ![0.22-1.62]Rural6.2[4.44-8.60]16.0[12.65-20.04]27.9[24.15-32.05]44.0[39.08-48.97]5.9[3.95-8.72]6.5[4.24-9.71]19.1[15.39-23.42]26.0[21.83-30.59]43.4[38.84-48.06]5.1[3.27-7.88]6.3[4.37-9.00]13.3[10.51-16.64]27.1[23.06-31.59]49.1[43.87-54.27]4.2[2.53-7.05]3.5[2.21-5.42]16.6[13.24-20.70]26.3[21.59-31.55]52.0[46.46-57.46]1.6 ![0.89-2.99]Level of crime where students liveHigh level of crime9.5 ![4.91-17.44]26.8[19.92-34.96]27.7[20.38-36.43]35.3[26.41-45.27]0.8 !![0.11-5.88]9.3[5.41-15.54]34.5[24.30-46.33]23.8[15.03-35.55]31.2[22.57-41.40]1.2 !![0.29-4.79]11.1[6.90-17.44]23.5[15.98-33.25]31.8[23.80-40.99]33.1[24.58-42.95]0.4 !![0.11-1.84]9.1[5.77-14.16]35.4[26.80-44.99]28.4[19.85-38.82]27.1[18.89-37.29]##Moderate level of crime5.7[3.80-8.35]19.2[16.29-22.48]32.8[27.01-39.10]39.9[33.92-46.23]2.5 ![1.00-5.92]6.6[4.63-9.39]22.0[18.01-26.59]33.8[28.61-39.43]35.6[30.00-41.62]2.0 ![0.91-4.21]4.2[2.96-5.99]19.0[15.21-23.49]43.1[37.14-49.23]32.5[27.08-38.50]1.2 ![0.45-2.93]2.9[1.80-4.52]21.8[17.88-26.36]35.4[30.41-40.80]39.3[33.22-45.77]0.6 !![0.17-1.83]Low level of crime2.9[2.21-3.83]12.5[10.57-14.66]27.5[25.00-30.23]53.7[50.46-56.87]3.4[2.38-4.84]4.1[2.85-5.99]13.7[11.44-16.28]24.7[21.96-27.63]55.0[51.15-58.76]2.5[1.51-4.12]3.6[2.67-4.79]10.2[8.69-11.96]25.8[22.99-28.89]57.9[54.59-61.06]2.5[1.59-3.95]2.1[1.50-3.00]12.5[10.69-14.62]26.0[22.96-29.26]58.7[55.33-61.98]0.7[0.38-1.18]Students come from areas with very different levels of crime8.8[5.56-13.52]14.9[11.33-19.32]29.4[23.22-36.54]45.3[37.72-53.14]1.6 !![0.51-4.90]5.2[3.31-8.09]16.0[11.73-21.36]31.9[26.87-37.31]44.3[37.44-51.46]2.6 ![1.03-6.58]6.1[3.46-10.70]15.1[11.08-20.29]30.0[24.06-36.64]47.4[40.00-54.86]1.4 ![0.51-3.71]6.0[3.29-10.67]17.8[12.61-24.46]25.6[18.65-34.09]48.2[39.68-56.81]2.4 !![0.59-9.43]# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: C0196, FR_LVEL, FR_SIZE, FR_URBAN and C0560. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: C0196 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_LVEL (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_SIZE (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_LOC4 (SSOCS:2006), C0560 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016) and FR_URBAN (SSOCS:2008, SSOCS:2010, SSOCS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006, 2007–08 School Survey on Crime and Safety (SSOCS), 2008, 2009-10 School Survey on Crime and Safety (SSOCS), 2010 and 2015-16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES TrendStats on 7/9/2018.mgbkcfdmgbkcfd2How often cyberbullying occurences happen by School Level, Enrollment Size, Locale and Percent White enrollment (categorical) for years 2010 and 2016 How often cyberbullying occurences happenHappens at least once a weekHappens less than once a weekTotalEstimatesTotal20107.992.1100%201612.088.0100%School LevelPrimary20101.598.5100%20164.295.8100%Middle201018.681.4100%201625.674.4100%High school201017.682.4100%201625.974.1100%Combined201012.687.4100%201610.6 !89.4100%Enrollment SizeLess than 30020104.895.2100%20167.992.1100%300 - 49920104.695.4100%20168.591.5100%500 - 99920109.390.7100%201612.987.1100%1,000 or more201019.280.8100%201627.372.7100%LocaleCity20105.794.3100%201612.287.8100%Suburb20108.591.5100%201610.989.1100%Town20109.690.4100%201614.485.6100%Rural20108.491.6100%201612.088.0100%Percent White enrollment (categorical)More than 95 percent201012.887.2100%201611.888.2100%More than 80 but less than or equal to 95 percent201010.189.9100%201612.687.4100%More than 50 but less than or equal to 80 percent20106.793.3100%201611.788.3100%50 percent or less20105.394.7100%201611.988.1100%How often cyberbullying occurences happen by School Level, Enrollment Size, Locale and Percent White enrollment (categorical) for years 2010 and 2016 How often cyberbullying occurences happenHappens at least once a weekHappens less than once a weekTotalEstimatesTotal20107.992.1100%201612.088.0100%School LevelPrimary20101.598.5100%20164.295.8100%Middle201018.681.4100%201625.674.4100%High school201017.682.4100%201625.974.1100%Combined201012.687.4100%201610.6 !89.4100%Enrollment SizeLess than 30020104.895.2100%20167.992.1100%300 - 49920104.695.4100%20168.591.5100%500 - 99920109.390.7100%201612.987.1100%1,000 or more201019.280.8100%201627.372.7100%LocaleCity20105.794.3100%201612.287.8100%Suburb20108.591.5100%201610.989.1100%Town20109.690.4100%201614.485.6100%Rural20108.491.6100%201612.088.0100%Percent White enrollment (categorical)More than 95 percent201012.887.2100%201611.888.2100%More than 80 but less than or equal to 95 percent201010.189.9100%201612.687.4100%More than 50 but less than or equal to 80 percent20106.793.3100%201611.788.3100%50 percent or less20105.394.7100%201611.988.1100%Standard Error (BRR)Total20100.490.49 20160.640.64 School LevelPrimary20100.430.43 20160.810.81 Middle20101.481.48 20161.791.79 High school20101.111.11 20161.631.63 Combined20103.343.34 20163.353.35 Enrollment SizeLess than 30020101.211.21 20161.621.62 300 - 49920100.740.74 20161.371.37 500 - 99920100.630.63 20160.970.97 1,000 or more20101.421.42 20161.981.98 LocaleCity20100.620.62 20161.361.36 Suburb20100.850.85 20161.151.15 Town20101.451.45 20162.212.21 Rural20101.071.07 20161.481.48 Percent White enrollment (categorical)More than 95 percent20102.052.05 20162.612.61 More than 80 but less than or equal to 95 percent20100.900.90 20161.801.80 More than 50 but less than or equal to 80 percent20100.770.77 20161.211.21 50 percent or less20100.600.60 20161.201.20 Relative Standard Error (%)Total20106.260.54 20165.370.73 School LevelPrimary201028.090.43 201619.000.84 Middle20107.921.81 20167.012.41 High school20106.301.35 20166.292.20 Combined201026.573.83 201631.723.75 Enrollment SizeLess than 300201025.441.27 201620.381.75 300 - 499201016.020.78 201616.061.50 500 - 99920106.790.70 20167.541.12 1,000 or more20107.411.76 20167.252.72 LocaleCity201010.860.66 201611.131.55 Suburb20109.990.93 201610.531.29 Town201015.171.60 201615.382.58 Rural201012.701.17 201612.361.68 Percent White enrollment (categorical)More than 95 percent201015.982.36 201622.152.96 More than 80 but less than or equal to 95 percent20108.871.00 201614.372.06 More than 50 but less than or equal to 80 percent201011.490.82 201610.331.37 50 percent or less201011.390.63 201610.081.36 Weighted Sample Sizes (n/1,000s)Total201082.8 201683.6 School LevelPrimary201048.9 201649.1 Middle201015.3 201615.6 High school201012.2 201612.8 Combined20106.4 20166.2 Enrollment SizeLess than 300201018.9 201618.2 300 - 499201025.2 201625.0 500 - 999201029.8 201631.7 1,000 or more20108.9 20168.7 LocaleCity201021.5 201622.8 Suburb201023.8 201627.4 Town201012.1 201611.0 Rural201025.3 201622.5 Percent White enrollment (categorical)More than 95 percent201011.7 20165.3 More than 80 but less than or equal to 95 percent201020.9 201621.3 More than 50 but less than or equal to 80 percent201020.0 201621.9 50 percent or less201030.1 201635.1 How often cyberbullying occurences happen by School Level, Enrollment Size, Locale and Percent White enrollment (categorical) for years 2010 and 2016 How often cyberbullying occurences happenHappens at least once a weekHappens less than once a weekTotalPct.95% CIPct.95% CI EstimatesTotal20107.9[6.97-8.96]92.1[91.04-93.03]100%201612.0[10.77-13.36]88.0[86.64-89.23]100%School LevelPrimary20101.5[0.86-2.66]98.5[97.34-99.14]100%20164.2[2.89-6.19]95.8[93.81-97.11]100%Middle201018.6[15.85-21.78]81.4[78.22-84.15]100%201625.6[22.14-29.33]74.4[70.67-77.86]100%High school201017.6[15.50-19.96]82.4[80.04-84.50]100%201625.9[22.77-29.31]74.1[70.69-77.23]100%Combined201012.6[7.25-20.96]87.4[79.04-92.75]100%201610.6 ![5.48-19.41]89.4[80.59-94.52]100%Enrollment SizeLess than 30020104.8[2.84-7.88]95.2[92.12-97.16]100%20167.9[5.23-11.84]92.1[88.16-94.77]100%300 - 49920104.6[3.34-6.36]95.4[93.64-96.66]100%20168.5[6.15-11.71]91.5[88.29-93.85]100%500 - 99920109.3[8.10-10.64]90.7[89.36-91.90]100%201612.9[11.05-14.96]87.1[85.04-88.95]100%1,000 or more201019.2[16.47-22.18]80.8[77.82-83.53]100%201627.3[23.52-31.47]72.7[68.53-76.48]100%LocaleCity20105.7[4.59-7.10]94.3[92.90-95.41]100%201612.2[9.72-15.20]87.8[84.80-90.28]100%Suburb20108.5[6.94-10.37]91.5[89.63-93.06]100%201610.9[8.80-13.44]89.1[86.56-91.20]100%Town20109.6[7.02-12.89]90.4[87.11-92.98]100%201614.4[10.48-19.41]85.6[80.59-89.52]100%Rural20108.4[6.50-10.83]91.6[89.17-93.50]100%201612.0[9.31-15.29]88.0[84.71-90.69]100%Percent White enrollment (categorical)More than 95 percent201012.8[9.25-17.56]87.2[82.44-90.75]100%201611.8[7.47-18.13]88.2[81.87-92.53]100%More than 80 but less than or equal to 95 percent201010.1[8.45-12.06]89.9[87.94-91.55]100%201612.6[9.36-16.66]87.4[83.34-90.64]100%More than 50 but less than or equal to 80 percent20106.7[5.29-8.40]93.3[91.60-94.71]100%201611.7[9.49-14.37]88.3[85.63-90.51]100%50 percent or less20105.3[4.18-6.60]94.7[93.40-95.82]100%201611.9[9.67-14.50]88.1[85.50-90.33]100%20102016 How often cyberbullying occurences happenHow often cyberbullying occurences happen Happens at least once a weekHappens less than once a weekHappens at least once a weekHappens less than once a weekEstimatesTotal7.992.112.088.0School LevelPrimary1.598.54.295.8Middle18.681.425.674.4High school17.682.425.974.1Combined12.687.410.689.4Enrollment SizeLess than 3004.895.27.992.1300 - 4994.695.48.591.5500 - 9999.390.712.987.11,000 or more19.280.827.372.7LocaleCity5.794.312.287.8Suburb8.591.510.989.1Town9.690.414.485.6Rural8.491.612.088.0Percent White enrollment (categorical)More than 95 percent12.887.211.888.2More than 80 but less than or equal to 95 percent10.189.912.687.4More than 50 but less than or equal to 80 percent6.793.311.788.350 percent or less5.394.711.988.120102016 How often cyberbullying occurences happenHow often cyberbullying occurences happen Happens at least once a weekHappens less than once a weekHappens at least once a weekHappens less than once a weekEstimatesTotal7.992.112.088.0School LevelPrimary1.598.54.295.8Middle18.681.425.674.4High school17.682.425.974.1Combined12.687.410.689.4Enrollment SizeLess than 3004.895.27.992.1300 - 4994.695.48.591.5500 - 9999.390.712.987.11,000 or more19.280.827.372.7LocaleCity5.794.312.287.8Suburb8.591.510.989.1Town9.690.414.485.6Rural8.491.612.088.0Percent White enrollment (categorical)More than 95 percent12.887.211.888.2More than 80 but less than or equal to 95 percent10.189.912.687.4More than 50 but less than or equal to 80 percent6.793.311.788.350 percent or less5.394.711.988.1Standard Error (BRR)Total0.490.490.640.64School LevelPrimary0.430.430.810.81Middle1.481.481.791.79High school1.111.111.631.63Combined3.343.343.353.35Enrollment SizeLess than 3001.211.211.621.62300 - 4990.740.741.371.37500 - 9990.630.630.970.971,000 or more1.421.421.981.98LocaleCity0.620.621.361.36Suburb0.850.851.151.15Town1.451.452.212.21Rural1.071.071.481.48Percent White enrollment (categorical)More than 95 percent2.052.052.612.61More than 80 but less than or equal to 95 percent0.900.901.801.80More than 50 but less than or equal to 80 percent0.770.771.211.2150 percent or less0.600.601.201.20Relative Standard Error (%)Total6.260.545.370.73School LevelPrimary28.090.4319.000.84Middle7.921.817.012.41High school6.301.356.292.20Combined26.573.8331.723.75Enrollment SizeLess than 30025.441.2720.381.75300 - 49916.020.7816.061.50500 - 9996.790.707.541.121,000 or more7.411.767.252.72LocaleCity10.860.6611.131.55Suburb9.990.9310.531.29Town15.171.6015.382.58Rural12.701.1712.361.68Percent White enrollment (categorical)More than 95 percent15.982.3622.152.96More than 80 but less than or equal to 95 percent8.871.0014.372.06More than 50 but less than or equal to 80 percent11.490.8210.331.3750 percent or less11.390.6310.081.36Weighted Sample Sizes (n/1,000s)Total82.8 83.6 School LevelPrimary48.9 49.1 Middle15.3 15.6 High school12.2 12.8 Combined6.4 6.2 Enrollment SizeLess than 30018.9 18.2 300 - 49925.2 25.0 500 - 99929.8 31.7 1,000 or more8.9 8.7 LocaleCity21.5 22.8 Suburb23.8 27.4 Town12.1 11.0 Rural25.3 22.5 Percent White enrollment (categorical)More than 95 percent11.7 5.3 More than 80 but less than or equal to 95 percent20.9 21.3 More than 50 but less than or equal to 80 percent20.0 21.9 50 percent or less30.1 35.1 20102016 How often cyberbullying occurences happenHow often cyberbullying occurences happen Happens at least once a weekHappens less than once a weekHappens at least once a weekHappens less than once a week Pct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal7.9[6.97-8.96]92.1[91.04-93.03]12.0[10.77-13.36]88.0[86.64-89.23]School LevelPrimary1.5[0.86-2.66]98.5[97.34-99.14]4.2[2.89-6.19]95.8[93.81-97.11]Middle18.6[15.85-21.78]81.4[78.22-84.15]25.6[22.14-29.33]74.4[70.67-77.86]High school17.6[15.50-19.96]82.4[80.04-84.50]25.9[22.77-29.31]74.1[70.69-77.23]Combined12.6[7.25-20.96]87.4[79.04-92.75]10.6 ![5.48-19.41]89.4[80.59-94.52]Enrollment SizeLess than 3004.8[2.84-7.88]95.2[92.12-97.16]7.9[5.23-11.84]92.1[88.16-94.77]300 - 4994.6[3.34-6.36]95.4[93.64-96.66]8.5[6.15-11.71]91.5[88.29-93.85]500 - 9999.3[8.10-10.64]90.7[89.36-91.90]12.9[11.05-14.96]87.1[85.04-88.95]1,000 or more19.2[16.47-22.18]80.8[77.82-83.53]27.3[23.52-31.47]72.7[68.53-76.48]LocaleCity5.7[4.59-7.10]94.3[92.90-95.41]12.2[9.72-15.20]87.8[84.80-90.28]Suburb8.5[6.94-10.37]91.5[89.63-93.06]10.9[8.80-13.44]89.1[86.56-91.20]Town9.6[7.02-12.89]90.4[87.11-92.98]14.4[10.48-19.41]85.6[80.59-89.52]Rural8.4[6.50-10.83]91.6[89.17-93.50]12.0[9.31-15.29]88.0[84.71-90.69]Percent White enrollment (categorical)More than 95 percent12.8[9.25-17.56]87.2[82.44-90.75]11.8[7.47-18.13]88.2[81.87-92.53]More than 80 but less than or equal to 95 percent10.1[8.45-12.06]89.9[87.94-91.55]12.6[9.36-16.66]87.4[83.34-90.64]More than 50 but less than or equal to 80 percent6.7[5.29-8.40]93.3[91.60-94.71]11.7[9.49-14.37]88.3[85.63-90.51]50 percent or less5.3[4.18-6.60]94.7[93.40-95.82]11.9[9.67-14.50]88.1[85.50-90.33]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: C0389, FR_LVEL, FR_SIZE, FR_URBAN and PERCWHT. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: C0389 (SSOCS:2010, SSOCS:2016), FR_LVEL (SSOCS:2010, SSOCS:2016), FR_SIZE (SSOCS:2010, SSOCS:2016), FR_URBAN (SSOCS:2010, SSOCS:2016) and PERCWHT (SSOCS:2010, SSOCS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2009-10 School Survey on Crime and Safety (SSOCS), 2010 and 2015-16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES TrendStats on 7/9/2018.mgbkcd3mgbkcd33Disciplinary occurrences: Student bullying by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Disciplinary occurrences: Student bullyingHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensTotalEstimatesTotal20066.717.818.455.02.1100%20088.516.820.352.42.1100%20106.816.222.651.72.6100%20162.89.118.665.54.0100%School grades offered - based on CCD frame variables (School)Primary20064.316.315.861.22.4100%20085.614.920.457.12.0100%20105.713.920.456.83.2100%20161.8 !6.315.670.55.7100%Middle200613.829.225.031.10.8 !100%200817.526.022.533.70.3 !!100%201013.225.426.135.00.3 !!100%20165.016.822.554.61.1 !100%High school20068.114.320.755.81.1 !100%20087.514.221.153.63.6100%20103.716.125.053.31.9100%20163.411.321.062.51.8100%Combined20065.1 !9.5 !18.263.04.1 !!100%200811.013.912.958.43.7 !!100%20106.7 !12.026.450.24.8 !!100%20163.9 !7.1 !27.558.82.8 !!100%School size categories - based on CCD frame variables (School)Less than 30020065.014.315.060.94.8 !100%20086.112.618.357.65.4100%20104.711.723.554.45.6 !100%20161.3 !!5.119.968.35.4 !100%300 - 49920064.117.819.957.30.9 !!100%20086.614.221.056.91.3 !100%20107.216.821.352.81.9 !100%20163.1 !6.515.370.54.6100%500 - 99920068.819.718.751.31.5100%200810.719.921.347.40.7 !100%20107.917.421.651.31.8 !100%20163.110.920.062.53.4100%1,000 or more200610.319.621.547.80.8 !100%200811.421.819.246.01.6 !!100%20106.920.127.944.40.7 !100%20163.918.220.355.71.9 !100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City20069.120.416.751.72.1 !100%200810.117.421.548.62.5 !100%201010.017.022.147.33.6100%20163.79.218.265.23.7 !100%Suburb20066.316.519.356.31.6 !100%20088.316.318.054.92.4 !100%20106.613.322.455.02.7 !100%20162.57.915.867.66.2100%Town20066.521.822.647.02.1 !!100%200810.020.319.249.11.4 !!100%20105.221.025.248.30.3 !!100%20164.4 !13.821.658.81.3 !!100%Rural20065.215.817.658.82.6 !100%20086.715.121.954.61.8 !100%20105.216.022.054.02.8 !100%20161.6 !8.121.066.33.0 !100%Level of crime where students liveHigh level of crime200618.323.716.639.91.5 !!100%200815.319.122.441.41.8 !!100%201013.7 !16.626.543.10.2 !!100%20167.5 !12.026.751.22.7 !!100%Moderate level of crime20065.621.324.546.12.5 !100%200811.721.325.740.01.4 !100%20108.621.023.543.73.2 !100%20164.012.020.560.72.8 !!100%Low level of crime20064.815.817.060.22.1100%20086.115.119.457.42.0100%20105.114.321.356.72.6100%20161.97.617.468.54.5100%Students come from areas with very different levels of crime200610.018.517.152.81.6 !100%200810.415.714.555.83.5 !100%20107.816.324.948.12.8 !100%20161.7 !9.015.269.44.7 !100%Disciplinary occurrences: Student bullying by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Disciplinary occurrences: Student bullyingHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensTotalEstimatesTotal20066.717.818.455.02.1100%20088.516.820.352.42.1100%20106.816.222.651.72.6100%20162.89.118.665.54.0100%School grades offered - based on CCD frame variables (School)Primary20064.316.315.861.22.4100%20085.614.920.457.12.0100%20105.713.920.456.83.2100%20161.8 !6.315.670.55.7100%Middle200613.829.225.031.10.8 !100%200817.526.022.533.70.3 !!100%201013.225.426.135.00.3 !!100%20165.016.822.554.61.1 !100%High school20068.114.320.755.81.1 !100%20087.514.221.153.63.6100%20103.716.125.053.31.9100%20163.411.321.062.51.8100%Combined20065.1 !9.5 !18.263.04.1 !!100%200811.013.912.958.43.7 !!100%20106.7 !12.026.450.24.8 !!100%20163.9 !7.1 !27.558.82.8 !!100%School size categories - based on CCD frame variables (School)Less than 30020065.014.315.060.94.8 !100%20086.112.618.357.65.4100%20104.711.723.554.45.6 !100%20161.3 !!5.119.968.35.4 !100%300 - 49920064.117.819.957.30.9 !!100%20086.614.221.056.91.3 !100%20107.216.821.352.81.9 !100%20163.1 !6.515.370.54.6100%500 - 99920068.819.718.751.31.5100%200810.719.921.347.40.7 !100%20107.917.421.651.31.8 !100%20163.110.920.062.53.4100%1,000 or more200610.319.621.547.80.8 !100%200811.421.819.246.01.6 !!100%20106.920.127.944.40.7 !100%20163.918.220.355.71.9 !100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City20069.120.416.751.72.1 !100%200810.117.421.548.62.5 !100%201010.017.022.147.33.6100%20163.79.218.265.23.7 !100%Suburb20066.316.519.356.31.6 !100%20088.316.318.054.92.4 !100%20106.613.322.455.02.7 !100%20162.57.915.867.66.2100%Town20066.521.822.647.02.1 !!100%200810.020.319.249.11.4 !!100%20105.221.025.248.30.3 !!100%20164.4 !13.821.658.81.3 !!100%Rural20065.215.817.658.82.6 !100%20086.715.121.954.61.8 !100%20105.216.022.054.02.8 !100%20161.6 !8.121.066.33.0 !100%Level of crime where students liveHigh level of crime200618.323.716.639.91.5 !!100%200815.319.122.441.41.8 !!100%201013.7 !16.626.543.10.2 !!100%20167.5 !12.026.751.22.7 !!100%Moderate level of crime20065.621.324.546.12.5 !100%200811.721.325.740.01.4 !100%20108.621.023.543.73.2 !100%20164.012.020.560.72.8 !!100%Low level of crime20064.815.817.060.22.1100%20086.115.119.457.42.0100%20105.114.321.356.72.6100%20161.97.617.468.54.5100%Students come from areas with very different levels of crime200610.018.517.152.81.6 !100%200810.415.714.555.83.5 !100%20107.816.324.948.12.8 !100%20161.7 !9.015.269.44.7 !100%Standard Error (BRR)Total20060.640.980.941.160.41 20080.710.880.931.140.37 20100.620.901.001.310.47 20160.430.681.121.580.73 School grades offered - based on CCD frame variables (School)Primary20060.771.571.552.000.66 20080.991.431.371.570.57 20100.901.371.412.030.69 20160.570.901.682.161.25 Middle20061.471.431.511.770.31 20081.231.301.481.690.21 20101.141.341.491.840.19 20160.781.571.722.050.45 High school20061.051.301.401.830.43 20081.061.151.641.910.81 20100.561.301.461.790.56 20160.871.131.662.140.50 Combined20062.322.944.115.092.16 20083.023.293.014.972.08 20102.832.854.995.482.53 20161.822.975.145.792.20 School size categories - based on CCD frame variables (School)Less than 30020061.262.221.922.471.50 20081.672.092.393.271.41 20101.282.353.203.971.80 20160.721.492.863.881.75 300 - 49920060.871.552.232.210.70 20081.301.772.012.590.54 20101.291.892.022.580.69 20161.161.322.032.851.36 500 - 99920061.081.581.492.170.46 20081.061.701.742.150.33 20100.931.331.511.790.62 20160.651.231.722.370.87 1,000 or more20061.191.061.802.080.28 20081.432.261.412.320.84 20101.141.661.931.890.26 20160.761.831.802.160.68 Urbanicity - Based on Urban-centric location of school - from CCD (School)City20061.372.121.592.480.72 20081.551.742.122.890.82 20101.701.592.142.531.02 20160.961.082.132.801.33 Suburb20060.791.321.551.610.55 20081.242.071.612.400.78 20101.201.461.862.370.98 20160.681.021.562.021.47 Town20061.713.233.724.371.28 20081.972.702.632.970.92 20101.262.862.392.830.34 20161.452.522.714.131.01 Rural20060.911.711.812.570.94 20081.101.631.922.200.77 20101.011.982.052.280.96 20160.491.402.893.181.03 Level of crime where students liveHigh level of crime20063.863.843.374.281.32 20083.714.234.644.491.13 20104.333.003.854.690.16 20162.652.624.755.002.29 Moderate level of crime20061.032.282.262.771.10 20081.502.272.262.970.62 20101.742.472.263.031.12 20161.132.032.833.431.50 Low level of crime20060.581.211.131.500.60 20080.781.151.261.570.51 20100.660.891.641.860.62 20160.460.881.532.010.96 Students come from areas with very different levels of crime20062.012.402.723.280.77 20081.812.232.422.931.49 20101.842.052.703.601.26 20160.601.712.773.512.15 Relative Standard Error (%)Total20069.505.505.092.1119.54 20088.375.254.612.1717.63 20109.095.574.442.5317.95 201615.397.456.002.4218.13 School grades offered - based on CCD frame variables (School)Primary200617.879.639.793.2727.50 200817.549.596.742.7528.08 201015.829.846.913.5721.52 201630.8614.2710.723.0621.84 Middle200610.664.896.035.6737.25 20087.054.996.595.0271.02 20108.645.285.725.2570.25 201615.809.347.663.7641.08 High school200613.029.126.763.2838.01 200814.178.077.773.5722.45 201014.998.075.853.3628.54 201625.5710.007.903.4327.86 Combined200645.5730.8422.568.0852.40 200827.4323.6923.258.5055.94 201042.5123.8018.9210.9252.75 201647.2541.7218.719.8679.05 School size categories - based on CCD frame variables (School)Less than 300200624.9715.5312.794.0531.33 200827.3916.5613.105.6925.86 201027.0820.0313.607.3032.07 201653.6429.5114.395.6832.66 300 - 499200621.448.7011.183.8675.91 200819.6012.529.604.5542.79 201017.9411.279.514.8935.93 201637.6220.2213.294.0529.44 500 - 999200612.258.037.954.2329.95 20089.918.548.184.5345.46 201011.747.667.013.4934.20 201620.7811.268.613.7825.58 1,000 or more200611.555.388.414.3435.66 200812.5910.387.335.0552.60 201016.448.246.894.2738.98 201619.4610.078.843.8835.07 Urbanicity - Based on Urban-centric location of school - from CCD (School)City200615.1010.389.524.7934.19 200815.3010.019.895.9533.25 201017.039.309.705.3428.62 201626.1611.7311.704.3035.78 Suburb200612.557.988.052.8534.74 200814.8512.708.944.3732.46 201018.2410.978.314.3036.29 201627.6712.939.852.9823.60 Town200626.2014.8116.479.2960.80 200819.7013.3113.666.0466.45 201024.1013.639.485.86103.75 201632.7718.2312.517.0279.69 Rural200617.4110.8210.294.3836.12 200816.4410.818.794.0243.72 201019.4312.419.314.2234.75 201630.5017.3813.804.7933.89 Level of crime where students liveHigh level of crime200621.1516.2320.2710.7285.24 200824.2722.1520.7010.8660.88 201031.6918.0514.5410.90100.79 201635.5521.8917.819.7686.25 Moderate level of crime200618.4410.699.226.0143.67 200812.9010.708.807.4243.22 201020.1911.769.606.9435.08 201628.2416.9513.805.6553.44 Low level of crime200611.907.646.652.4927.92 200812.777.646.532.7325.33 201012.906.217.723.2823.69 201624.0911.598.772.9421.10 Students come from areas with very different levels of crime200620.1913.0115.876.2148.83 200817.3414.1616.665.2542.57 201023.4212.5910.877.4744.37 201636.6119.0318.235.0645.34 Weighted Sample Sizes (n/1,000s)Total200683.2 200883.0 201082.8 201683.6 School grades offered - based on CCD frame variables (School)Primary200648.6 200849.2 201048.9 201649.1 Middle200615.5 200815.3 201015.3 201615.6 High school200611.7 200811.9 201012.2 201612.8 Combined20067.4 20086.6 20106.4 20166.2 School size categories - based on CCD frame variables (School)Less than 300200620.8 200819.2 201018.9 201618.2 300 - 499200623.8 200824.3 201025.2 201625.0 500 - 999200629.3 200830.2 201029.8 201631.7 1,000 or more20069.3 20089.3 20108.9 20168.7 Urbanicity - Based on Urban-centric location of school - from CCD (School)City200621.0 200821.3 201021.5 201622.8 Suburb200627.6 200823.9 201023.8 201627.4 Town20068.2 200811.8 201012.1 201611.0 Rural200626.4 200826.0 201025.3 201622.5 Level of crime where students liveHigh level of crime20066.5 20086.2 20105.9 20167.4 Moderate level of crime200615.9 200817.1 201018.4 201617.5 Low level of crime200650.3 200849.2 201047.7 201648.4 Students come from areas with very different levels of crime200610.5 200810.5 201010.7 201610.4 Disciplinary occurrences: Student bullying by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Disciplinary occurrences: Student bullyingHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal20066.7[5.52-8.08]17.8[15.92-19.85]18.4[16.61-20.38]55.0[52.67-57.32]2.1[1.41-3.09]100%20088.5[7.18-10.05]16.8[15.06-18.60]20.3[18.47-22.22]52.4[50.09-54.66]2.1[1.45-2.95]100%20106.8[5.70-8.21]16.2[14.48-18.11]22.6[20.65-24.68]51.7[49.11-54.37]2.6[1.81-3.71]100%20162.8[2.06-3.82]9.1[7.81-10.53]18.6[16.47-20.96]65.5[62.22-68.57]4.0[2.80-5.80]100%School grades offered - based on CCD frame variables (School)Primary20064.3[3.01-6.17]16.3[13.35-19.65]15.8[12.92-19.14]61.2[57.13-65.17]2.4[1.38-4.17]100%20085.6[3.94-7.97]14.9[12.23-17.98]20.4[17.77-23.29]57.1[53.89-60.19]2.0[1.15-3.55]100%20105.7[4.12-7.77]13.9[11.39-16.90]20.4[17.73-23.40]56.8[52.69-60.81]3.2[2.07-4.91]100%20161.8 ![0.99-3.40]6.3[4.70-8.33]15.6[12.56-19.31]70.5[66.02-74.66]5.7[3.67-8.82]100%Middle200613.8[11.09-17.01]29.2[26.45-32.19]25.0[22.11-28.16]31.1[27.71-34.80]0.8 ![0.39-1.74]100%200817.5[15.14-20.10]26.0[23.50-28.72]22.5[19.62-25.57]33.7[30.43-37.23]0.3 !![0.07-1.22]100%201013.2[11.07-15.66]25.4[22.84-28.24]26.1[23.23-29.23]35.0[31.37-38.73]0.3 !![0.07-1.12]100%20165.0[3.60-6.79]16.8[13.92-20.25]22.5[19.20-26.11]54.6[50.49-58.72]1.1 ![0.48-2.49]100%High school20068.1[6.20-10.47]14.3[11.84-17.08]20.7[18.06-23.69]55.8[52.08-59.43]1.1 ![0.53-2.42]100%20087.5[5.62-9.92]14.2[12.04-16.65]21.1[17.99-24.58]53.6[49.74-57.42]3.6[2.30-5.66]100%20103.7[2.74-4.99]16.1[13.65-18.88]25.0[22.17-28.03]53.3[49.67-56.85]1.9[1.09-3.44]100%20163.4[2.03-5.67]11.3[9.21-13.76]21.0[17.89-24.56]62.5[58.10-66.69]1.8[1.02-3.11]100%Combined20065.1 ![2.00-12.31]9.5 ![5.04-17.28]18.2[11.36-27.96]63.0[52.36-72.56]4.1 !![1.42-11.43]100%200811.0[6.24-18.68]13.9[8.50-21.91]12.9[7.99-20.25]58.4[48.25-67.97]3.7 !![1.19-11.04]100%20106.7 ![2.78-15.13]12.0[7.32-18.97]26.4[17.62-37.54]50.2[39.34-61.01]4.8 !![1.63-13.29]100%20163.9 ![1.47-9.72]7.1 ![3.02-15.92]27.5[18.41-38.89]58.8[46.84-69.72]2.8 !![0.55-12.76]100%School size categories - based on CCD frame variables (School)Less than 30020065.0[3.04-8.27]14.3[10.39-19.37]15.0[11.53-19.27]60.9[55.80-65.68]4.8 ![2.54-8.90]100%20086.1[3.48-10.43]12.6[9.00-17.47]18.3[13.93-23.55]57.6[50.91-63.99]5.4[3.22-9.07]100%20104.7[2.72-8.06]11.7[7.78-17.35]23.5[17.68-30.50]54.4[46.41-62.23]5.6 ![2.92-10.54]100%20161.3 !![0.45-3.90]5.1[2.78-9.06]19.9[14.75-26.25]68.3[60.08-75.59]5.4 ![2.76-10.19]100%300 - 49920064.1[2.62-6.20]17.8[14.93-21.17]19.9[15.81-24.76]57.3[52.79-61.66]0.9 !![0.20-4.14]100%20086.6[4.46-9.80]14.2[10.97-18.12]21.0[17.23-25.33]56.9[51.66-62.04]1.3 ![0.53-2.95]100%20107.2[4.98-10.23]16.8[13.34-20.97]21.3[17.49-25.61]52.8[47.62-57.95]1.9 ![0.93-3.95]100%20163.1 ![1.44-6.48]6.5[4.32-9.71]15.3[11.63-19.82]70.5[64.46-75.89]4.6[2.55-8.28]100%500 - 99920068.8[6.89-11.27]19.7[16.70-23.04]18.7[15.88-21.86]51.3[46.91-55.61]1.5[0.84-2.78]100%200810.7[8.72-12.98]19.9[16.71-23.55]21.3[18.03-25.04]47.4[43.09-51.69]0.7 ![0.29-1.82]100%20107.9[6.22-9.97]17.4[14.86-20.22]21.6[18.68-24.76]51.3[47.74-54.93]1.8 ![0.91-3.59]100%20163.1[2.06-4.75]10.9[8.68-13.63]20.0[16.79-23.72]62.5[57.67-67.15]3.4[2.02-5.64]100%1,000 or more200610.3[8.14-12.94]19.6[17.59-21.84]21.5[18.06-25.30]47.8[43.69-52.02]0.8 ![0.38-1.59]100%200811.4[8.81-14.60]21.8[17.57-26.66]19.2[16.54-22.20]46.0[41.41-50.72]1.6 !![0.55-4.52]100%20106.9[4.95-9.58]20.1[16.97-23.63]27.9[24.23-31.96]44.4[40.61-48.21]0.7 ![0.31-1.47]100%20163.9[2.63-5.74]18.2[14.77-22.13]20.3[16.96-24.19]55.7[51.30-59.96]1.9 ![0.95-3.88]100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City20069.1[6.66-12.21]20.4[16.51-25.04]16.7[13.76-20.17]51.7[46.68-56.60]2.1 ![1.06-4.18]100%200810.1[7.40-13.67]17.4[14.18-21.19]21.5[17.50-26.02]48.6[42.80-54.36]2.5 ![1.26-4.77]100%201010.0[7.06-13.97]17.0[14.09-20.47]22.1[18.08-26.68]47.3[42.29-52.41]3.6[1.99-6.27]100%20163.7[2.16-6.16]9.2[7.26-11.62]18.2[14.30-22.86]65.2[59.39-70.62]3.7 ![1.80-7.53]100%Suburb20066.3[4.90-8.10]16.5[14.05-19.35]19.3[16.36-22.60]56.3[53.05-59.49]1.6 ![0.78-3.16]100%20088.3[6.17-11.19]16.3[12.55-20.89]18.0[15.01-21.49]54.9[50.09-59.70]2.4 ![1.25-4.59]100%20106.6[4.53-9.41]13.3[10.65-16.54]22.4[18.87-26.34]55.0[50.24-59.73]2.7 ![1.30-5.55]100%20162.5[1.40-4.26]7.9[6.07-10.20]15.8[12.91-19.17]67.6[63.44-71.53]6.2[3.87-9.95]100%Town20066.5[3.82-10.91]21.8[16.03-29.01]22.6[15.97-30.88]47.0[38.39-55.76]2.1 !![0.61-6.96]100%200810.0[6.69-14.73]20.3[15.40-26.26]19.2[14.49-25.05]49.1[43.17-55.03]1.4 !![0.36-5.16]100%20105.2[3.20-8.42]21.0[15.81-27.31]25.2[20.70-30.28]48.3[42.63-53.95]0.3 !![0.04-2.58]100%20164.4 ![2.27-8.42]13.8[9.50-19.72]21.6[16.70-27.57]58.8[50.36-66.81]1.3 !![0.25-6.12]100%Rural20065.2[3.68-7.39]15.8[12.64-19.51]17.6[14.24-21.52]58.8[53.56-63.87]2.6 ![1.25-5.32]100%20086.7[4.78-9.24]15.1[12.06-18.62]21.9[18.26-25.98]54.6[50.20-59.01]1.8 ![0.73-4.19]100%20105.2[3.52-7.67]16.0[12.37-20.35]22.0[18.18-26.41]54.0[49.45-58.58]2.8 ![1.37-5.50]100%20161.6 ![0.86-2.93]8.1[5.66-11.37]21.0[15.75-27.37]66.3[59.68-72.40]3.0 ![1.52-5.93]100%Level of crime where students liveHigh level of crime200618.3[11.73-27.32]23.7[16.82-32.21]16.6[10.91-24.54]39.9[31.68-48.70]1.5 !![0.27-8.20]100%200815.3[9.22-24.30]19.1[11.97-29.00]22.4[14.47-33.08]41.4[32.71-50.56]1.8 !![0.54-6.15]100%201013.7 ![7.04-24.85]16.6[11.44-23.58]26.5[19.48-34.87]43.1[34.00-52.64]0.2 !![0.02-1.22]100%20167.5 ![3.59-14.86]12.0[7.63-18.33]26.7[18.26-37.23]51.2[41.24-61.06]2.7 !![0.46-13.95]100%Moderate level of crime20065.6[3.84-8.05]21.3[17.09-26.24]24.5[20.24-29.30]46.1[40.62-51.70]2.5 ![1.04-5.96]100%200811.7[8.96-15.03]21.3[17.04-26.18]25.7[21.42-30.50]40.0[34.18-46.05]1.4 ![0.60-3.38]100%20108.6[5.70-12.81]21.0[16.47-26.39]23.5[19.30-28.38]43.7[37.70-49.82]3.2 ![1.56-6.38]100%20164.0[2.26-7.01]12.0[8.44-16.66]20.5[15.41-26.80]60.7[53.63-67.34]2.8 !![0.95-8.03]100%Low level of crime20064.8[3.81-6.14]15.8[13.51-18.36]17.0[14.85-19.39]60.2[57.18-63.21]2.1[1.21-3.72]100%20086.1[4.75-7.93]15.1[12.94-17.59]19.4[16.94-22.02]57.4[54.19-60.47]2.0[1.21-3.35]100%20105.1[3.93-6.59]14.3[12.60-16.17]21.3[18.14-24.74]56.7[52.96-60.44]2.6[1.62-4.20]100%20161.9[1.18-3.09]7.6[6.02-9.58]17.4[14.55-20.69]68.5[64.35-72.43]4.5[2.96-6.91]100%Students come from areas with very different levels of crime200610.0[6.59-14.80]18.5[14.12-23.80]17.1[12.35-23.32]52.8[46.22-59.34]1.6 ![0.59-4.17]100%200810.4[7.30-14.64]15.7[11.75-20.73]14.5[10.31-20.10]55.8[49.88-61.61]3.5 ![1.47-8.09]100%20107.8[4.86-12.42]16.3[12.59-20.87]24.9[19.84-30.68]48.1[41.00-55.36]2.8 ![1.15-6.80]100%20161.7 ![0.79-3.42]9.0[6.10-13.08]15.2[10.43-21.64]69.4[61.94-75.98]4.7 ![1.88-11.46]100%2006200820102016 Disciplinary occurrences: Student bullyingDisciplinary occurrences: Student bullyingDisciplinary occurrences: Student bullyingDisciplinary occurrences: Student bullying Happens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensEstimatesTotal6.717.818.455.02.18.516.820.352.42.16.816.222.651.72.62.89.118.665.54.0School grades offered - based on CCD frame variables (School)Primary4.316.315.861.22.45.614.920.457.12.05.713.920.456.83.21.86.315.670.55.7Middle13.829.225.031.10.817.526.022.533.70.313.225.426.135.00.35.016.822.554.61.1High school8.114.320.755.81.17.514.221.153.63.63.716.125.053.31.93.411.321.062.51.8Combined5.19.518.263.04.111.013.912.958.43.76.712.026.450.24.83.97.127.558.82.8School size categories - based on CCD frame variables (School)Less than 3005.014.315.060.94.86.112.618.357.65.44.711.723.554.45.61.35.119.968.35.4300 - 4994.117.819.957.30.96.614.221.056.91.37.216.821.352.81.93.16.515.370.54.6500 - 9998.819.718.751.31.510.719.921.347.40.77.917.421.651.31.83.110.920.062.53.41,000 or more10.319.621.547.80.811.421.819.246.01.66.920.127.944.40.73.918.220.355.71.9Urbanicity - Based on Urban-centric location of school - from CCD (School)City9.120.416.751.72.110.117.421.548.62.510.017.022.147.33.63.79.218.265.23.7Suburb6.316.519.356.31.68.316.318.054.92.46.613.322.455.02.72.57.915.867.66.2Town6.521.822.647.02.110.020.319.249.11.45.221.025.248.30.34.413.821.658.81.3Rural5.215.817.658.82.66.715.121.954.61.85.216.022.054.02.81.68.121.066.33.0Level of crime where students liveHigh level of crime18.323.716.639.91.515.319.122.441.41.813.716.626.543.10.27.512.026.751.22.7Moderate level of crime5.621.324.546.12.511.721.325.740.01.48.621.023.543.73.24.012.020.560.72.8Low level of crime4.815.817.060.22.16.115.119.457.42.05.114.321.356.72.61.97.617.468.54.5Students come from areas with very different levels of crime10.018.517.152.81.610.415.714.555.83.57.816.324.948.12.81.79.015.269.44.72006200820102016 Disciplinary occurrences: Student bullyingDisciplinary occurrences: Student bullyingDisciplinary occurrences: Student bullyingDisciplinary occurrences: Student bullying Happens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensEstimatesTotal6.717.818.455.02.18.516.820.352.42.16.816.222.651.72.62.89.118.665.54.0School grades offered - based on CCD frame variables (School)Primary4.316.315.861.22.45.614.920.457.12.05.713.920.456.83.21.86.315.670.55.7Middle13.829.225.031.10.817.526.022.533.70.313.225.426.135.00.35.016.822.554.61.1High school8.114.320.755.81.17.514.221.153.63.63.716.125.053.31.93.411.321.062.51.8Combined5.19.518.263.04.111.013.912.958.43.76.712.026.450.24.83.97.127.558.82.8School size categories - based on CCD frame variables (School)Less than 3005.014.315.060.94.86.112.618.357.65.44.711.723.554.45.61.35.119.968.35.4300 - 4994.117.819.957.30.96.614.221.056.91.37.216.821.352.81.93.16.515.370.54.6500 - 9998.819.718.751.31.510.719.921.347.40.77.917.421.651.31.83.110.920.062.53.41,000 or more10.319.621.547.80.811.421.819.246.01.66.920.127.944.40.73.918.220.355.71.9Urbanicity - Based on Urban-centric location of school - from CCD (School)City9.120.416.751.72.110.117.421.548.62.510.017.022.147.33.63.79.218.265.23.7Suburb6.316.519.356.31.68.316.318.054.92.46.613.322.455.02.72.57.915.867.66.2Town6.521.822.647.02.110.020.319.249.11.45.221.025.248.30.34.413.821.658.81.3Rural5.215.817.658.82.66.715.121.954.61.85.216.022.054.02.81.68.121.066.33.0Level of crime where students liveHigh level of crime18.323.716.639.91.515.319.122.441.41.813.716.626.543.10.27.512.026.751.22.7Moderate level of crime5.621.324.546.12.511.721.325.740.01.48.621.023.543.73.24.012.020.560.72.8Low level of crime4.815.817.060.22.16.115.119.457.42.05.114.321.356.72.61.97.617.468.54.5Students come from areas with very different levels of crime10.018.517.152.81.610.415.714.555.83.57.816.324.948.12.81.79.015.269.44.7Standard Error (BRR)Total0.640.980.941.160.410.710.880.931.140.370.620.901.001.310.470.430.681.121.580.73School grades offered - based on CCD frame variables (School)Primary0.771.571.552.000.660.991.431.371.570.570.901.371.412.030.690.570.901.682.161.25Middle1.471.431.511.770.311.231.301.481.690.211.141.341.491.840.190.781.571.722.050.45High school1.051.301.401.830.431.061.151.641.910.810.561.301.461.790.560.871.131.662.140.50Combined2.322.944.115.092.163.023.293.014.972.082.832.854.995.482.531.822.975.145.792.20School size categories - based on CCD frame variables (School)Less than 3001.262.221.922.471.501.672.092.393.271.411.282.353.203.971.800.721.492.863.881.75300 - 4990.871.552.232.210.701.301.772.012.590.541.291.892.022.580.691.161.322.032.851.36500 - 9991.081.581.492.170.461.061.701.742.150.330.931.331.511.790.620.651.231.722.370.871,000 or more1.191.061.802.080.281.432.261.412.320.841.141.661.931.890.260.761.831.802.160.68Urbanicity - Based on Urban-centric location of school - from CCD (School)City1.372.121.592.480.721.551.742.122.890.821.701.592.142.531.020.961.082.132.801.33Suburb0.791.321.551.610.551.242.071.612.400.781.201.461.862.370.980.681.021.562.021.47Town1.713.233.724.371.281.972.702.632.970.921.262.862.392.830.341.452.522.714.131.01Rural0.911.711.812.570.941.101.631.922.200.771.011.982.052.280.960.491.402.893.181.03Level of crime where students liveHigh level of crime3.863.843.374.281.323.714.234.644.491.134.333.003.854.690.162.652.624.755.002.29Moderate level of crime1.032.282.262.771.101.502.272.262.970.621.742.472.263.031.121.132.032.833.431.50Low level of crime0.581.211.131.500.600.781.151.261.570.510.660.891.641.860.620.460.881.532.010.96Students come from areas with very different levels of crime2.012.402.723.280.771.812.232.422.931.491.842.052.703.601.260.601.712.773.512.15Relative Standard Error (%)Total9.505.505.092.1119.548.375.254.612.1717.639.095.574.442.5317.9515.397.456.002.4218.13School grades offered - based on CCD frame variables (School)Primary17.879.639.793.2727.5017.549.596.742.7528.0815.829.846.913.5721.5230.8614.2710.723.0621.84Middle10.664.896.035.6737.257.054.996.595.0271.028.645.285.725.2570.2515.809.347.663.7641.08High school13.029.126.763.2838.0114.178.077.773.5722.4514.998.075.853.3628.5425.5710.007.903.4327.86Combined45.5730.8422.568.0852.4027.4323.6923.258.5055.9442.5123.8018.9210.9252.7547.2541.7218.719.8679.05School size categories - based on CCD frame variables (School)Less than 30024.9715.5312.794.0531.3327.3916.5613.105.6925.8627.0820.0313.607.3032.0753.6429.5114.395.6832.66300 - 49921.448.7011.183.8675.9119.6012.529.604.5542.7917.9411.279.514.8935.9337.6220.2213.294.0529.44500 - 99912.258.037.954.2329.959.918.548.184.5345.4611.747.667.013.4934.2020.7811.268.613.7825.581,000 or more11.555.388.414.3435.6612.5910.387.335.0552.6016.448.246.894.2738.9819.4610.078.843.8835.07Urbanicity - Based on Urban-centric location of school - from CCD (School)City15.1010.389.524.7934.1915.3010.019.895.9533.2517.039.309.705.3428.6226.1611.7311.704.3035.78Suburb12.557.988.052.8534.7414.8512.708.944.3732.4618.2410.978.314.3036.2927.6712.939.852.9823.60Town26.2014.8116.479.2960.8019.7013.3113.666.0466.4524.1013.639.485.86103.7532.7718.2312.517.0279.69Rural17.4110.8210.294.3836.1216.4410.818.794.0243.7219.4312.419.314.2234.7530.5017.3813.804.7933.89Level of crime where students liveHigh level of crime21.1516.2320.2710.7285.2424.2722.1520.7010.8660.8831.6918.0514.5410.90100.7935.5521.8917.819.7686.25Moderate level of crime18.4410.699.226.0143.6712.9010.708.807.4243.2220.1911.769.606.9435.0828.2416.9513.805.6553.44Low level of crime11.907.646.652.4927.9212.777.646.532.7325.3312.906.217.723.2823.6924.0911.598.772.9421.10Students come from areas with very different levels of crime20.1913.0115.876.2148.8317.3414.1616.665.2542.5723.4212.5910.877.4744.3736.6119.0318.235.0645.34Weighted Sample Sizes (n/1,000s)Total83.2 83.0 82.8 83.6 School grades offered - based on CCD frame variables (School)Primary48.6 49.2 48.9 49.1 Middle15.5 15.3 15.3 15.6 High school11.7 11.9 12.2 12.8 Combined7.4 6.6 6.4 6.2 School size categories - based on CCD frame variables (School)Less than 30020.8 19.2 18.9 18.2 300 - 49923.8 24.3 25.2 25.0 500 - 99929.3 30.2 29.8 31.7 1,000 or more9.3 9.3 8.9 8.7 Urbanicity - Based on Urban-centric location of school - from CCD (School)City21.0 21.3 21.5 22.8 Suburb27.6 23.9 23.8 27.4 Town8.2 11.8 12.1 11.0 Rural26.4 26.0 25.3 22.5 Level of crime where students liveHigh level of crime6.5 6.2 5.9 7.4 Moderate level of crime15.9 17.1 18.4 17.5 Low level of crime50.3 49.2 47.7 48.4 Students come from areas with very different levels of crime10.5 10.5 10.7 10.4 2006200820102016 Disciplinary occurrences: Student bullyingDisciplinary occurrences: Student bullyingDisciplinary occurrences: Student bullyingDisciplinary occurrences: Student bullying Happens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happensHappens dailyHappens at least once a weekHappens at least once a monthHappens on occasionNever happens Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal6.7[5.52-8.08]17.8[15.92-19.85]18.4[16.61-20.38]55.0[52.67-57.32]2.1[1.41-3.09]8.5[7.18-10.05]16.8[15.06-18.60]20.3[18.47-22.22]52.4[50.09-54.66]2.1[1.45-2.95]6.8[5.70-8.21]16.2[14.48-18.11]22.6[20.65-24.68]51.7[49.11-54.37]2.6[1.81-3.71]2.8[2.06-3.82]9.1[7.81-10.53]18.6[16.47-20.96]65.5[62.22-68.57]4.0[2.80-5.80]School grades offered - based on CCD frame variables (School)Primary4.3[3.01-6.17]16.3[13.35-19.65]15.8[12.92-19.14]61.2[57.13-65.17]2.4[1.38-4.17]5.6[3.94-7.97]14.9[12.23-17.98]20.4[17.77-23.29]57.1[53.89-60.19]2.0[1.15-3.55]5.7[4.12-7.77]13.9[11.39-16.90]20.4[17.73-23.40]56.8[52.69-60.81]3.2[2.07-4.91]1.8 ![0.99-3.40]6.3[4.70-8.33]15.6[12.56-19.31]70.5[66.02-74.66]5.7[3.67-8.82]Middle13.8[11.09-17.01]29.2[26.45-32.19]25.0[22.11-28.16]31.1[27.71-34.80]0.8 ![0.39-1.74]17.5[15.14-20.10]26.0[23.50-28.72]22.5[19.62-25.57]33.7[30.43-37.23]0.3 !![0.07-1.22]13.2[11.07-15.66]25.4[22.84-28.24]26.1[23.23-29.23]35.0[31.37-38.73]0.3 !![0.07-1.12]5.0[3.60-6.79]16.8[13.92-20.25]22.5[19.20-26.11]54.6[50.49-58.72]1.1 ![0.48-2.49]High school8.1[6.20-10.47]14.3[11.84-17.08]20.7[18.06-23.69]55.8[52.08-59.43]1.1 ![0.53-2.42]7.5[5.62-9.92]14.2[12.04-16.65]21.1[17.99-24.58]53.6[49.74-57.42]3.6[2.30-5.66]3.7[2.74-4.99]16.1[13.65-18.88]25.0[22.17-28.03]53.3[49.67-56.85]1.9[1.09-3.44]3.4[2.03-5.67]11.3[9.21-13.76]21.0[17.89-24.56]62.5[58.10-66.69]1.8[1.02-3.11]Combined5.1 ![2.00-12.31]9.5 ![5.04-17.28]18.2[11.36-27.96]63.0[52.36-72.56]4.1 !![1.42-11.43]11.0[6.24-18.68]13.9[8.50-21.91]12.9[7.99-20.25]58.4[48.25-67.97]3.7 !![1.19-11.04]6.7 ![2.78-15.13]12.0[7.32-18.97]26.4[17.62-37.54]50.2[39.34-61.01]4.8 !![1.63-13.29]3.9 ![1.47-9.72]7.1 ![3.02-15.92]27.5[18.41-38.89]58.8[46.84-69.72]2.8 !![0.55-12.76]School size categories - based on CCD frame variables (School)Less than 3005.0[3.04-8.27]14.3[10.39-19.37]15.0[11.53-19.27]60.9[55.80-65.68]4.8 ![2.54-8.90]6.1[3.48-10.43]12.6[9.00-17.47]18.3[13.93-23.55]57.6[50.91-63.99]5.4[3.22-9.07]4.7[2.72-8.06]11.7[7.78-17.35]23.5[17.68-30.50]54.4[46.41-62.23]5.6 ![2.92-10.54]1.3 !![0.45-3.90]5.1[2.78-9.06]19.9[14.75-26.25]68.3[60.08-75.59]5.4 ![2.76-10.19]300 - 4994.1[2.62-6.20]17.8[14.93-21.17]19.9[15.81-24.76]57.3[52.79-61.66]0.9 !![0.20-4.14]6.6[4.46-9.80]14.2[10.97-18.12]21.0[17.23-25.33]56.9[51.66-62.04]1.3 ![0.53-2.95]7.2[4.98-10.23]16.8[13.34-20.97]21.3[17.49-25.61]52.8[47.62-57.95]1.9 ![0.93-3.95]3.1 ![1.44-6.48]6.5[4.32-9.71]15.3[11.63-19.82]70.5[64.46-75.89]4.6[2.55-8.28]500 - 9998.8[6.89-11.27]19.7[16.70-23.04]18.7[15.88-21.86]51.3[46.91-55.61]1.5[0.84-2.78]10.7[8.72-12.98]19.9[16.71-23.55]21.3[18.03-25.04]47.4[43.09-51.69]0.7 ![0.29-1.82]7.9[6.22-9.97]17.4[14.86-20.22]21.6[18.68-24.76]51.3[47.74-54.93]1.8 ![0.91-3.59]3.1[2.06-4.75]10.9[8.68-13.63]20.0[16.79-23.72]62.5[57.67-67.15]3.4[2.02-5.64]1,000 or more10.3[8.14-12.94]19.6[17.59-21.84]21.5[18.06-25.30]47.8[43.69-52.02]0.8 ![0.38-1.59]11.4[8.81-14.60]21.8[17.57-26.66]19.2[16.54-22.20]46.0[41.41-50.72]1.6 !![0.55-4.52]6.9[4.95-9.58]20.1[16.97-23.63]27.9[24.23-31.96]44.4[40.61-48.21]0.7 ![0.31-1.47]3.9[2.63-5.74]18.2[14.77-22.13]20.3[16.96-24.19]55.7[51.30-59.96]1.9 ![0.95-3.88]Urbanicity - Based on Urban-centric location of school - from CCD (School)City9.1[6.66-12.21]20.4[16.51-25.04]16.7[13.76-20.17]51.7[46.68-56.60]2.1 ![1.06-4.18]10.1[7.40-13.67]17.4[14.18-21.19]21.5[17.50-26.02]48.6[42.80-54.36]2.5 ![1.26-4.77]10.0[7.06-13.97]17.0[14.09-20.47]22.1[18.08-26.68]47.3[42.29-52.41]3.6[1.99-6.27]3.7[2.16-6.16]9.2[7.26-11.62]18.2[14.30-22.86]65.2[59.39-70.62]3.7 ![1.80-7.53]Suburb6.3[4.90-8.10]16.5[14.05-19.35]19.3[16.36-22.60]56.3[53.05-59.49]1.6 ![0.78-3.16]8.3[6.17-11.19]16.3[12.55-20.89]18.0[15.01-21.49]54.9[50.09-59.70]2.4 ![1.25-4.59]6.6[4.53-9.41]13.3[10.65-16.54]22.4[18.87-26.34]55.0[50.24-59.73]2.7 ![1.30-5.55]2.5[1.40-4.26]7.9[6.07-10.20]15.8[12.91-19.17]67.6[63.44-71.53]6.2[3.87-9.95]Town6.5[3.82-10.91]21.8[16.03-29.01]22.6[15.97-30.88]47.0[38.39-55.76]2.1 !![0.61-6.96]10.0[6.69-14.73]20.3[15.40-26.26]19.2[14.49-25.05]49.1[43.17-55.03]1.4 !![0.36-5.16]5.2[3.20-8.42]21.0[15.81-27.31]25.2[20.70-30.28]48.3[42.63-53.95]0.3 !![0.04-2.58]4.4 ![2.27-8.42]13.8[9.50-19.72]21.6[16.70-27.57]58.8[50.36-66.81]1.3 !![0.25-6.12]Rural5.2[3.68-7.39]15.8[12.64-19.51]17.6[14.24-21.52]58.8[53.56-63.87]2.6 ![1.25-5.32]6.7[4.78-9.24]15.1[12.06-18.62]21.9[18.26-25.98]54.6[50.20-59.01]1.8 ![0.73-4.19]5.2[3.52-7.67]16.0[12.37-20.35]22.0[18.18-26.41]54.0[49.45-58.58]2.8 ![1.37-5.50]1.6 ![0.86-2.93]8.1[5.66-11.37]21.0[15.75-27.37]66.3[59.68-72.40]3.0 ![1.52-5.93]Level of crime where students liveHigh level of crime18.3[11.73-27.32]23.7[16.82-32.21]16.6[10.91-24.54]39.9[31.68-48.70]1.5 !![0.27-8.20]15.3[9.22-24.30]19.1[11.97-29.00]22.4[14.47-33.08]41.4[32.71-50.56]1.8 !![0.54-6.15]13.7 ![7.04-24.85]16.6[11.44-23.58]26.5[19.48-34.87]43.1[34.00-52.64]0.2 !![0.02-1.22]7.5 ![3.59-14.86]12.0[7.63-18.33]26.7[18.26-37.23]51.2[41.24-61.06]2.7 !![0.46-13.95]Moderate level of crime5.6[3.84-8.05]21.3[17.09-26.24]24.5[20.24-29.30]46.1[40.62-51.70]2.5 ![1.04-5.96]11.7[8.96-15.03]21.3[17.04-26.18]25.7[21.42-30.50]40.0[34.18-46.05]1.4 ![0.60-3.38]8.6[5.70-12.81]21.0[16.47-26.39]23.5[19.30-28.38]43.7[37.70-49.82]3.2 ![1.56-6.38]4.0[2.26-7.01]12.0[8.44-16.66]20.5[15.41-26.80]60.7[53.63-67.34]2.8 !![0.95-8.03]Low level of crime4.8[3.81-6.14]15.8[13.51-18.36]17.0[14.85-19.39]60.2[57.18-63.21]2.1[1.21-3.72]6.1[4.75-7.93]15.1[12.94-17.59]19.4[16.94-22.02]57.4[54.19-60.47]2.0[1.21-3.35]5.1[3.93-6.59]14.3[12.60-16.17]21.3[18.14-24.74]56.7[52.96-60.44]2.6[1.62-4.20]1.9[1.18-3.09]7.6[6.02-9.58]17.4[14.55-20.69]68.5[64.35-72.43]4.5[2.96-6.91]Students come from areas with very different levels of crime10.0[6.59-14.80]18.5[14.12-23.80]17.1[12.35-23.32]52.8[46.22-59.34]1.6 ![0.59-4.17]10.4[7.30-14.64]15.7[11.75-20.73]14.5[10.31-20.10]55.8[49.88-61.61]3.5 ![1.47-8.09]7.8[4.86-12.42]16.3[12.59-20.87]24.9[19.84-30.68]48.1[41.00-55.36]2.8 ![1.15-6.80]1.7 ![0.79-3.42]9.0[6.10-13.08]15.2[10.43-21.64]69.4[61.94-75.98]4.7 ![1.88-11.46]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: C0376, FR_LVEL, FR_SIZE, FR_URBAN and C0560. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: C0376 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_LVEL (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_SIZE (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_LOC4 (SSOCS:2006), C0560 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016) and FR_URBAN (SSOCS:2008, SSOCS:2010, SSOCS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006, 2007–08 School Survey on Crime and Safety (SSOCS), 2008, 2009-10 School Survey on Crime and Safety (SSOCS), 2010 and 2015-16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES TrendStats on 7/9/2018.mgbkd6bmgbkd6b4Number of students transferred from school by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Number of students transferred from school01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or moreTotalEstimatesTotal20062.645.021.410.57.15.93.73.7100%20082.247.722.99.75.66.32.33.2100%20102.849.720.411.26.45.02.02.6100%20162.353.521.19.35.64.01.92.2100%School grades offered - based on CCD frame variables (School)Primary20062.547.323.410.57.04.93.31.1100%20082.648.725.29.64.85.52.01.5100%20103.452.420.111.66.13.91.3 !1.2100%20162.7 !54.721.29.06.03.61.5 !1.3 !100%Middle20060.9 !40.121.313.08.88.23.64.1100%20080.5 !!45.523.911.56.37.32.72.3100%20101.546.625.211.06.75.71.41.8100%20162.253.024.210.54.43.01.90.8 !100%High school20061.2 !32.217.79.48.29.96.414.9100%20080.8 !!33.917.611.19.610.74.212.1100%20102.4 !35.018.611.08.69.35.99.2100%20161.7 !40.917.412.17.18.14.58.1100%Combined20068.7 !60.714.87.7 !2.3 !1.7 !!2.0 !2.2 !!100%20085.6 !70.113.73.7 !3.0 !!1.6 !!#2.3 !100%20101.8 !!64.714.68.6 !3.8 !!3.6 !0.8 !!2.2 !100%20161.6 !!70.619.63.3 !!2.9 !!1.2 !!#0.8 !!100%School size categories - based on CCD frame variables (School)Less than 30020068.672.215.02.6 !0.9 !0.6 !!#0.1 !!100%20084.5 !73.815.03.2 !2.1 !1.1 !!#0.3 !!100%20106.3 !77.79.44.8 !0.8 !!0.7 !!#0.2 !!100%20163.3 !80.88.93.8 !1.4 !!0.3 !!1.0 !!0.6 !!100%300 - 49920060.5 !!51.226.512.25.02.61.4 !0.4 !!100%20082.2 !51.430.07.94.03.0 !0.8 !!0.7 !!100%20101.9 !57.023.58.85.82.6 !0.1 !!0.3 !!100%20161.7 !59.423.68.04.8 !2.1 !0.2 !!0.2 !!100%500 - 99920060.8 !31.223.614.912.49.05.52.6100%20081.4 !37.824.614.87.89.02.62.0100%20101.8 !36.126.616.97.96.52.41.8100%20162.7 !40.627.512.67.64.72.41.8 !100%1,000 or more20060.2 !!11.915.810.39.716.811.823.4100%20080.2 !!16.415.511.110.116.610.120.0100%20101.0 !15.614.311.915.015.89.816.6100%20161.0 !25.916.013.09.914.67.212.7100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City20061.9 !31.922.913.57.49.46.36.6100%20082.5 !38.718.411.96.812.13.75.9100%20102.841.420.712.38.56.82.94.5100%20163.2 !42.821.610.18.65.63.54.7100%Suburb20061.0 !!40.122.111.08.57.74.75.0100%20081.0 !!45.524.39.56.76.22.74.0100%20101.3 !46.221.611.17.26.22.73.7100%20161.9 !51.422.38.66.94.52.12.3100%Town20060.7 !!42.625.614.49.43.22.3 !1.8100%20080.2 !!44.134.012.03.43.61.5 !1.2 !!100%20102.5 !45.829.310.85.54.70.9 !0.4 !100%20161.0 !!52.629.211.71.61.9 !1.2 !0.8 !!100%Rural20065.461.418.36.64.72.20.8 !0.6100%20083.958.720.47.04.72.81.2 !1.3100%20104.161.914.810.44.32.51.0 !0.9 !100%20162.7 !67.215.18.43.2 !2.80.5 !0.2 !100%Level of crime where students liveHigh level of crime20060.9 !!22.216.419.415.211.87.76.4100%20085.2 !31.318.410.18.8 !14.84.3 !7.0100%20103.0 !!38.319.89.79.410.32.7 !!6.8100%20163.5 !!32.320.48.3 !14.25.7 !6.9 !8.6100%Moderate level of crime20061.5 !!32.225.313.17.18.05.77.1100%20081.2 !!30.824.214.27.711.84.55.6100%20101.1 !!41.623.312.66.96.93.04.7100%20160.9 !!43.626.810.88.44.12.4 !2.9100%Low level of crime20063.353.620.78.35.64.12.42.0100%20082.558.721.77.64.42.90.9 !1.3100%20103.257.819.49.74.53.21.31.1100%20162.861.918.78.23.03.80.6 !0.8 !100%Students come from areas with very different levels of crime20061.6 !!37.622.311.99.48.04.05.1100%20080.8 !!33.329.711.76.48.03.9 !6.2100%20103.7 !33.920.416.212.77.02.83.3100%20161.6 !46.022.812.97.1 !3.43.6 !!2.7 !100%Number of students transferred from school by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Number of students transferred from school01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or moreTotalEstimatesTotal20062.645.021.410.57.15.93.73.7100%20082.247.722.99.75.66.32.33.2100%20102.849.720.411.26.45.02.02.6100%20162.353.521.19.35.64.01.92.2100%School grades offered - based on CCD frame variables (School)Primary20062.547.323.410.57.04.93.31.1100%20082.648.725.29.64.85.52.01.5100%20103.452.420.111.66.13.91.3 !1.2100%20162.7 !54.721.29.06.03.61.5 !1.3 !100%Middle20060.9 !40.121.313.08.88.23.64.1100%20080.5 !!45.523.911.56.37.32.72.3100%20101.546.625.211.06.75.71.41.8100%20162.253.024.210.54.43.01.90.8 !100%High school20061.2 !32.217.79.48.29.96.414.9100%20080.8 !!33.917.611.19.610.74.212.1100%20102.4 !35.018.611.08.69.35.99.2100%20161.7 !40.917.412.17.18.14.58.1100%Combined20068.7 !60.714.87.7 !2.3 !1.7 !!2.0 !2.2 !!100%20085.6 !70.113.73.7 !3.0 !!1.6 !!#2.3 !100%20101.8 !!64.714.68.6 !3.8 !!3.6 !0.8 !!2.2 !100%20161.6 !!70.619.63.3 !!2.9 !!1.2 !!#0.8 !!100%School size categories - based on CCD frame variables (School)Less than 30020068.672.215.02.6 !0.9 !0.6 !!#0.1 !!100%20084.5 !73.815.03.2 !2.1 !1.1 !!#0.3 !!100%20106.3 !77.79.44.8 !0.8 !!0.7 !!#0.2 !!100%20163.3 !80.88.93.8 !1.4 !!0.3 !!1.0 !!0.6 !!100%300 - 49920060.5 !!51.226.512.25.02.61.4 !0.4 !!100%20082.2 !51.430.07.94.03.0 !0.8 !!0.7 !!100%20101.9 !57.023.58.85.82.6 !0.1 !!0.3 !!100%20161.7 !59.423.68.04.8 !2.1 !0.2 !!0.2 !!100%500 - 99920060.8 !31.223.614.912.49.05.52.6100%20081.4 !37.824.614.87.89.02.62.0100%20101.8 !36.126.616.97.96.52.41.8100%20162.7 !40.627.512.67.64.72.41.8 !100%1,000 or more20060.2 !!11.915.810.39.716.811.823.4100%20080.2 !!16.415.511.110.116.610.120.0100%20101.0 !15.614.311.915.015.89.816.6100%20161.0 !25.916.013.09.914.67.212.7100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City20061.9 !31.922.913.57.49.46.36.6100%20082.5 !38.718.411.96.812.13.75.9100%20102.841.420.712.38.56.82.94.5100%20163.2 !42.821.610.18.65.63.54.7100%Suburb20061.0 !!40.122.111.08.57.74.75.0100%20081.0 !!45.524.39.56.76.22.74.0100%20101.3 !46.221.611.17.26.22.73.7100%20161.9 !51.422.38.66.94.52.12.3100%Town20060.7 !!42.625.614.49.43.22.3 !1.8100%20080.2 !!44.134.012.03.43.61.5 !1.2 !!100%20102.5 !45.829.310.85.54.70.9 !0.4 !100%20161.0 !!52.629.211.71.61.9 !1.2 !0.8 !!100%Rural20065.461.418.36.64.72.20.8 !0.6100%20083.958.720.47.04.72.81.2 !1.3100%20104.161.914.810.44.32.51.0 !0.9 !100%20162.7 !67.215.18.43.2 !2.80.5 !0.2 !100%Level of crime where students liveHigh level of crime20060.9 !!22.216.419.415.211.87.76.4100%20085.2 !31.318.410.18.8 !14.84.3 !7.0100%20103.0 !!38.319.89.79.410.32.7 !!6.8100%20163.5 !!32.320.48.3 !14.25.7 !6.9 !8.6100%Moderate level of crime20061.5 !!32.225.313.17.18.05.77.1100%20081.2 !!30.824.214.27.711.84.55.6100%20101.1 !!41.623.312.66.96.93.04.7100%20160.9 !!43.626.810.88.44.12.4 !2.9100%Low level of crime20063.353.620.78.35.64.12.42.0100%20082.558.721.77.64.42.90.9 !1.3100%20103.257.819.49.74.53.21.31.1100%20162.861.918.78.23.03.80.6 !0.8 !100%Students come from areas with very different levels of crime20061.6 !!37.622.311.99.48.04.05.1100%20080.8 !!33.329.711.76.48.03.9 !6.2100%20103.7 !33.920.416.212.77.02.83.3100%20161.6 !46.022.812.97.1 !3.43.6 !!2.7 !100%Standard Error (BRR)Total20060.511.171.110.770.660.500.450.25 20080.421.140.980.780.510.560.320.28 20100.510.970.850.790.590.500.260.30 20160.511.181.060.880.660.420.340.35 School grades offered - based on CCD frame variables (School)Primary20060.691.891.891.251.040.710.660.31 20080.681.781.491.270.800.910.480.44 20100.821.561.481.230.920.660.410.36 20160.821.981.611.271.050.730.560.47 Middle20060.381.351.411.160.930.920.610.60 20080.261.271.380.940.880.770.550.48 20100.401.601.400.910.810.740.320.35 20160.561.781.811.060.620.600.460.30 High school20060.601.451.270.981.010.800.640.99 20080.381.441.211.080.910.900.490.87 20100.801.471.261.240.850.840.610.73 20160.581.801.531.311.200.910.651.03 Combined20063.154.643.262.321.130.900.931.13 20082.424.334.031.391.781.02†0.93 20101.394.762.983.271.971.650.621.04 20161.174.013.732.141.781.28†0.90 School size categories - based on CCD frame variables (School)Less than 30020062.012.722.640.920.350.34†0.13 20081.522.982.741.190.840.79†0.20 20101.923.011.961.700.680.49†0.16 20161.342.861.941.650.690.280.960.42 300 - 49920060.312.242.531.510.960.700.600.31 20080.962.752.391.481.080.960.520.50 20100.582.061.751.361.360.780.070.32 20160.762.852.351.631.450.970.150.13 500 - 99920060.321.751.751.401.431.111.100.54 20080.481.771.791.361.071.250.620.58 20100.541.741.461.290.831.090.690.53 20160.912.021.951.351.221.020.640.65 1,000 or more20060.161.331.871.391.031.601.501.77 20080.231.831.941.201.181.461.591.56 20100.441.611.631.421.541.371.151.40 20160.391.791.581.551.521.931.071.70 Urbanicity - Based on Urban-centric location of school - from CCD (School)City20060.652.262.191.981.231.471.230.72 20080.902.922.111.911.371.750.900.78 20100.822.282.051.631.221.180.700.61 20160.982.672.261.701.731.301.041.07 Suburb20060.542.001.981.091.090.850.730.61 20080.582.251.971.270.980.810.630.66 20100.592.432.011.441.040.990.460.68 20160.682.451.961.401.260.830.580.47 Town20060.704.453.362.781.970.741.090.47 20080.183.423.742.130.970.840.630.70 20101.053.763.151.891.241.330.270.18 20160.623.923.402.420.480.880.560.80 Rural20061.372.241.780.910.730.440.300.13 20081.162.212.201.110.920.570.420.31 20101.202.091.571.330.980.540.340.30 20161.062.402.081.670.950.710.170.11 Level of crime where students liveHigh level of crime20060.824.613.474.393.292.751.951.32 20082.325.274.063.002.973.921.621.72 20101.934.663.952.492.562.691.351.63 20162.184.674.182.733.641.872.332.12 Moderate level of crime20060.832.402.321.971.591.311.330.88 20080.752.692.222.221.311.881.001.22 20100.732.592.251.841.261.430.700.88 20160.573.412.771.761.661.170.730.76 Low level of crime20060.701.631.380.810.770.460.410.29 20080.631.621.370.850.660.490.290.25 20100.741.440.950.890.600.440.260.20 20160.761.551.331.060.580.620.200.30 Students come from areas with very different levels of crime20061.113.542.881.951.751.431.090.85 20080.763.282.742.351.471.491.240.97 20101.333.442.632.682.091.770.800.80 20160.794.133.792.682.520.971.791.13 Relative Standard Error (%)Total200619.602.605.197.309.368.3712.336.75 200819.392.394.268.039.048.9713.678.71 201018.501.964.147.059.2610.0113.3411.44 201621.682.215.049.4611.6010.5917.5516.09 School grades offered - based on CCD frame variables (School)Primary200627.434.008.0811.9614.8214.3120.0428.52 200826.133.645.9013.2316.6916.4723.5828.97 201024.392.977.3510.5914.9917.0232.4828.91 201630.903.627.5714.1117.5520.2737.7536.38 Middle200641.703.356.628.9410.5311.2917.0714.56 200851.332.805.778.1613.9210.4820.1220.93 201025.723.445.548.2712.0012.9222.9520.01 201625.813.367.5010.0714.2319.8123.9439.26 High school200648.024.497.1610.3512.378.1310.036.65 200850.334.256.889.789.518.3811.667.23 201033.134.216.7711.329.869.0310.377.97 201633.544.408.8010.7516.8411.2414.4912.72 Combined200636.287.6522.0930.2448.5753.2146.7350.68 200843.156.1729.4037.1459.1863.65†40.62 201077.737.3620.4138.1452.2746.4276.4647.23 201673.735.6818.9965.4461.01106.18†111.03 School size categories - based on CCD frame variables (School)Less than 300200623.523.7717.5735.4039.4659.87†100.28 200833.834.0418.2837.4039.0969.72†70.25 201030.323.8720.8435.4881.2365.94†100.30 201640.713.5421.9043.4350.12100.95100.7468.76 300 - 499200665.774.389.5112.3019.0026.6042.0078.47 200844.695.367.9618.7926.9231.7863.9069.71 201030.763.627.4415.3723.4630.60100.27100.16 201644.854.809.9720.4330.4045.3471.0170.08 500 - 999200639.625.617.419.4211.5812.3520.1220.24 200835.474.687.269.1813.6813.9124.3028.91 201030.784.835.517.6510.5616.7728.6828.81 201634.004.987.1110.7115.9621.5526.7435.70 1,000 or more200672.0211.1511.8013.4210.699.5312.667.57 2008100.0711.1612.5210.8411.688.7915.747.80 201044.2710.3011.4211.9210.288.7111.778.43 201641.356.919.9011.8815.4613.2615.0113.39 Urbanicity - Based on Urban-centric location of school - from CCD (School)City200634.147.079.5514.7016.5815.6819.4910.98 200835.947.5611.4916.0320.0514.4424.4713.26 201029.045.499.9013.2014.3617.3624.0313.74 201630.756.2410.4716.9320.2523.0729.9222.77 Suburb200656.104.988.979.9612.8611.0415.4412.23 200858.244.948.1013.3614.4813.0023.4816.41 201043.885.259.3013.0114.4616.0117.3318.16 201636.184.758.7616.2618.3218.6227.4720.32 Town2006100.4710.4413.1219.3221.0923.2247.1225.43 2008100.277.7710.9717.8028.2723.5940.4958.72 201041.428.2210.7617.4922.4228.5030.6543.55 201664.917.4511.6520.6629.8746.4047.42100.58 Rural200625.483.649.7613.6915.5819.6335.9321.45 200829.303.7610.7715.9319.6520.4735.6924.42 201028.933.3810.5712.7623.0121.1535.9432.45 201639.133.5713.8319.8930.1524.9736.3847.91 Level of crime where students liveHigh level of crime200687.8220.7821.1222.6621.6923.2925.3520.66 200844.5016.8722.0029.7433.7326.4237.3424.51 201064.2312.1720.0125.7727.1526.1450.5623.95 201661.7414.4320.4332.9825.5932.7233.6224.76 Moderate level of crime200654.847.459.1815.1222.3616.3523.2012.32 200862.198.739.2115.6517.0315.9522.3121.80 201066.906.229.6714.5518.4420.8623.7418.48 201660.707.8310.3216.3519.6428.3830.3825.71 Low level of crime200621.093.046.679.7113.9511.2916.8614.57 200825.652.756.3111.1115.1016.7931.0418.94 201023.532.494.889.2113.3313.9820.4018.69 201626.882.517.1112.8719.1816.1730.9935.21 Students come from areas with very different levels of crime200668.299.4112.8816.3618.5517.8227.6316.71 2008100.879.859.2220.0522.8718.6831.6215.57 201035.6610.1412.8616.5416.4825.1529.0224.62 201649.418.9916.6520.6935.4729.0350.3242.42 Weighted Sample Sizes (n/1,000s)Total200683.2 200883.0 201082.8 201683.6 School grades offered - based on CCD frame variables (School)Primary200648.6 200849.2 201048.9 201649.1 Middle200615.5 200815.3 201015.3 201615.6 High school200611.7 200811.9 201012.2 201612.8 Combined20067.4 20086.6 20106.4 20166.2 School size categories - based on CCD frame variables (School)Less than 300200620.8 200819.2 201018.9 201618.2 300 - 499200623.8 200824.3 201025.2 201625.0 500 - 999200629.3 200830.2 201029.8 201631.7 1,000 or more20069.3 20089.3 20108.9 20168.7 Urbanicity - Based on Urban-centric location of school - from CCD (School)City200621.0 200821.3 201021.5 201622.8 Suburb200627.6 200823.9 201023.8 201627.4 Town20068.2 200811.8 201012.1 201611.0 Rural200626.4 200826.0 201025.3 201622.5 Level of crime where students liveHigh level of crime20066.5 20086.2 20105.9 20167.4 Moderate level of crime200615.9 200817.1 201018.4 201617.5 Low level of crime200650.3 200849.2 201047.7 201648.4 Students come from areas with very different levels of crime200610.5 200810.5 201010.7 201610.4 Number of students transferred from school by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Urbanicity - Based on Urban-centric location of school - from CCD (School) and Level of crime where students live for years 2006, 2008, 2010 and 2016 Number of students transferred from school01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or moreTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal20062.6[1.74-3.82]45.0[42.70-47.40]21.4[19.28-23.75]10.5[9.10-12.20]7.1[5.87-8.55]5.9[5.02-7.03]3.7[2.85-4.68]3.7[3.23-4.23]100%20082.2[1.48-3.23]47.7[45.43-50.00]22.9[21.04-24.97]9.7[8.24-11.37]5.6[4.70-6.76]6.3[5.23-7.50]2.3[1.75-3.03]3.2[2.72-3.86]100%20102.8[1.90-3.99]49.7[47.77-51.68]20.4[18.76-22.16]11.2[9.68-12.84]6.4[5.32-7.72]5.0[4.09-6.11]2.0[1.49-2.55]2.6[2.05-3.25]100%20162.3[1.51-3.61]53.5[51.08-55.83]21.1[19.02-23.29]9.3[7.71-11.28]5.6[4.47-7.12]4.0[3.24-4.95]1.9[1.35-2.73]2.2[1.59-3.03]100%School grades offered - based on CCD frame variables (School)Primary20062.5[1.44-4.33]47.3[43.51-51.11]23.4[19.80-27.39]10.5[8.22-13.29]7.0[5.19-9.41]4.9[3.70-6.57]3.3[2.18-4.88]1.1[0.62-1.95]100%20082.6[1.54-4.38]48.7[45.19-52.31]25.2[22.33-28.30]9.6[7.34-12.48]4.8[3.44-6.72]5.5[3.94-7.62]2.0[1.25-3.23]1.5[0.85-2.71]100%20103.4[2.05-5.45]52.4[49.31-55.55]20.1[17.31-23.25]11.6[9.35-14.30]6.1[4.51-8.23]3.9[2.75-5.44]1.3 ![0.66-2.44]1.2[0.69-2.20]100%20162.7 ![1.42-4.91]54.7[50.72-58.65]21.2[18.17-24.63]9.0[6.77-11.93]6.0[4.21-8.51]3.6[2.39-5.39]1.5 ![0.70-3.16]1.3 ![0.62-2.67]100%Middle20060.9 ![0.40-2.11]40.1[37.47-42.87]21.3[18.62-24.29]13.0[10.80-15.46]8.8[7.13-10.89]8.2[6.50-10.23]3.6[2.53-5.03]4.1[3.05-5.47]100%20080.5 !![0.18-1.41]45.5[42.92-48.03]23.9[21.22-26.75]11.5[9.73-13.50]6.3[4.77-8.35]7.3[5.91-9.01]2.7[1.82-4.08]2.3[1.51-3.50]100%20101.5[0.91-2.57]46.6[43.39-49.83]25.2[22.52-28.14]11.0[9.30-12.96]6.7[5.27-8.54]5.7[4.42-7.42]1.4[0.89-2.24]1.8[1.18-2.63]100%20162.2[1.29-3.63]53.0[49.46-56.60]24.2[20.73-28.01]10.5[8.54-12.80]4.4[3.29-5.82]3.0[2.04-4.51]1.9[1.19-3.10]0.8 ![0.35-1.69]100%High school20061.2 ![0.47-3.25]32.2[29.41-35.22]17.7[15.31-20.42]9.4[7.64-11.58]8.2[6.35-10.43]9.9[8.39-11.63]6.4[5.25-7.85]14.9[12.99-16.97]100%20080.8 !![0.28-2.09]33.9[31.09-36.87]17.6[15.29-20.15]11.1[9.06-13.42]9.6[7.93-11.62]10.7[9.06-12.69]4.2[3.34-5.33]12.1[10.44-13.96]100%20102.4 ![1.23-4.66]35.0[32.11-38.03]18.6[16.16-21.21]11.0[8.70-13.70]8.6[7.05-10.48]9.3[7.77-11.17]5.9[4.80-7.28]9.2[7.83-10.79]100%20161.7 ![0.87-3.35]40.9[37.33-44.54]17.4[14.56-20.73]12.1[9.75-15.02]7.1[5.07-9.95]8.1[6.45-10.12]4.5[3.37-6.03]8.1[6.25-10.41]100%Combined20068.7 ![4.11-17.44]60.7[51.06-69.50]14.8[9.32-22.55]7.7 ![4.13-13.84]2.3 ![0.87-6.07]1.7 !![0.57-4.84]2.0 ![0.78-5.04]2.2 !![0.80-6.05]100%20085.6 ![2.31-12.94]70.1[60.74-78.00]13.7[7.41-23.94]3.7 ![1.76-7.77]3.0 !![0.90-9.53]1.6 !![0.44-5.64]##2.3 ![1.00-5.11]100%20101.8 !![0.37-8.18]64.7[54.66-73.58]14.6[9.57-21.65]8.6 ![3.90-17.84]3.8 !![1.30-10.43]3.6 ![1.38-8.85]0.8 !![0.17-3.69]2.2 ![0.85-5.60]100%20161.6 !![0.36-6.75]70.6[61.96-77.96]19.6[13.18-28.18]3.3 !![0.86-11.62]2.9 !![0.84-9.62]1.2 !![0.14-9.59]##0.8 !![0.09-7.19]100%School size categories - based on CCD frame variables (School)Less than 30020068.6[5.29-13.56]72.2[66.45-77.35]15.0[10.46-21.15]2.6 ![1.27-5.24]0.9 ![0.40-1.94]0.6 !![0.17-1.88]##0.1 !![0.02-0.96]100%20084.5 ![2.25-8.73]73.8[67.37-79.33]15.0[10.27-21.35]3.2 ![1.49-6.68]2.1 ![0.97-4.64]1.1 !![0.28-4.50]##0.3 !![0.07-1.18]100%20106.3 ![3.40-11.45]77.7[71.10-83.17]9.4[6.15-14.16]4.8 ![2.33-9.63]0.8 !![0.16-4.19]0.7 !![0.20-2.79]##0.2 !![0.02-1.20]100%20163.3 ![1.44-7.34]80.8[74.43-85.93]8.9[5.67-13.62]3.8 ![1.57-8.89]1.4 !![0.50-3.74]0.3 !![0.04-2.09]1.0 !![0.12-6.91]0.6 !![0.15-2.42]100%300 - 49920060.5 !![0.13-1.76]51.2[46.72-55.72]26.5[21.79-31.91]12.2[9.52-15.60]5.0[3.43-7.36]2.6[1.54-4.49]1.4 ![0.61-3.31]0.4 !![0.08-1.87]100%20082.2 ![0.87-5.23]51.4[45.90-56.92]30.0[25.41-34.98]7.9[5.38-11.44]4.0[2.32-6.83]3.0 ![1.58-5.66]0.8 !![0.22-2.91]0.7 !![0.18-2.86]100%20101.9 ![1.01-3.48]57.0[52.82-61.09]23.5[20.19-27.22]8.8[6.47-11.99]5.8[3.59-9.20]2.6 ![1.38-4.71]0.1 !![0.01-0.54]0.3 !![0.04-2.33]100%20161.7 ![0.68-4.13]59.4[53.60-65.02]23.6[19.18-28.62]8.0[5.27-11.95]4.8 ![2.57-8.68]2.1 ![0.85-5.24]0.2 !![0.05-0.85]0.2 !![0.05-0.78]100%500 - 99920060.8 ![0.36-1.77]31.2[27.78-34.80]23.6[20.28-27.31]14.9[12.29-17.94]12.4[9.76-15.53]9.0[7.02-11.53]5.5[3.64-8.16]2.6[1.76-3.97]100%20081.4 ![0.67-2.78]37.8[34.30-41.39]24.6[21.22-28.41]14.8[12.31-17.79]7.8[5.92-10.25]9.0[6.78-11.84]2.6[1.57-4.17]2.0[1.11-3.55]100%20101.8 ![0.95-3.26]36.1[32.68-39.68]26.6[23.73-29.61]16.9[14.46-19.67]7.9[6.37-9.73]6.5[4.64-9.09]2.4[1.35-4.28]1.8[1.02-3.25]100%20162.7 ![1.35-5.28]40.6[36.65-44.77]27.5[23.74-31.58]12.6[10.12-15.56]7.6[5.52-10.48]4.7[3.05-7.25]2.4[1.40-4.09]1.8 ![0.88-3.70]100%1,000 or more20060.2 !![0.05-0.97]11.9[9.49-14.84]15.8[12.42-19.95]10.3[7.87-13.49]9.7[7.78-11.95]16.8[13.79-20.22]11.8[9.14-15.19]23.4[20.05-27.18]100%20080.2 !![0.03-1.67]16.4[13.02-20.38]15.5[11.98-19.81]11.1[8.90-13.76]10.1[7.96-12.73]16.6[13.88-19.74]10.1[7.32-13.77]20.0[17.06-23.33]100%20101.0 ![0.41-2.42]15.6[12.62-19.09]14.3[11.29-17.85]11.9[9.36-15.11]15.0[12.18-18.40]15.8[13.21-18.75]9.8[7.72-12.39]16.6[13.98-19.61]100%20161.0 ![0.41-2.18]25.9[22.43-29.61]16.0[13.03-19.39]13.0[10.20-16.44]9.9[7.19-13.37]14.6[11.09-18.87]7.2[5.28-9.64]12.7[9.63-16.48]100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City20061.9 ![0.96-3.78]31.9[27.57-36.62]22.9[18.84-27.64]13.5[9.95-17.95]7.4[5.31-10.32]9.4[6.83-12.81]6.3[4.26-9.31]6.6[5.28-8.20]100%20082.5 ![1.21-5.09]38.7[33.00-44.70]18.4[14.53-23.03]11.9[8.59-16.34]6.8[4.53-10.13]12.1[9.01-16.09]3.7[2.24-5.98]5.9[4.50-7.66]100%20102.8[1.57-5.04]41.4[36.94-46.06]20.7[16.87-25.11]12.3[9.42-15.99]8.5[6.37-11.33]6.8[4.79-9.60]2.9[1.80-4.71]4.5[3.39-5.88]100%20163.2 ![1.72-5.89]42.8[37.50-48.18]21.6[17.41-26.50]10.1[7.12-14.03]8.6[5.66-12.75]5.6[3.53-8.91]3.5[1.90-6.31]4.7[2.95-7.36]100%Suburb20061.0 !![0.31-2.96]40.1[36.17-44.17]22.1[18.35-26.30]11.0[8.95-13.35]8.5[6.52-10.92]7.7[6.15-9.58]4.7[3.46-6.43]5.0[3.92-6.40]100%20081.0 !![0.31-3.19]45.5[41.08-50.09]24.3[20.53-28.43]9.5[7.26-12.41]6.7[5.03-8.99]6.2[4.76-8.03]2.7[1.67-4.28]4.0[2.90-5.60]100%20101.3 ![0.56-3.23]46.2[41.40-51.12]21.6[17.82-25.88]11.1[8.48-14.29]7.2[5.38-9.62]6.2[4.46-8.47]2.7[1.89-3.78]3.7[2.59-5.37]100%20161.9 ![0.91-3.87]51.4[46.52-56.31]22.3[18.64-26.50]8.6[6.18-11.86]6.9[4.75-9.91]4.5[3.06-6.45]2.1[1.21-3.65]2.3[1.53-3.46]100%Town20060.7 !![0.09-5.11]42.6[33.99-51.67]25.6[19.48-32.95]14.4[9.66-20.93]9.4[6.07-14.13]3.2[1.98-5.03]2.3 ![0.89-5.89]1.8[1.10-3.04]100%20080.2 !![0.02-1.37]44.1[37.35-51.02]34.0[26.98-41.90]12.0[8.31-16.95]3.4[1.94-6.02]3.6[2.20-5.67]1.5 ![0.68-3.46]1.2 !![0.37-3.85]100%20102.5 ![1.09-5.75]45.8[38.40-53.42]29.3[23.38-36.01]10.8[7.58-15.27]5.5[3.52-8.64]4.7[2.61-8.17]0.9 ![0.48-1.65]0.4 ![0.18-1.01]100%20161.0 !![0.26-3.49]52.6[44.77-60.40]29.2[22.87-36.48]11.7[7.65-17.49]1.6[0.88-2.92]1.9 ![0.74-4.74]1.2 ![0.46-3.06]0.8 !![0.11-5.82]100%Rural20065.4[3.21-8.92]61.4[56.79-65.75]18.3[14.95-22.12]6.6[5.01-8.68]4.7[3.42-6.40]2.2[1.51-3.32]0.8 ![0.41-1.72]0.6[0.39-0.92]100%20083.9[2.18-7.04]58.7[54.21-63.07]20.4[16.36-25.20]7.0[5.05-9.57]4.7[3.14-6.90]2.8[1.85-4.22]1.2 ![0.58-2.41]1.3[0.78-2.09]100%20104.1[2.30-7.33]61.9[57.64-66.02]14.8[11.95-18.26]10.4[8.04-13.41]4.3[2.68-6.75]2.5[1.66-3.87]1.0 ![0.46-1.96]0.9 ![0.48-1.78]100%20162.7 ![1.22-5.87]67.2[62.19-71.79]15.1[11.33-19.73]8.4[5.59-12.40]3.2 ![1.71-5.73]2.8[1.71-4.66]0.5 ![0.23-0.98]0.2 ![0.09-0.59]100%Level of crime where students liveHigh level of crime20060.9 !![0.16-5.32]22.2[14.30-32.79]16.4[10.59-24.64]19.4[12.01-29.69]15.2[9.68-23.04]11.8[7.29-18.52]7.7[4.58-12.65]6.4[4.19-9.60]100%20085.2 ![2.09-12.36]31.3[21.75-42.69]18.4[11.62-28.00]10.1[5.46-17.91]8.8 ![4.39-16.87]14.8[8.55-24.54]4.3 ![2.03-9.03]7.0[4.25-11.35]100%20103.0 !![0.81-10.48]38.3[29.49-48.04]19.8[12.99-28.91]9.7[5.69-15.96]9.4[5.40-16.00]10.3[5.99-17.05]2.7 !![0.96-7.23]6.8[4.19-10.94]100%20163.5 !![1.00-11.67]32.3[23.75-42.33]20.4[13.29-30.08]8.3 ![4.19-15.65]14.2[8.34-23.19]5.7 ![2.93-10.86]6.9 ![3.47-13.32]8.6[5.16-13.90]100%Moderate level of crime20061.5 !![0.50-4.47]32.2[27.56-37.16]25.3[20.94-30.26]13.1[9.58-17.56]7.1[4.50-11.02]8.0[5.75-11.08]5.7[3.57-9.05]7.1[5.54-9.08]100%20081.2 !![0.35-4.16]30.8[25.71-36.50]24.2[19.97-28.90]14.2[10.29-19.26]7.7[5.44-10.78]11.8[8.50-16.11]4.5[2.86-7.00]5.6[3.61-8.65]100%20101.1 !![0.28-4.11]41.6[36.50-46.86]23.3[19.08-28.12]12.6[9.37-16.79]6.9[4.71-9.87]6.9[4.48-10.34]3.0[1.83-4.74]4.7[3.26-6.85]100%20160.9 !![0.28-3.17]43.6[36.88-50.50]26.8[21.64-32.74]10.8[7.70-14.84]8.4[5.66-12.43]4.1[2.32-7.23]2.4 ![1.29-4.37]2.9[1.75-4.91]100%Low level of crime20063.3[2.18-5.09]53.6[50.34-56.88]20.7[18.03-23.57]8.3[6.83-10.10]5.6[4.19-7.33]4.1[3.26-5.13]2.4[1.72-3.39]2.0[1.48-2.65]100%20082.5[1.46-4.10]58.7[55.45-61.94]21.7[19.04-24.52]7.6[6.10-9.53]4.4[3.21-5.89]2.9[2.07-4.07]0.9 ![0.50-1.74]1.3[0.89-1.91]100%20103.2[1.96-5.04]57.8[54.88-60.66]19.4[17.53-21.33]9.7[8.01-11.60]4.5[3.42-5.84]3.2[2.40-4.20]1.3[0.86-1.95]1.1[0.74-1.56]100%20162.8[1.64-4.82]61.9[58.71-64.94]18.7[16.20-21.56]8.2[6.34-10.63]3.0[2.04-4.42]3.8[2.77-5.30]0.6 ![0.34-1.19]0.8 ![0.42-1.72]100%Students come from areas with very different levels of crime20061.6 !![0.41-6.24]37.6[30.81-44.95]22.3[17.09-28.64]11.9[8.54-16.45]9.4[6.45-13.57]8.0[5.60-11.44]4.0[2.26-6.84]5.1[3.61-7.05]100%20080.8 !![0.10-5.55]33.3[27.09-40.22]29.7[24.49-35.46]11.7[7.76-17.32]6.4[4.03-10.07]8.0[5.44-11.52]3.9 ![2.07-7.35]6.2[4.52-8.45]100%20103.7 ![1.81-7.55]33.9[27.40-41.16]20.4[15.65-26.21]16.2[11.50-22.32]12.7[9.02-17.47]7.0[4.21-11.53]2.8[1.53-4.90]3.3[1.98-5.31]100%20161.6 ![0.59-4.27]46.0[37.85-54.31]22.8[16.05-31.27]12.9[8.45-19.34]7.1 ![3.44-14.17]3.4[1.86-5.96]3.6 !![1.28-9.54]2.7 ![1.13-6.17]100%2006200820102016 Number of students transferred from schoolNumber of students transferred from schoolNumber of students transferred from schoolNumber of students transferred from school 01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or more01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or more01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or more01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or moreEstimatesTotal2.645.021.410.57.15.93.73.72.247.722.99.75.66.32.33.22.849.720.411.26.45.02.02.62.353.521.19.35.64.01.92.2School grades offered - based on CCD frame variables (School)Primary2.547.323.410.57.04.93.31.12.648.725.29.64.85.52.01.53.452.420.111.66.13.91.31.22.754.721.29.06.03.61.51.3Middle0.940.121.313.08.88.23.64.10.545.523.911.56.37.32.72.31.546.625.211.06.75.71.41.82.253.024.210.54.43.01.90.8High school1.232.217.79.48.29.96.414.90.833.917.611.19.610.74.212.12.435.018.611.08.69.35.99.21.740.917.412.17.18.14.58.1Combined8.760.714.87.72.31.72.02.25.670.113.73.73.01.6#2.31.864.714.68.63.83.60.82.21.670.619.63.32.91.2#0.8School size categories - based on CCD frame variables (School)Less than 3008.672.215.02.60.90.6#0.14.573.815.03.22.11.1#0.36.377.79.44.80.80.7#0.23.380.88.93.81.40.31.00.6300 - 4990.551.226.512.25.02.61.40.42.251.430.07.94.03.00.80.71.957.023.58.85.82.60.10.31.759.423.68.04.82.10.20.2500 - 9990.831.223.614.912.49.05.52.61.437.824.614.87.89.02.62.01.836.126.616.97.96.52.41.82.740.627.512.67.64.72.41.81,000 or more0.211.915.810.39.716.811.823.40.216.415.511.110.116.610.120.01.015.614.311.915.015.89.816.61.025.916.013.09.914.67.212.7Urbanicity - Based on Urban-centric location of school - from CCD (School)City1.931.922.913.57.49.46.36.62.538.718.411.96.812.13.75.92.841.420.712.38.56.82.94.53.242.821.610.18.65.63.54.7Suburb1.040.122.111.08.57.74.75.01.045.524.39.56.76.22.74.01.346.221.611.17.26.22.73.71.951.422.38.66.94.52.12.3Town0.742.625.614.49.43.22.31.80.244.134.012.03.43.61.51.22.545.829.310.85.54.70.90.41.052.629.211.71.61.91.20.8Rural5.461.418.36.64.72.20.80.63.958.720.47.04.72.81.21.34.161.914.810.44.32.51.00.92.767.215.18.43.22.80.50.2Level of crime where students liveHigh level of crime0.922.216.419.415.211.87.76.45.231.318.410.18.814.84.37.03.038.319.89.79.410.32.76.83.532.320.48.314.25.76.98.6Moderate level of crime1.532.225.313.17.18.05.77.11.230.824.214.27.711.84.55.61.141.623.312.66.96.93.04.70.943.626.810.88.44.12.42.9Low level of crime3.353.620.78.35.64.12.42.02.558.721.77.64.42.90.91.33.257.819.49.74.53.21.31.12.861.918.78.23.03.80.60.8Students come from areas with very different levels of crime1.637.622.311.99.48.04.05.10.833.329.711.76.48.03.96.23.733.920.416.212.77.02.83.31.646.022.812.97.13.43.62.72006200820102016 Number of students transferred from schoolNumber of students transferred from schoolNumber of students transferred from schoolNumber of students transferred from school 01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or more01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or more01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or more01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or moreEstimatesTotal2.645.021.410.57.15.93.73.72.247.722.99.75.66.32.33.22.849.720.411.26.45.02.02.62.353.521.19.35.64.01.92.2School grades offered - based on CCD frame variables (School)Primary2.547.323.410.57.04.93.31.12.648.725.29.64.85.52.01.53.452.420.111.66.13.91.31.22.754.721.29.06.03.61.51.3Middle0.940.121.313.08.88.23.64.10.545.523.911.56.37.32.72.31.546.625.211.06.75.71.41.82.253.024.210.54.43.01.90.8High school1.232.217.79.48.29.96.414.90.833.917.611.19.610.74.212.12.435.018.611.08.69.35.99.21.740.917.412.17.18.14.58.1Combined8.760.714.87.72.31.72.02.25.670.113.73.73.01.6#2.31.864.714.68.63.83.60.82.21.670.619.63.32.91.2#0.8School size categories - based on CCD frame variables (School)Less than 3008.672.215.02.60.90.6#0.14.573.815.03.22.11.1#0.36.377.79.44.80.80.7#0.23.380.88.93.81.40.31.00.6300 - 4990.551.226.512.25.02.61.40.42.251.430.07.94.03.00.80.71.957.023.58.85.82.60.10.31.759.423.68.04.82.10.20.2500 - 9990.831.223.614.912.49.05.52.61.437.824.614.87.89.02.62.01.836.126.616.97.96.52.41.82.740.627.512.67.64.72.41.81,000 or more0.211.915.810.39.716.811.823.40.216.415.511.110.116.610.120.01.015.614.311.915.015.89.816.61.025.916.013.09.914.67.212.7Urbanicity - Based on Urban-centric location of school - from CCD (School)City1.931.922.913.57.49.46.36.62.538.718.411.96.812.13.75.92.841.420.712.38.56.82.94.53.242.821.610.18.65.63.54.7Suburb1.040.122.111.08.57.74.75.01.045.524.39.56.76.22.74.01.346.221.611.17.26.22.73.71.951.422.38.66.94.52.12.3Town0.742.625.614.49.43.22.31.80.244.134.012.03.43.61.51.22.545.829.310.85.54.70.90.41.052.629.211.71.61.91.20.8Rural5.461.418.36.64.72.20.80.63.958.720.47.04.72.81.21.34.161.914.810.44.32.51.00.92.767.215.18.43.22.80.50.2Level of crime where students liveHigh level of crime0.922.216.419.415.211.87.76.45.231.318.410.18.814.84.37.03.038.319.89.79.410.32.76.83.532.320.48.314.25.76.98.6Moderate level of crime1.532.225.313.17.18.05.77.11.230.824.214.27.711.84.55.61.141.623.312.66.96.93.04.70.943.626.810.88.44.12.42.9Low level of crime3.353.620.78.35.64.12.42.02.558.721.77.64.42.90.91.33.257.819.49.74.53.21.31.12.861.918.78.23.03.80.60.8Students come from areas with very different levels of crime1.637.622.311.99.48.04.05.10.833.329.711.76.48.03.96.23.733.920.416.212.77.02.83.31.646.022.812.97.13.43.62.7Standard Error (BRR)Total0.511.171.110.770.660.500.450.250.421.140.980.780.510.560.320.280.510.970.850.790.590.500.260.300.511.181.060.880.660.420.340.35School grades offered - based on CCD frame variables (School)Primary0.691.891.891.251.040.710.660.310.681.781.491.270.800.910.480.440.821.561.481.230.920.660.410.360.821.981.611.271.050.730.560.47Middle0.381.351.411.160.930.920.610.600.261.271.380.940.880.770.550.480.401.601.400.910.810.740.320.350.561.781.811.060.620.600.460.30High school0.601.451.270.981.010.800.640.990.381.441.211.080.910.900.490.870.801.471.261.240.850.840.610.730.581.801.531.311.200.910.651.03Combined3.154.643.262.321.130.900.931.132.424.334.031.391.781.02†0.931.394.762.983.271.971.650.621.041.174.013.732.141.781.28†0.90School size categories - based on CCD frame variables (School)Less than 3002.012.722.640.920.350.34†0.131.522.982.741.190.840.79†0.201.923.011.961.700.680.49†0.161.342.861.941.650.690.280.960.42300 - 4990.312.242.531.510.960.700.600.310.962.752.391.481.080.960.520.500.582.061.751.361.360.780.070.320.762.852.351.631.450.970.150.13500 - 9990.321.751.751.401.431.111.100.540.481.771.791.361.071.250.620.580.541.741.461.290.831.090.690.530.912.021.951.351.221.020.640.651,000 or more0.161.331.871.391.031.601.501.770.231.831.941.201.181.461.591.560.441.611.631.421.541.371.151.400.391.791.581.551.521.931.071.70Urbanicity - Based on Urban-centric location of school - from CCD (School)City0.652.262.191.981.231.471.230.720.902.922.111.911.371.750.900.780.822.282.051.631.221.180.700.610.982.672.261.701.731.301.041.07Suburb0.542.001.981.091.090.850.730.610.582.251.971.270.980.810.630.660.592.432.011.441.040.990.460.680.682.451.961.401.260.830.580.47Town0.704.453.362.781.970.741.090.470.183.423.742.130.970.840.630.701.053.763.151.891.241.330.270.180.623.923.402.420.480.880.560.80Rural1.372.241.780.910.730.440.300.131.162.212.201.110.920.570.420.311.202.091.571.330.980.540.340.301.062.402.081.670.950.710.170.11Level of crime where students liveHigh level of crime0.824.613.474.393.292.751.951.322.325.274.063.002.973.921.621.721.934.663.952.492.562.691.351.632.184.674.182.733.641.872.332.12Moderate level of crime0.832.402.321.971.591.311.330.880.752.692.222.221.311.881.001.220.732.592.251.841.261.430.700.880.573.412.771.761.661.170.730.76Low level of crime0.701.631.380.810.770.460.410.290.631.621.370.850.660.490.290.250.741.440.950.890.600.440.260.200.761.551.331.060.580.620.200.30Students come from areas with very different levels of crime1.113.542.881.951.751.431.090.850.763.282.742.351.471.491.240.971.333.442.632.682.091.770.800.800.794.133.792.682.520.971.791.13Relative Standard Error (%)Total19.602.605.197.309.368.3712.336.7519.392.394.268.039.048.9713.678.7118.501.964.147.059.2610.0113.3411.4421.682.215.049.4611.6010.5917.5516.09School grades offered - based on CCD frame variables (School)Primary27.434.008.0811.9614.8214.3120.0428.5226.133.645.9013.2316.6916.4723.5828.9724.392.977.3510.5914.9917.0232.4828.9130.903.627.5714.1117.5520.2737.7536.38Middle41.703.356.628.9410.5311.2917.0714.5651.332.805.778.1613.9210.4820.1220.9325.723.445.548.2712.0012.9222.9520.0125.813.367.5010.0714.2319.8123.9439.26High school48.024.497.1610.3512.378.1310.036.6550.334.256.889.789.518.3811.667.2333.134.216.7711.329.869.0310.377.9733.544.408.8010.7516.8411.2414.4912.72Combined36.287.6522.0930.2448.5753.2146.7350.6843.156.1729.4037.1459.1863.65†40.6277.737.3620.4138.1452.2746.4276.4647.2373.735.6818.9965.4461.01106.18†111.03School size categories - based on CCD frame variables (School)Less than 30023.523.7717.5735.4039.4659.87†100.2833.834.0418.2837.4039.0969.72†70.2530.323.8720.8435.4881.2365.94†100.3040.713.5421.9043.4350.12100.95100.7468.76300 - 49965.774.389.5112.3019.0026.6042.0078.4744.695.367.9618.7926.9231.7863.9069.7130.763.627.4415.3723.4630.60100.27100.1644.854.809.9720.4330.4045.3471.0170.08500 - 99939.625.617.419.4211.5812.3520.1220.2435.474.687.269.1813.6813.9124.3028.9130.784.835.517.6510.5616.7728.6828.8134.004.987.1110.7115.9621.5526.7435.701,000 or more72.0211.1511.8013.4210.699.5312.667.57100.0711.1612.5210.8411.688.7915.747.8044.2710.3011.4211.9210.288.7111.778.4341.356.919.9011.8815.4613.2615.0113.39Urbanicity - Based on Urban-centric location of school - from CCD (School)City34.147.079.5514.7016.5815.6819.4910.9835.947.5611.4916.0320.0514.4424.4713.2629.045.499.9013.2014.3617.3624.0313.7430.756.2410.4716.9320.2523.0729.9222.77Suburb56.104.988.979.9612.8611.0415.4412.2358.244.948.1013.3614.4813.0023.4816.4143.885.259.3013.0114.4616.0117.3318.1636.184.758.7616.2618.3218.6227.4720.32Town100.4710.4413.1219.3221.0923.2247.1225.43100.277.7710.9717.8028.2723.5940.4958.7241.428.2210.7617.4922.4228.5030.6543.5564.917.4511.6520.6629.8746.4047.42100.58Rural25.483.649.7613.6915.5819.6335.9321.4529.303.7610.7715.9319.6520.4735.6924.4228.933.3810.5712.7623.0121.1535.9432.4539.133.5713.8319.8930.1524.9736.3847.91Level of crime where students liveHigh level of crime87.8220.7821.1222.6621.6923.2925.3520.6644.5016.8722.0029.7433.7326.4237.3424.5164.2312.1720.0125.7727.1526.1450.5623.9561.7414.4320.4332.9825.5932.7233.6224.76Moderate level of crime54.847.459.1815.1222.3616.3523.2012.3262.198.739.2115.6517.0315.9522.3121.8066.906.229.6714.5518.4420.8623.7418.4860.707.8310.3216.3519.6428.3830.3825.71Low level of crime21.093.046.679.7113.9511.2916.8614.5725.652.756.3111.1115.1016.7931.0418.9423.532.494.889.2113.3313.9820.4018.6926.882.517.1112.8719.1816.1730.9935.21Students come from areas with very different levels of crime68.299.4112.8816.3618.5517.8227.6316.71100.879.859.2220.0522.8718.6831.6215.5735.6610.1412.8616.5416.4825.1529.0224.6249.418.9916.6520.6935.4729.0350.3242.42Weighted Sample Sizes (n/1,000s)Total83.2 83.0 82.8 83.6 School grades offered - based on CCD frame variables (School)Primary48.6 49.2 48.9 49.1 Middle15.5 15.3 15.3 15.6 High school11.7 11.9 12.2 12.8 Combined7.4 6.6 6.4 6.2 School size categories - based on CCD frame variables (School)Less than 30020.8 19.2 18.9 18.2 300 - 49923.8 24.3 25.2 25.0 500 - 99929.3 30.2 29.8 31.7 1,000 or more9.3 9.3 8.9 8.7 Urbanicity - Based on Urban-centric location of school - from CCD (School)City21.0 21.3 21.5 22.8 Suburb27.6 23.9 23.8 27.4 Town8.2 11.8 12.1 11.0 Rural26.4 26.0 25.3 22.5 Level of crime where students liveHigh level of crime6.5 6.2 5.9 7.4 Moderate level of crime15.9 17.1 18.4 17.5 Low level of crime50.3 49.2 47.7 48.4 Students come from areas with very different levels of crime10.5 10.5 10.7 10.4 2006200820102016 Number of students transferred from schoolNumber of students transferred from schoolNumber of students transferred from schoolNumber of students transferred from school 01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or more01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or more01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or more01 to 2526 to 5051 to 7576 to 100101 to 150151 to 200201 or more Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal2.6[1.74-3.82]45.0[42.70-47.40]21.4[19.28-23.75]10.5[9.10-12.20]7.1[5.87-8.55]5.9[5.02-7.03]3.7[2.85-4.68]3.7[3.23-4.23]2.2[1.48-3.23]47.7[45.43-50.00]22.9[21.04-24.97]9.7[8.24-11.37]5.6[4.70-6.76]6.3[5.23-7.50]2.3[1.75-3.03]3.2[2.72-3.86]2.8[1.90-3.99]49.7[47.77-51.68]20.4[18.76-22.16]11.2[9.68-12.84]6.4[5.32-7.72]5.0[4.09-6.11]2.0[1.49-2.55]2.6[2.05-3.25]2.3[1.51-3.61]53.5[51.08-55.83]21.1[19.02-23.29]9.3[7.71-11.28]5.6[4.47-7.12]4.0[3.24-4.95]1.9[1.35-2.73]2.2[1.59-3.03]School grades offered - based on CCD frame variables (School)Primary2.5[1.44-4.33]47.3[43.51-51.11]23.4[19.80-27.39]10.5[8.22-13.29]7.0[5.19-9.41]4.9[3.70-6.57]3.3[2.18-4.88]1.1[0.62-1.95]2.6[1.54-4.38]48.7[45.19-52.31]25.2[22.33-28.30]9.6[7.34-12.48]4.8[3.44-6.72]5.5[3.94-7.62]2.0[1.25-3.23]1.5[0.85-2.71]3.4[2.05-5.45]52.4[49.31-55.55]20.1[17.31-23.25]11.6[9.35-14.30]6.1[4.51-8.23]3.9[2.75-5.44]1.3 ![0.66-2.44]1.2[0.69-2.20]2.7 ![1.42-4.91]54.7[50.72-58.65]21.2[18.17-24.63]9.0[6.77-11.93]6.0[4.21-8.51]3.6[2.39-5.39]1.5 ![0.70-3.16]1.3 ![0.62-2.67]Middle0.9 ![0.40-2.11]40.1[37.47-42.87]21.3[18.62-24.29]13.0[10.80-15.46]8.8[7.13-10.89]8.2[6.50-10.23]3.6[2.53-5.03]4.1[3.05-5.47]0.5 !![0.18-1.41]45.5[42.92-48.03]23.9[21.22-26.75]11.5[9.73-13.50]6.3[4.77-8.35]7.3[5.91-9.01]2.7[1.82-4.08]2.3[1.51-3.50]1.5[0.91-2.57]46.6[43.39-49.83]25.2[22.52-28.14]11.0[9.30-12.96]6.7[5.27-8.54]5.7[4.42-7.42]1.4[0.89-2.24]1.8[1.18-2.63]2.2[1.29-3.63]53.0[49.46-56.60]24.2[20.73-28.01]10.5[8.54-12.80]4.4[3.29-5.82]3.0[2.04-4.51]1.9[1.19-3.10]0.8 ![0.35-1.69]High school1.2 ![0.47-3.25]32.2[29.41-35.22]17.7[15.31-20.42]9.4[7.64-11.58]8.2[6.35-10.43]9.9[8.39-11.63]6.4[5.25-7.85]14.9[12.99-16.97]0.8 !![0.28-2.09]33.9[31.09-36.87]17.6[15.29-20.15]11.1[9.06-13.42]9.6[7.93-11.62]10.7[9.06-12.69]4.2[3.34-5.33]12.1[10.44-13.96]2.4 ![1.23-4.66]35.0[32.11-38.03]18.6[16.16-21.21]11.0[8.70-13.70]8.6[7.05-10.48]9.3[7.77-11.17]5.9[4.80-7.28]9.2[7.83-10.79]1.7 ![0.87-3.35]40.9[37.33-44.54]17.4[14.56-20.73]12.1[9.75-15.02]7.1[5.07-9.95]8.1[6.45-10.12]4.5[3.37-6.03]8.1[6.25-10.41]Combined8.7 ![4.11-17.44]60.7[51.06-69.50]14.8[9.32-22.55]7.7 ![4.13-13.84]2.3 ![0.87-6.07]1.7 !![0.57-4.84]2.0 ![0.78-5.04]2.2 !![0.80-6.05]5.6 ![2.31-12.94]70.1[60.74-78.00]13.7[7.41-23.94]3.7 ![1.76-7.77]3.0 !![0.90-9.53]1.6 !![0.44-5.64]##2.3 ![1.00-5.11]1.8 !![0.37-8.18]64.7[54.66-73.58]14.6[9.57-21.65]8.6 ![3.90-17.84]3.8 !![1.30-10.43]3.6 ![1.38-8.85]0.8 !![0.17-3.69]2.2 ![0.85-5.60]1.6 !![0.36-6.75]70.6[61.96-77.96]19.6[13.18-28.18]3.3 !![0.86-11.62]2.9 !![0.84-9.62]1.2 !![0.14-9.59]##0.8 !![0.09-7.19]School size categories - based on CCD frame variables (School)Less than 3008.6[5.29-13.56]72.2[66.45-77.35]15.0[10.46-21.15]2.6 ![1.27-5.24]0.9 ![0.40-1.94]0.6 !![0.17-1.88]##0.1 !![0.02-0.96]4.5 ![2.25-8.73]73.8[67.37-79.33]15.0[10.27-21.35]3.2 ![1.49-6.68]2.1 ![0.97-4.64]1.1 !![0.28-4.50]##0.3 !![0.07-1.18]6.3 ![3.40-11.45]77.7[71.10-83.17]9.4[6.15-14.16]4.8 ![2.33-9.63]0.8 !![0.16-4.19]0.7 !![0.20-2.79]##0.2 !![0.02-1.20]3.3 ![1.44-7.34]80.8[74.43-85.93]8.9[5.67-13.62]3.8 ![1.57-8.89]1.4 !![0.50-3.74]0.3 !![0.04-2.09]1.0 !![0.12-6.91]0.6 !![0.15-2.42]300 - 4990.5 !![0.13-1.76]51.2[46.72-55.72]26.5[21.79-31.91]12.2[9.52-15.60]5.0[3.43-7.36]2.6[1.54-4.49]1.4 ![0.61-3.31]0.4 !![0.08-1.87]2.2 ![0.87-5.23]51.4[45.90-56.92]30.0[25.41-34.98]7.9[5.38-11.44]4.0[2.32-6.83]3.0 ![1.58-5.66]0.8 !![0.22-2.91]0.7 !![0.18-2.86]1.9 ![1.01-3.48]57.0[52.82-61.09]23.5[20.19-27.22]8.8[6.47-11.99]5.8[3.59-9.20]2.6 ![1.38-4.71]0.1 !![0.01-0.54]0.3 !![0.04-2.33]1.7 ![0.68-4.13]59.4[53.60-65.02]23.6[19.18-28.62]8.0[5.27-11.95]4.8 ![2.57-8.68]2.1 ![0.85-5.24]0.2 !![0.05-0.85]0.2 !![0.05-0.78]500 - 9990.8 ![0.36-1.77]31.2[27.78-34.80]23.6[20.28-27.31]14.9[12.29-17.94]12.4[9.76-15.53]9.0[7.02-11.53]5.5[3.64-8.16]2.6[1.76-3.97]1.4 ![0.67-2.78]37.8[34.30-41.39]24.6[21.22-28.41]14.8[12.31-17.79]7.8[5.92-10.25]9.0[6.78-11.84]2.6[1.57-4.17]2.0[1.11-3.55]1.8 ![0.95-3.26]36.1[32.68-39.68]26.6[23.73-29.61]16.9[14.46-19.67]7.9[6.37-9.73]6.5[4.64-9.09]2.4[1.35-4.28]1.8[1.02-3.25]2.7 ![1.35-5.28]40.6[36.65-44.77]27.5[23.74-31.58]12.6[10.12-15.56]7.6[5.52-10.48]4.7[3.05-7.25]2.4[1.40-4.09]1.8 ![0.88-3.70]1,000 or more0.2 !![0.05-0.97]11.9[9.49-14.84]15.8[12.42-19.95]10.3[7.87-13.49]9.7[7.78-11.95]16.8[13.79-20.22]11.8[9.14-15.19]23.4[20.05-27.18]0.2 !![0.03-1.67]16.4[13.02-20.38]15.5[11.98-19.81]11.1[8.90-13.76]10.1[7.96-12.73]16.6[13.88-19.74]10.1[7.32-13.77]20.0[17.06-23.33]1.0 ![0.41-2.42]15.6[12.62-19.09]14.3[11.29-17.85]11.9[9.36-15.11]15.0[12.18-18.40]15.8[13.21-18.75]9.8[7.72-12.39]16.6[13.98-19.61]1.0 ![0.41-2.18]25.9[22.43-29.61]16.0[13.03-19.39]13.0[10.20-16.44]9.9[7.19-13.37]14.6[11.09-18.87]7.2[5.28-9.64]12.7[9.63-16.48]Urbanicity - Based on Urban-centric location of school - from CCD (School)City1.9 ![0.96-3.78]31.9[27.57-36.62]22.9[18.84-27.64]13.5[9.95-17.95]7.4[5.31-10.32]9.4[6.83-12.81]6.3[4.26-9.31]6.6[5.28-8.20]2.5 ![1.21-5.09]38.7[33.00-44.70]18.4[14.53-23.03]11.9[8.59-16.34]6.8[4.53-10.13]12.1[9.01-16.09]3.7[2.24-5.98]5.9[4.50-7.66]2.8[1.57-5.04]41.4[36.94-46.06]20.7[16.87-25.11]12.3[9.42-15.99]8.5[6.37-11.33]6.8[4.79-9.60]2.9[1.80-4.71]4.5[3.39-5.88]3.2 ![1.72-5.89]42.8[37.50-48.18]21.6[17.41-26.50]10.1[7.12-14.03]8.6[5.66-12.75]5.6[3.53-8.91]3.5[1.90-6.31]4.7[2.95-7.36]Suburb1.0 !![0.31-2.96]40.1[36.17-44.17]22.1[18.35-26.30]11.0[8.95-13.35]8.5[6.52-10.92]7.7[6.15-9.58]4.7[3.46-6.43]5.0[3.92-6.40]1.0 !![0.31-3.19]45.5[41.08-50.09]24.3[20.53-28.43]9.5[7.26-12.41]6.7[5.03-8.99]6.2[4.76-8.03]2.7[1.67-4.28]4.0[2.90-5.60]1.3 ![0.56-3.23]46.2[41.40-51.12]21.6[17.82-25.88]11.1[8.48-14.29]7.2[5.38-9.62]6.2[4.46-8.47]2.7[1.89-3.78]3.7[2.59-5.37]1.9 ![0.91-3.87]51.4[46.52-56.31]22.3[18.64-26.50]8.6[6.18-11.86]6.9[4.75-9.91]4.5[3.06-6.45]2.1[1.21-3.65]2.3[1.53-3.46]Town0.7 !![0.09-5.11]42.6[33.99-51.67]25.6[19.48-32.95]14.4[9.66-20.93]9.4[6.07-14.13]3.2[1.98-5.03]2.3 ![0.89-5.89]1.8[1.10-3.04]0.2 !![0.02-1.37]44.1[37.35-51.02]34.0[26.98-41.90]12.0[8.31-16.95]3.4[1.94-6.02]3.6[2.20-5.67]1.5 ![0.68-3.46]1.2 !![0.37-3.85]2.5 ![1.09-5.75]45.8[38.40-53.42]29.3[23.38-36.01]10.8[7.58-15.27]5.5[3.52-8.64]4.7[2.61-8.17]0.9 ![0.48-1.65]0.4 ![0.18-1.01]1.0 !![0.26-3.49]52.6[44.77-60.40]29.2[22.87-36.48]11.7[7.65-17.49]1.6[0.88-2.92]1.9 ![0.74-4.74]1.2 ![0.46-3.06]0.8 !![0.11-5.82]Rural5.4[3.21-8.92]61.4[56.79-65.75]18.3[14.95-22.12]6.6[5.01-8.68]4.7[3.42-6.40]2.2[1.51-3.32]0.8 ![0.41-1.72]0.6[0.39-0.92]3.9[2.18-7.04]58.7[54.21-63.07]20.4[16.36-25.20]7.0[5.05-9.57]4.7[3.14-6.90]2.8[1.85-4.22]1.2 ![0.58-2.41]1.3[0.78-2.09]4.1[2.30-7.33]61.9[57.64-66.02]14.8[11.95-18.26]10.4[8.04-13.41]4.3[2.68-6.75]2.5[1.66-3.87]1.0 ![0.46-1.96]0.9 ![0.48-1.78]2.7 ![1.22-5.87]67.2[62.19-71.79]15.1[11.33-19.73]8.4[5.59-12.40]3.2 ![1.71-5.73]2.8[1.71-4.66]0.5 ![0.23-0.98]0.2 ![0.09-0.59]Level of crime where students liveHigh level of crime0.9 !![0.16-5.32]22.2[14.30-32.79]16.4[10.59-24.64]19.4[12.01-29.69]15.2[9.68-23.04]11.8[7.29-18.52]7.7[4.58-12.65]6.4[4.19-9.60]5.2 ![2.09-12.36]31.3[21.75-42.69]18.4[11.62-28.00]10.1[5.46-17.91]8.8 ![4.39-16.87]14.8[8.55-24.54]4.3 ![2.03-9.03]7.0[4.25-11.35]3.0 !![0.81-10.48]38.3[29.49-48.04]19.8[12.99-28.91]9.7[5.69-15.96]9.4[5.40-16.00]10.3[5.99-17.05]2.7 !![0.96-7.23]6.8[4.19-10.94]3.5 !![1.00-11.67]32.3[23.75-42.33]20.4[13.29-30.08]8.3 ![4.19-15.65]14.2[8.34-23.19]5.7 ![2.93-10.86]6.9 ![3.47-13.32]8.6[5.16-13.90]Moderate level of crime1.5 !![0.50-4.47]32.2[27.56-37.16]25.3[20.94-30.26]13.1[9.58-17.56]7.1[4.50-11.02]8.0[5.75-11.08]5.7[3.57-9.05]7.1[5.54-9.08]1.2 !![0.35-4.16]30.8[25.71-36.50]24.2[19.97-28.90]14.2[10.29-19.26]7.7[5.44-10.78]11.8[8.50-16.11]4.5[2.86-7.00]5.6[3.61-8.65]1.1 !![0.28-4.11]41.6[36.50-46.86]23.3[19.08-28.12]12.6[9.37-16.79]6.9[4.71-9.87]6.9[4.48-10.34]3.0[1.83-4.74]4.7[3.26-6.85]0.9 !![0.28-3.17]43.6[36.88-50.50]26.8[21.64-32.74]10.8[7.70-14.84]8.4[5.66-12.43]4.1[2.32-7.23]2.4 ![1.29-4.37]2.9[1.75-4.91]Low level of crime3.3[2.18-5.09]53.6[50.34-56.88]20.7[18.03-23.57]8.3[6.83-10.10]5.6[4.19-7.33]4.1[3.26-5.13]2.4[1.72-3.39]2.0[1.48-2.65]2.5[1.46-4.10]58.7[55.45-61.94]21.7[19.04-24.52]7.6[6.10-9.53]4.4[3.21-5.89]2.9[2.07-4.07]0.9 ![0.50-1.74]1.3[0.89-1.91]3.2[1.96-5.04]57.8[54.88-60.66]19.4[17.53-21.33]9.7[8.01-11.60]4.5[3.42-5.84]3.2[2.40-4.20]1.3[0.86-1.95]1.1[0.74-1.56]2.8[1.64-4.82]61.9[58.71-64.94]18.7[16.20-21.56]8.2[6.34-10.63]3.0[2.04-4.42]3.8[2.77-5.30]0.6 ![0.34-1.19]0.8 ![0.42-1.72]Students come from areas with very different levels of crime1.6 !![0.41-6.24]37.6[30.81-44.95]22.3[17.09-28.64]11.9[8.54-16.45]9.4[6.45-13.57]8.0[5.60-11.44]4.0[2.26-6.84]5.1[3.61-7.05]0.8 !![0.10-5.55]33.3[27.09-40.22]29.7[24.49-35.46]11.7[7.76-17.32]6.4[4.03-10.07]8.0[5.44-11.52]3.9 ![2.07-7.35]6.2[4.52-8.45]3.7 ![1.81-7.55]33.9[27.40-41.16]20.4[15.65-26.21]16.2[11.50-22.32]12.7[9.02-17.47]7.0[4.21-11.53]2.8[1.53-4.90]3.3[1.98-5.31]1.6 ![0.59-4.27]46.0[37.85-54.31]22.8[16.05-31.27]12.9[8.45-19.34]7.1 ![3.44-14.17]3.4[1.86-5.96]3.6 !![1.28-9.54]2.7 ![1.13-6.17]# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: C0572, FR_LVEL, FR_SIZE, FR_URBAN and C0560. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: C0572 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_LVEL (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_SIZE (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_LOC4 (SSOCS:2006), C0560 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016) and FR_URBAN (SSOCS:2008, SSOCS:2010, SSOCS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006, 2007–08 School Survey on Crime and Safety (SSOCS), 2008, 2009-10 School Survey on Crime and Safety (SSOCS), 2010 and 2015-16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES TrendStats on 7/9/2018.mgbkdb19mgbkdb195Total number of incidents reported to police by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Level of crime where students live and Urbanicity - Based on Urban-centric location of school - from CCD (School) for years 2006, 2008, 2010 and 2016 Total number of incidents reported to police01 to 1011 to 2021 to 3031 to 5051 or moreTotalEstimatesTotal200639.141.17.83.83.84.4100%200838.042.88.63.62.84.2100%201040.042.96.82.93.73.8100%201652.635.15.32.72.12.2100%School grades offered - based on CCD frame variables (School)Primary200655.740.42.90.3 !!0.5 !0.2 !!100%200853.641.14.00.5 !0.3 !!0.4 !!100%201056.641.51.0 !0.2 !!0.3 !!0.5 !100%201671.326.41.2 !0.9 !!#0.2 !!100%Middle200614.848.115.67.87.56.3100%200814.351.615.68.83.85.9100%201017.850.714.95.87.03.8100%201630.952.58.23.52.42.5100%High school20066.530.819.110.413.219.9100%20088.231.518.210.513.218.5100%20106.735.617.110.112.517.9100%201613.939.916.59.19.511.0100%Combined200632.647.56.07.7 !3.2 !3.0 !100%200830.755.49.4 !1.9 !!0.5 !!2.2 !!100%201029.348.412.0 !2.2 !!5.6 !2.5 !100%201638.549.87.5 !1.9 !!2.3 !!#100%School size categories - based on CCD frame variables (School)Less than 300200652.341.93.42.1 !0.1 !!0.2 !!100%200852.043.43.11.0 !!0.2 !!0.3 !!100%201056.737.84.1 !0.6 !0.8 !!#100%201668.329.72.0 !!###100%300 - 499200648.544.74.40.9 !1.5#100%200847.844.15.71.50.9 !0.1 !!100%201046.645.74.81.60.6 !0.7 !100%201660.135.82.21.4 !0.4 !0.2 !!100%500 - 999200632.344.710.74.54.23.5100%200830.947.212.24.32.23.2100%201033.550.28.02.93.71.8100%201648.738.86.03.31.51.6100%1,000 or more20066.619.017.112.916.627.8100%20086.924.115.912.115.325.7100%20107.621.014.111.118.927.2100%201612.230.618.710.213.215.0100%Level of crime where students liveHigh level of crime200625.741.714.93.4 !5.58.8100%200825.744.414.74.9 !2.7 !7.5100%201025.344.89.45.2 !6.3 !8.9100%201643.841.03.8 !4.9 !2.0 !!4.5100%Moderate level of crime200629.947.27.93.24.96.8100%200826.947.211.24.63.76.5100%201035.845.65.62.94.85.3100%201643.539.57.82.03.14.1100%Low level of crime200644.439.96.43.63.12.6100%200844.241.76.72.92.12.3100%201045.541.36.22.22.62.2100%201657.133.34.22.51.71.2100%Students come from areas with very different levels of crime200635.537.410.25.84.66.5100%200834.639.89.54.34.96.9100%201030.544.19.94.45.55.5100%201653.131.77.13.62.12.4100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City200634.441.58.23.24.68.2100%200827.846.29.94.94.27.0100%201036.444.16.13.34.65.4100%201652.133.65.23.22.73.3100%Suburb200637.940.77.54.24.75.0100%200840.740.77.63.22.85.0100%201039.542.26.92.84.04.6100%201652.235.84.52.61.93.0100%Town200634.745.210.03.73.62.9100%200838.841.011.14.01.73.3100%201036.144.88.53.64.22.7100%201640.242.68.24.83.0 !1.1 !100%Rural200645.340.07.33.92.31.2100%200843.542.87.32.62.31.5100%201045.241.56.42.22.52.2100%201659.632.05.01.41.10.9100%Total number of incidents reported to police by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Level of crime where students live and Urbanicity - Based on Urban-centric location of school - from CCD (School) for years 2006, 2008, 2010 and 2016 Total number of incidents reported to police01 to 1011 to 2021 to 3031 to 5051 or moreTotalEstimatesTotal200639.141.17.83.83.84.4100%200838.042.88.63.62.84.2100%201040.042.96.82.93.73.8100%201652.635.15.32.72.12.2100%School grades offered - based on CCD frame variables (School)Primary200655.740.42.90.3 !!0.5 !0.2 !!100%200853.641.14.00.5 !0.3 !!0.4 !!100%201056.641.51.0 !0.2 !!0.3 !!0.5 !100%201671.326.41.2 !0.9 !!#0.2 !!100%Middle200614.848.115.67.87.56.3100%200814.351.615.68.83.85.9100%201017.850.714.95.87.03.8100%201630.952.58.23.52.42.5100%High school20066.530.819.110.413.219.9100%20088.231.518.210.513.218.5100%20106.735.617.110.112.517.9100%201613.939.916.59.19.511.0100%Combined200632.647.56.07.7 !3.2 !3.0 !100%200830.755.49.4 !1.9 !!0.5 !!2.2 !!100%201029.348.412.0 !2.2 !!5.6 !2.5 !100%201638.549.87.5 !1.9 !!2.3 !!#100%School size categories - based on CCD frame variables (School)Less than 300200652.341.93.42.1 !0.1 !!0.2 !!100%200852.043.43.11.0 !!0.2 !!0.3 !!100%201056.737.84.1 !0.6 !0.8 !!#100%201668.329.72.0 !!###100%300 - 499200648.544.74.40.9 !1.5#100%200847.844.15.71.50.9 !0.1 !!100%201046.645.74.81.60.6 !0.7 !100%201660.135.82.21.4 !0.4 !0.2 !!100%500 - 999200632.344.710.74.54.23.5100%200830.947.212.24.32.23.2100%201033.550.28.02.93.71.8100%201648.738.86.03.31.51.6100%1,000 or more20066.619.017.112.916.627.8100%20086.924.115.912.115.325.7100%20107.621.014.111.118.927.2100%201612.230.618.710.213.215.0100%Level of crime where students liveHigh level of crime200625.741.714.93.4 !5.58.8100%200825.744.414.74.9 !2.7 !7.5100%201025.344.89.45.2 !6.3 !8.9100%201643.841.03.8 !4.9 !2.0 !!4.5100%Moderate level of crime200629.947.27.93.24.96.8100%200826.947.211.24.63.76.5100%201035.845.65.62.94.85.3100%201643.539.57.82.03.14.1100%Low level of crime200644.439.96.43.63.12.6100%200844.241.76.72.92.12.3100%201045.541.36.22.22.62.2100%201657.133.34.22.51.71.2100%Students come from areas with very different levels of crime200635.537.410.25.84.66.5100%200834.639.89.54.34.96.9100%201030.544.19.94.45.55.5100%201653.131.77.13.62.12.4100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City200634.441.58.23.24.68.2100%200827.846.29.94.94.27.0100%201036.444.16.13.34.65.4100%201652.133.65.23.22.73.3100%Suburb200637.940.77.54.24.75.0100%200840.740.77.63.22.85.0100%201039.542.26.92.84.04.6100%201652.235.84.52.61.93.0100%Town200634.745.210.03.73.62.9100%200838.841.011.14.01.73.3100%201036.144.88.53.64.22.7100%201640.242.68.24.83.0 !1.1 !100%Rural200645.340.07.33.92.31.2100%200843.542.87.32.62.31.5100%201045.241.56.42.22.52.2100%201659.632.05.01.41.10.9100%Standard Error (BRR)Total20061.151.050.460.310.210.22 20081.241.410.610.280.210.31 20101.581.590.410.240.260.20 20161.541.560.430.300.220.19 School grades offered - based on CCD frame variables (School)Primary20061.631.510.580.260.230.17 20082.062.140.810.250.230.22 20102.282.240.350.150.180.21 20162.142.130.490.48†0.16 Middle20060.961.321.160.890.860.71 20081.231.611.190.860.700.65 20101.221.431.090.830.690.50 20161.812.020.960.630.550.46 High school20061.141.661.401.031.090.94 20081.051.451.371.080.981.22 20101.061.691.360.811.180.89 20161.661.841.200.850.810.89 Combined20064.615.131.792.841.251.08 20085.585.792.871.180.391.13 20105.175.363.991.202.161.22 20165.315.722.961.371.42† School size categories - based on CCD frame variables (School)Less than 30020062.812.910.820.930.140.15 20082.662.680.920.520.190.22 20103.343.511.330.310.73† 20163.303.301.27††† 300 - 49920062.762.670.740.320.44† 20082.362.441.290.320.410.08 20102.612.490.750.420.250.33 20162.622.760.580.540.180.17 500 - 99920061.872.030.850.520.410.57 20082.202.301.200.600.310.70 20102.332.310.560.420.440.34 20162.272.260.730.710.370.39 1,000 or more20061.491.851.621.231.361.28 20081.441.821.840.951.121.68 20101.562.111.221.101.691.44 20161.772.362.080.881.281.38 Level of crime where students liveHigh level of crime20064.034.733.611.021.401.61 20084.144.373.211.580.901.39 20104.784.892.281.632.081.60 20165.174.861.781.491.101.16 Moderate level of crime20063.263.091.220.580.670.77 20083.143.341.670.830.680.87 20103.153.370.760.440.840.71 20164.083.871.550.450.520.64 Low level of crime20061.531.390.510.400.330.24 20081.892.000.630.230.220.25 20102.002.130.590.240.310.24 20161.921.990.400.370.290.15 Students come from areas with very different levels of crime20063.423.311.701.590.740.93 20083.723.411.560.960.761.16 20103.543.501.601.030.860.96 20163.773.401.820.970.540.62 Urbanicity - Based on Urban-centric location of school - from CCD (School)City20062.522.491.110.440.520.57 20082.662.841.360.800.610.71 20102.252.300.800.490.560.46 20162.952.760.980.640.500.58 Suburb20062.122.030.720.420.490.40 20082.262.450.900.380.320.54 20102.392.370.640.500.500.40 20161.941.840.700.430.320.34 Town20063.564.051.310.890.750.76 20083.503.701.880.680.470.64 20103.543.101.260.580.730.56 20163.693.821.571.371.020.39 Rural20062.342.690.790.920.380.22 20082.182.320.990.420.310.25 20102.212.331.010.400.540.44 20162.662.540.770.370.280.24 Relative Standard Error (%)Total20062.952.565.848.185.485.04 20083.273.307.117.977.427.34 20103.953.726.068.367.065.13 20162.934.448.1410.8010.498.63 School grades offered - based on CCD frame variables (School)Primary20062.933.7519.9877.8849.1970.84 20083.845.2020.2048.2672.1256.32 20104.035.4035.0572.8069.2144.55 20163.008.0742.0851.95†100.48 Middle20066.492.747.4811.4511.4811.21 20088.583.117.649.8018.0910.95 20106.812.827.3314.349.8213.10 20165.863.8411.7217.9023.3018.14 High school200617.545.387.329.858.234.70 200812.894.597.5310.297.406.60 201015.784.747.997.969.394.98 201611.944.627.289.308.508.11 Combined200614.1610.8129.8036.7338.9435.73 200818.2110.4430.6863.7974.1151.60 201017.6111.0733.3653.7638.6249.10 201613.7911.5039.2872.4962.60† School size categories - based on CCD frame variables (School)Less than 30020065.376.9523.6644.58100.12100.39 20085.126.1929.4953.49100.3770.05 20105.899.3032.5948.1193.32† 20164.8411.0864.85††† 300 - 49920065.685.9716.7734.6929.40† 20084.935.5522.6121.5946.08100.16 20105.595.4415.7526.6642.1843.95 20164.357.7226.8739.1248.3170.32 500 - 99920065.804.547.9311.629.6116.12 20087.124.889.8413.9014.0521.72 20106.954.597.0514.4211.9318.93 20164.665.8312.2121.4723.9524.39 1,000 or more200622.659.739.439.488.204.61 200820.867.5511.537.907.296.54 201020.6810.068.689.898.905.28 201614.517.6911.138.629.769.21 Level of crime where students liveHigh level of crime200615.7111.3324.2930.3425.4118.21 200816.119.8521.8132.1532.9118.46 201018.8510.9124.2530.9832.8418.08 201611.8111.8447.1730.7055.1125.43 Moderate level of crime200610.896.5415.4118.2813.6911.36 200811.667.0714.9518.2618.3113.42 20108.797.3813.7215.2917.5613.28 20169.379.8019.8222.9916.9815.53 Low level of crime20063.453.497.9211.0510.699.03 20084.284.799.428.0310.4910.90 20104.395.159.5610.5612.1411.03 20163.375.989.4314.5517.2612.34 Students come from areas with very different levels of crime20069.638.8616.6227.5716.0914.36 200810.738.5716.4322.5315.6016.80 201011.627.9316.1623.3315.5317.37 20167.1010.7225.6527.2225.4825.72 Urbanicity - Based on Urban-centric location of school - from CCD (School)City20067.326.0113.5713.7011.237.00 20089.576.1413.7616.2714.3310.20 20106.185.2213.1114.7712.158.40 20165.688.2118.8420.2518.3317.72 Suburb20065.604.989.5910.1210.288.07 20085.556.0111.8011.7911.3410.95 20106.045.609.3017.9512.518.62 20163.725.1515.7116.4616.5311.63 Town200610.268.9713.1224.1320.6826.23 20089.019.0116.9017.1328.7619.26 20109.816.9214.7516.0717.2220.76 20169.188.9719.1328.2833.8434.13 Rural20065.166.7410.9223.3416.7317.82 20085.015.4113.5516.0513.6116.83 20104.895.6215.7618.6021.5320.20 20164.477.9415.3425.6526.1527.47 Weighted Sample Sizes (n/1,000s)Total200683.2 200883.0 201082.8 201683.6 School grades offered - based on CCD frame variables (School)Primary200648.6 200849.2 201048.9 201649.1 Middle200615.5 200815.3 201015.3 201615.6 High school200611.7 200811.9 201012.2 201612.8 Combined20067.4 20086.6 20106.4 20166.2 School size categories - based on CCD frame variables (School)Less than 300200620.8 200819.2 201018.9 201618.2 300 - 499200623.8 200824.3 201025.2 201625.0 500 - 999200629.3 200830.2 201029.8 201631.7 1,000 or more20069.3 20089.3 20108.9 20168.7 Level of crime where students liveHigh level of crime20066.5 20086.2 20105.9 20167.4 Moderate level of crime200615.9 200817.1 201018.4 201617.5 Low level of crime200650.3 200849.2 201047.7 201648.4 Students come from areas with very different levels of crime200610.5 200810.5 201010.7 201610.4 Urbanicity - Based on Urban-centric location of school - from CCD (School)City200621.0 200821.3 201021.5 201622.8 Suburb200627.6 200823.9 201023.8 201627.4 Town20068.2 200811.8 201012.1 201611.0 Rural200626.4 200826.0 201025.3 201622.5 Total number of incidents reported to police by School grades offered - based on CCD frame variables (School), School size categories - based on CCD frame variables (School), Level of crime where students live and Urbanicity - Based on Urban-centric location of school - from CCD (School) for years 2006, 2008, 2010 and 2016 Total number of incidents reported to police01 to 1011 to 2021 to 3031 to 5051 or moreTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal200639.1[36.77-41.40]41.1[39.02-43.25]7.8[6.95-8.79]3.8[3.22-4.48]3.8[3.41-4.25]4.4[3.96-4.85]100%200838.0[35.56-40.55]42.8[40.00-45.68]8.6[7.45-9.91]3.6[3.04-4.19]2.8[2.44-3.29]4.2[3.58-4.81]100%201040.0[36.83-43.17]42.9[39.69-46.09]6.8[5.99-7.65]2.9[2.42-3.39]3.7[3.23-4.29]3.8[3.44-4.22]100%201652.6[49.48-55.67]35.1[32.01-38.25]5.3[4.50-6.24]2.7[2.20-3.40]2.1[1.67-2.54]2.2[1.89-2.67]100%School grades offered - based on CCD frame variables (School)Primary200655.7[52.37-58.92]40.4[37.40-43.48]2.9[1.94-4.33]0.3 !![0.07-1.56]0.5 ![0.17-1.24]0.2 !![0.06-0.98]100%200853.6[49.49-57.75]41.1[36.89-45.47]4.0[2.67-6.00]0.5 ![0.19-1.34]0.3 !![0.08-1.36]0.4 !![0.13-1.23]100%201056.6[51.93-61.07]41.5[37.09-46.08]1.0 ![0.49-2.00]0.2 !![0.05-0.92]0.3 !![0.06-1.02]0.5 ![0.19-1.13]100%201671.3[66.84-75.44]26.4[22.36-30.91]1.2 ![0.50-2.71]0.9 !![0.33-2.62]##0.2 !![0.02-1.19]100%Middle200614.8[12.97-16.84]48.1[45.42-50.70]15.6[13.36-18.04]7.8[6.18-9.79]7.5[5.93-9.40]6.3[5.03-7.90]100%200814.3[11.99-16.92]51.6[48.42-54.86]15.6[13.33-18.12]8.8[7.18-10.64]3.8[2.67-5.51]5.9[4.72-7.33]100%201017.8[15.54-20.42]50.7[47.84-53.57]14.9[12.81-17.20]5.8[4.33-7.71]7.0[5.73-8.51]3.8[2.90-4.91]100%201630.9[27.36-34.61]52.5[48.41-56.50]8.2[6.48-10.38]3.5[2.46-5.06]2.4[1.48-3.77]2.5[1.75-3.63]100%High school20066.5[4.54-9.18]30.8[27.59-34.24]19.1[16.47-22.10]10.4[8.54-12.69]13.2[11.19-15.58]19.9[18.09-21.86]100%20088.2[6.28-10.53]31.5[28.66-34.47]18.2[15.57-21.06]10.5[8.51-12.86]13.2[11.35-15.27]18.5[16.19-21.10]100%20106.7[4.88-9.19]35.6[32.28-39.06]17.1[14.52-20.01]10.1[8.63-11.88]12.5[10.36-15.10]17.9[16.19-19.77]100%201613.9[10.91-17.61]39.9[36.30-43.70]16.5[14.23-19.06]9.1[7.55-10.96]9.5[8.00-11.26]11.0[9.34-12.93]100%Combined200632.6[24.04-42.39]47.5[37.40-57.74]6.0[3.27-10.79]7.7 ![3.63-15.70]3.2 ![1.46-6.93]3.0 ![1.47-6.16]100%200830.7[20.69-42.83]55.4[43.72-66.57]9.4 ![4.97-16.92]1.9 !![0.51-6.52]0.5 !![0.12-2.29]2.2 !![0.77-6.05]100%201029.3[20.10-40.64]48.4[37.86-59.05]12.0 ![5.97-22.53]2.2 !![0.75-6.45]5.6 ![2.54-11.89]2.5 ![0.92-6.57]100%201638.5[28.54-49.59]49.8[38.49-61.09]7.5 ![3.36-16.07]1.9 !![0.43-7.83]2.3 !![0.64-7.75]##100%School size categories - based on CCD frame variables (School)Less than 300200652.3[46.64-57.88]41.9[36.18-47.82]3.4[2.14-5.52]2.1 ![0.85-5.06]0.1 !![0.02-1.00]0.2 !![0.02-1.13]100%200852.0[46.69-57.35]43.4[38.07-48.80]3.1[1.72-5.62]1.0 !![0.33-2.84]0.2 !![0.02-1.38]0.3 !![0.08-1.25]100%201056.7[49.91-63.25]37.8[31.02-45.05]4.1 ![2.11-7.77]0.6 ![0.25-1.70]0.8 !![0.12-4.98]##100%201668.3[61.33-74.54]29.7[23.57-36.76]2.0 !![0.52-6.99]######100%300 - 499200648.5[43.01-54.04]44.7[39.38-50.05]4.4[3.15-6.18]0.9 ![0.45-1.83]1.5[0.83-2.70]##100%200847.8[43.06-52.50]44.1[39.25-49.04]5.7[3.59-8.90]1.5[0.96-2.29]0.9 ![0.35-2.25]0.1 !![0.01-0.63]100%201046.6[41.45-51.88]45.7[40.75-50.70]4.8[3.48-6.54]1.6[0.91-2.67]0.6 ![0.25-1.38]0.7 ![0.31-1.80]100%201660.1[54.73-65.21]35.8[30.42-41.47]2.2[1.26-3.70]1.4 ![0.63-3.02]0.4 ![0.14-0.97]0.2 !![0.06-0.96]100%500 - 999200632.3[28.66-36.18]44.7[40.69-48.83]10.7[9.15-12.58]4.5[3.53-5.64]4.2[3.49-5.14]3.5[2.54-4.85]100%200830.9[26.63-35.43]47.2[42.62-51.85]12.2[9.96-14.79]4.3[3.23-5.65]2.2[1.68-2.96]3.2[2.09-4.99]100%201033.5[28.95-38.27]50.2[45.63-54.86]8.0[6.90-9.16]2.9[2.16-3.85]3.7[2.88-4.65]1.8[1.22-2.61]100%201648.7[44.20-53.31]38.8[34.34-43.40]6.0[4.69-7.66]3.3[2.15-5.08]1.5[0.95-2.49]1.6[0.99-2.63]100%1,000 or more20066.6[4.14-10.25]19.0[15.55-22.97]17.1[14.12-20.62]12.9[10.66-15.60]16.6[14.05-19.53]27.8[25.30-30.44]100%20086.9[4.50-10.39]24.1[20.63-27.93]15.9[12.57-19.96]12.1[10.27-14.10]15.3[13.21-17.69]25.7[22.49-29.25]100%20107.6[4.95-11.35]21.0[17.09-25.58]14.1[11.83-16.76]11.1[9.11-13.55]18.9[15.78-22.56]27.2[24.44-30.21]100%201612.2[9.06-16.21]30.6[26.12-35.57]18.7[14.90-23.29]10.2[8.60-12.16]13.2[10.78-15.95]15.0[12.46-18.04]100%Level of crime where students liveHigh level of crime200625.7[18.43-34.57]41.7[32.66-51.44]14.9[8.96-23.66]3.4 ![1.81-6.12]5.5[3.29-9.10]8.8[6.09-12.65]100%200825.7[18.29-34.85]44.4[35.87-53.27]14.7[9.37-22.42]4.9 ![2.55-9.23]2.7 ![1.40-5.24]7.5[5.16-10.83]100%201025.3[16.98-36.06]44.8[35.31-54.71]9.4[5.72-15.09]5.2 ![2.79-9.65]6.3 ![3.23-12.02]8.9[6.13-12.65]100%201643.8[33.79-54.26]41.0[31.74-51.04]3.8 ![1.45-9.52]4.9 ![2.60-8.91]2.0 !![0.66-5.94]4.5[2.71-7.52]100%Moderate level of crime200629.9[23.82-36.87]47.2[41.08-53.42]7.9[5.80-10.76]3.2[2.21-4.60]4.9[3.73-6.47]6.8[5.40-8.53]100%200826.9[21.08-33.64]47.2[40.55-53.87]11.2[8.25-15.03]4.6[3.14-6.54]3.7[2.55-5.32]6.5[4.95-8.48]100%201035.8[29.79-42.39]45.6[38.96-52.41]5.6[4.22-7.31]2.9[2.10-3.89]4.8[3.37-6.81]5.3[4.07-6.93]100%201643.5[35.59-51.83]39.5[32.06-47.49]7.8[5.22-11.55]2.0[1.23-3.09]3.1[2.19-4.33]4.1[2.99-5.58]100%Low level of crime200644.4[41.37-47.51]39.9[37.12-42.71]6.4[5.44-7.47]3.6[2.91-4.54]3.1[2.48-3.80]2.6[2.17-3.12]100%200844.2[40.42-48.01]41.7[37.79-45.81]6.7[5.56-8.12]2.9[2.47-3.41]2.1[1.71-2.60]2.3[1.87-2.89]100%201045.5[41.50-49.51]41.3[37.09-45.62]6.2[5.12-7.51]2.2[1.80-2.75]2.6[2.02-3.29]2.2[1.77-2.76]100%201657.1[53.21-60.92]33.3[29.39-37.36]4.2[3.50-5.11]2.5[1.88-3.37]1.7[1.19-2.38]1.2[0.93-1.53]100%Students come from areas with very different levels of crime200635.5[29.00-42.68]37.4[31.01-44.24]10.2[7.26-14.15]5.8[3.29-9.94]4.6[3.33-6.35]6.5[4.84-8.62]100%200834.6[27.58-42.41]39.8[33.18-46.81]9.5[6.79-13.13]4.3[2.71-6.69]4.9[3.57-6.67]6.9[4.91-9.64]100%201030.5[23.88-38.05]44.1[37.23-51.19]9.9[7.14-13.65]4.4[2.74-6.99]5.5[4.04-7.54]5.5[3.89-7.82]100%201653.1[45.48-60.51]31.7[25.31-38.91]7.1[4.21-11.77]3.6[2.05-6.11]2.1[1.27-3.54]2.4[1.43-4.02]100%Urbanicity - Based on Urban-centric location of school - from CCD (School)City200634.4[29.53-39.61]41.5[36.55-46.53]8.2[6.21-10.70]3.2[2.43-4.22]4.6[3.68-5.77]8.2[7.08-9.37]100%200827.8[22.80-33.47]46.2[40.55-51.91]9.9[7.46-12.96]4.9[3.54-6.81]4.2[3.17-5.63]7.0[5.67-8.55]100%201036.4[32.03-41.05]44.1[39.50-48.71]6.1[4.69-7.94]3.3[2.49-4.50]4.6[3.61-5.88]5.4[4.60-6.45]100%201652.1[46.13-57.94]33.6[28.27-39.30]5.2[3.53-7.53]3.2[2.12-4.77]2.7[1.89-3.94]3.3[2.29-4.67]100%Suburb200637.9[33.77-42.29]40.7[36.73-44.87]7.5[6.14-9.02]4.2[3.39-5.09]4.7[3.85-5.82]5.0[4.22-5.84]100%200840.7[36.25-45.30]40.7[35.90-45.70]7.6[6.00-9.64]3.2[2.53-4.07]2.8[2.23-3.51]5.0[3.97-6.17]100%201039.5[34.84-44.40]42.2[37.57-47.05]6.9[5.69-8.27]2.8[1.93-3.98]4.0[3.09-5.10]4.6[3.88-5.49]100%201652.2[48.32-56.11]35.8[32.21-39.60]4.5[3.25-6.11]2.6[1.86-3.60]1.9[1.39-2.69]3.0[2.33-3.72]100%Town200634.7[27.91-42.12]45.2[37.22-53.36]10.0[7.62-12.90]3.7[2.26-5.96]3.6[2.38-5.45]2.9[1.71-4.90]100%200838.8[32.09-46.06]41.0[33.87-48.62]11.1[7.89-15.53]4.0[2.82-5.61]1.7[0.92-2.93]3.3[2.26-4.89]100%201036.1[29.36-43.52]44.8[38.66-51.06]8.5[6.31-11.41]3.6[2.62-5.00]4.2[2.99-5.96]2.7[1.78-4.09]100%201640.2[33.06-47.79]42.6[35.16-50.39]8.2[5.56-11.98]4.8[2.72-8.44]3.0 ![1.52-5.91]1.1 ![0.57-2.23]100%Rural200645.3[40.66-50.03]40.0[34.71-45.50]7.3[5.82-9.03]3.9[2.45-6.26]2.3[1.62-3.17]1.2[0.87-1.77]100%200843.5[39.21-47.96]42.8[38.22-47.50]7.3[5.55-9.57]2.6[1.88-3.59]2.3[1.72-2.97]1.5[1.06-2.09]100%201045.2[40.81-49.66]41.5[36.93-46.29]6.4[4.65-8.76]2.2[1.50-3.16]2.5[1.62-3.84]2.2[1.46-3.28]100%201659.6[54.15-64.82]32.0[27.12-37.30]5.0[3.67-6.80]1.4[0.86-2.40]1.1[0.63-1.80]0.9[0.51-1.54]100%2006200820102016 Total number of incidents reported to policeTotal number of incidents reported to policeTotal number of incidents reported to policeTotal number of incidents reported to police 01 to 1011 to 2021 to 3031 to 5051 or more01 to 1011 to 2021 to 3031 to 5051 or more01 to 1011 to 2021 to 3031 to 5051 or more01 to 1011 to 2021 to 3031 to 5051 or moreEstimatesTotal39.141.17.83.83.84.438.042.88.63.62.84.240.042.96.82.93.73.852.635.15.32.72.12.2School grades offered - based on CCD frame variables (School)Primary55.740.42.90.30.50.253.641.14.00.50.30.456.641.51.00.20.30.571.326.41.20.9#0.2Middle14.848.115.67.87.56.314.351.615.68.83.85.917.850.714.95.87.03.830.952.58.23.52.42.5High school6.530.819.110.413.219.98.231.518.210.513.218.56.735.617.110.112.517.913.939.916.59.19.511.0Combined32.647.56.07.73.23.030.755.49.41.90.52.229.348.412.02.25.62.538.549.87.51.92.3#School size categories - based on CCD frame variables (School)Less than 30052.341.93.42.10.10.252.043.43.11.00.20.356.737.84.10.60.8#68.329.72.0###300 - 49948.544.74.40.91.5#47.844.15.71.50.90.146.645.74.81.60.60.760.135.82.21.40.40.2500 - 99932.344.710.74.54.23.530.947.212.24.32.23.233.550.28.02.93.71.848.738.86.03.31.51.61,000 or more6.619.017.112.916.627.86.924.115.912.115.325.77.621.014.111.118.927.212.230.618.710.213.215.0Level of crime where students liveHigh level of crime25.741.714.93.45.58.825.744.414.74.92.77.525.344.89.45.26.38.943.841.03.84.92.04.5Moderate level of crime29.947.27.93.24.96.826.947.211.24.63.76.535.845.65.62.94.85.343.539.57.82.03.14.1Low level of crime44.439.96.43.63.12.644.241.76.72.92.12.345.541.36.22.22.62.257.133.34.22.51.71.2Students come from areas with very different levels of crime35.537.410.25.84.66.534.639.89.54.34.96.930.544.19.94.45.55.553.131.77.13.62.12.4Urbanicity - Based on Urban-centric location of school - from CCD (School)City34.441.58.23.24.68.227.846.29.94.94.27.036.444.16.13.34.65.452.133.65.23.22.73.3Suburb37.940.77.54.24.75.040.740.77.63.22.85.039.542.26.92.84.04.652.235.84.52.61.93.0Town34.745.210.03.73.62.938.841.011.14.01.73.336.144.88.53.64.22.740.242.68.24.83.01.1Rural45.340.07.33.92.31.243.542.87.32.62.31.545.241.56.42.22.52.259.632.05.01.41.10.92006200820102016 Total number of incidents reported to policeTotal number of incidents reported to policeTotal number of incidents reported to policeTotal number of incidents reported to police 01 to 1011 to 2021 to 3031 to 5051 or more01 to 1011 to 2021 to 3031 to 5051 or more01 to 1011 to 2021 to 3031 to 5051 or more01 to 1011 to 2021 to 3031 to 5051 or moreEstimatesTotal39.141.17.83.83.84.438.042.88.63.62.84.240.042.96.82.93.73.852.635.15.32.72.12.2School grades offered - based on CCD frame variables (School)Primary55.740.42.90.30.50.253.641.14.00.50.30.456.641.51.00.20.30.571.326.41.20.9#0.2Middle14.848.115.67.87.56.314.351.615.68.83.85.917.850.714.95.87.03.830.952.58.23.52.42.5High school6.530.819.110.413.219.98.231.518.210.513.218.56.735.617.110.112.517.913.939.916.59.19.511.0Combined32.647.56.07.73.23.030.755.49.41.90.52.229.348.412.02.25.62.538.549.87.51.92.3#School size categories - based on CCD frame variables (School)Less than 30052.341.93.42.10.10.252.043.43.11.00.20.356.737.84.10.60.8#68.329.72.0###300 - 49948.544.74.40.91.5#47.844.15.71.50.90.146.645.74.81.60.60.760.135.82.21.40.40.2500 - 99932.344.710.74.54.23.530.947.212.24.32.23.233.550.28.02.93.71.848.738.86.03.31.51.61,000 or more6.619.017.112.916.627.86.924.115.912.115.325.77.621.014.111.118.927.212.230.618.710.213.215.0Level of crime where students liveHigh level of crime25.741.714.93.45.58.825.744.414.74.92.77.525.344.89.45.26.38.943.841.03.84.92.04.5Moderate level of crime29.947.27.93.24.96.826.947.211.24.63.76.535.845.65.62.94.85.343.539.57.82.03.14.1Low level of crime44.439.96.43.63.12.644.241.76.72.92.12.345.541.36.22.22.62.257.133.34.22.51.71.2Students come from areas with very different levels of crime35.537.410.25.84.66.534.639.89.54.34.96.930.544.19.94.45.55.553.131.77.13.62.12.4Urbanicity - Based on Urban-centric location of school - from CCD (School)City34.441.58.23.24.68.227.846.29.94.94.27.036.444.16.13.34.65.452.133.65.23.22.73.3Suburb37.940.77.54.24.75.040.740.77.63.22.85.039.542.26.92.84.04.652.235.84.52.61.93.0Town34.745.210.03.73.62.938.841.011.14.01.73.336.144.88.53.64.22.740.242.68.24.83.01.1Rural45.340.07.33.92.31.243.542.87.32.62.31.545.241.56.42.22.52.259.632.05.01.41.10.9Standard Error (BRR)Total1.151.050.460.310.210.221.241.410.610.280.210.311.581.590.410.240.260.201.541.560.430.300.220.19School grades offered - based on CCD frame variables (School)Primary1.631.510.580.260.230.172.062.140.810.250.230.222.282.240.350.150.180.212.142.130.490.48†0.16Middle0.961.321.160.890.860.711.231.611.190.860.700.651.221.431.090.830.690.501.812.020.960.630.550.46High school1.141.661.401.031.090.941.051.451.371.080.981.221.061.691.360.811.180.891.661.841.200.850.810.89Combined4.615.131.792.841.251.085.585.792.871.180.391.135.175.363.991.202.161.225.315.722.961.371.42†School size categories - based on CCD frame variables (School)Less than 3002.812.910.820.930.140.152.662.680.920.520.190.223.343.511.330.310.73†3.303.301.27†††300 - 4992.762.670.740.320.44†2.362.441.290.320.410.082.612.490.750.420.250.332.622.760.580.540.180.17500 - 9991.872.030.850.520.410.572.202.301.200.600.310.702.332.310.560.420.440.342.272.260.730.710.370.391,000 or more1.491.851.621.231.361.281.441.821.840.951.121.681.562.111.221.101.691.441.772.362.080.881.281.38Level of crime where students liveHigh level of crime4.034.733.611.021.401.614.144.373.211.580.901.394.784.892.281.632.081.605.174.861.781.491.101.16Moderate level of crime3.263.091.220.580.670.773.143.341.670.830.680.873.153.370.760.440.840.714.083.871.550.450.520.64Low level of crime1.531.390.510.400.330.241.892.000.630.230.220.252.002.130.590.240.310.241.921.990.400.370.290.15Students come from areas with very different levels of crime3.423.311.701.590.740.933.723.411.560.960.761.163.543.501.601.030.860.963.773.401.820.970.540.62Urbanicity - Based on Urban-centric location of school - from CCD (School)City2.522.491.110.440.520.572.662.841.360.800.610.712.252.300.800.490.560.462.952.760.980.640.500.58Suburb2.122.030.720.420.490.402.262.450.900.380.320.542.392.370.640.500.500.401.941.840.700.430.320.34Town3.564.051.310.890.750.763.503.701.880.680.470.643.543.101.260.580.730.563.693.821.571.371.020.39Rural2.342.690.790.920.380.222.182.320.990.420.310.252.212.331.010.400.540.442.662.540.770.370.280.24Relative Standard Error (%)Total2.952.565.848.185.485.043.273.307.117.977.427.343.953.726.068.367.065.132.934.448.1410.8010.498.63School grades offered - based on CCD frame variables (School)Primary2.933.7519.9877.8849.1970.843.845.2020.2048.2672.1256.324.035.4035.0572.8069.2144.553.008.0742.0851.95†100.48Middle6.492.747.4811.4511.4811.218.583.117.649.8018.0910.956.812.827.3314.349.8213.105.863.8411.7217.9023.3018.14High school17.545.387.329.858.234.7012.894.597.5310.297.406.6015.784.747.997.969.394.9811.944.627.289.308.508.11Combined14.1610.8129.8036.7338.9435.7318.2110.4430.6863.7974.1151.6017.6111.0733.3653.7638.6249.1013.7911.5039.2872.4962.60†School size categories - based on CCD frame variables (School)Less than 3005.376.9523.6644.58100.12100.395.126.1929.4953.49100.3770.055.899.3032.5948.1193.32†4.8411.0864.85†††300 - 4995.685.9716.7734.6929.40†4.935.5522.6121.5946.08100.165.595.4415.7526.6642.1843.954.357.7226.8739.1248.3170.32500 - 9995.804.547.9311.629.6116.127.124.889.8413.9014.0521.726.954.597.0514.4211.9318.934.665.8312.2121.4723.9524.391,000 or more22.659.739.439.488.204.6120.867.5511.537.907.296.5420.6810.068.689.898.905.2814.517.6911.138.629.769.21Level of crime where students liveHigh level of crime15.7111.3324.2930.3425.4118.2116.119.8521.8132.1532.9118.4618.8510.9124.2530.9832.8418.0811.8111.8447.1730.7055.1125.43Moderate level of crime10.896.5415.4118.2813.6911.3611.667.0714.9518.2618.3113.428.797.3813.7215.2917.5613.289.379.8019.8222.9916.9815.53Low level of crime3.453.497.9211.0510.699.034.284.799.428.0310.4910.904.395.159.5610.5612.1411.033.375.989.4314.5517.2612.34Students come from areas with very different levels of crime9.638.8616.6227.5716.0914.3610.738.5716.4322.5315.6016.8011.627.9316.1623.3315.5317.377.1010.7225.6527.2225.4825.72Urbanicity - Based on Urban-centric location of school - from CCD (School)City7.326.0113.5713.7011.237.009.576.1413.7616.2714.3310.206.185.2213.1114.7712.158.405.688.2118.8420.2518.3317.72Suburb5.604.989.5910.1210.288.075.556.0111.8011.7911.3410.956.045.609.3017.9512.518.623.725.1515.7116.4616.5311.63Town10.268.9713.1224.1320.6826.239.019.0116.9017.1328.7619.269.816.9214.7516.0717.2220.769.188.9719.1328.2833.8434.13Rural5.166.7410.9223.3416.7317.825.015.4113.5516.0513.6116.834.895.6215.7618.6021.5320.204.477.9415.3425.6526.1527.47Weighted Sample Sizes (n/1,000s)Total83.2 83.0 82.8 83.6 School grades offered - based on CCD frame variables (School)Primary48.6 49.2 48.9 49.1 Middle15.5 15.3 15.3 15.6 High school11.7 11.9 12.2 12.8 Combined7.4 6.6 6.4 6.2 School size categories - based on CCD frame variables (School)Less than 30020.8 19.2 18.9 18.2 300 - 49923.8 24.3 25.2 25.0 500 - 99929.3 30.2 29.8 31.7 1,000 or more9.3 9.3 8.9 8.7 Level of crime where students liveHigh level of crime6.5 6.2 5.9 7.4 Moderate level of crime15.9 17.1 18.4 17.5 Low level of crime50.3 49.2 47.7 48.4 Students come from areas with very different levels of crime10.5 10.5 10.7 10.4 Urbanicity - Based on Urban-centric location of school - from CCD (School)City21.0 21.3 21.5 22.8 Suburb27.6 23.9 23.8 27.4 Town8.2 11.8 12.1 11.0 Rural26.4 26.0 25.3 22.5 2006200820102016 Total number of incidents reported to policeTotal number of incidents reported to policeTotal number of incidents reported to policeTotal number of incidents reported to police 01 to 1011 to 2021 to 3031 to 5051 or more01 to 1011 to 2021 to 3031 to 5051 or more01 to 1011 to 2021 to 3031 to 5051 or more01 to 1011 to 2021 to 3031 to 5051 or more Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal39.1[36.77-41.40]41.1[39.02-43.25]7.8[6.95-8.79]3.8[3.22-4.48]3.8[3.41-4.25]4.4[3.96-4.85]38.0[35.56-40.55]42.8[40.00-45.68]8.6[7.45-9.91]3.6[3.04-4.19]2.8[2.44-3.29]4.2[3.58-4.81]40.0[36.83-43.17]42.9[39.69-46.09]6.8[5.99-7.65]2.9[2.42-3.39]3.7[3.23-4.29]3.8[3.44-4.22]52.6[49.48-55.67]35.1[32.01-38.25]5.3[4.50-6.24]2.7[2.20-3.40]2.1[1.67-2.54]2.2[1.89-2.67]School grades offered - based on CCD frame variables (School)Primary55.7[52.37-58.92]40.4[37.40-43.48]2.9[1.94-4.33]0.3 !![0.07-1.56]0.5 ![0.17-1.24]0.2 !![0.06-0.98]53.6[49.49-57.75]41.1[36.89-45.47]4.0[2.67-6.00]0.5 ![0.19-1.34]0.3 !![0.08-1.36]0.4 !![0.13-1.23]56.6[51.93-61.07]41.5[37.09-46.08]1.0 ![0.49-2.00]0.2 !![0.05-0.92]0.3 !![0.06-1.02]0.5 ![0.19-1.13]71.3[66.84-75.44]26.4[22.36-30.91]1.2 ![0.50-2.71]0.9 !![0.33-2.62]##0.2 !![0.02-1.19]Middle14.8[12.97-16.84]48.1[45.42-50.70]15.6[13.36-18.04]7.8[6.18-9.79]7.5[5.93-9.40]6.3[5.03-7.90]14.3[11.99-16.92]51.6[48.42-54.86]15.6[13.33-18.12]8.8[7.18-10.64]3.8[2.67-5.51]5.9[4.72-7.33]17.8[15.54-20.42]50.7[47.84-53.57]14.9[12.81-17.20]5.8[4.33-7.71]7.0[5.73-8.51]3.8[2.90-4.91]30.9[27.36-34.61]52.5[48.41-56.50]8.2[6.48-10.38]3.5[2.46-5.06]2.4[1.48-3.77]2.5[1.75-3.63]High school6.5[4.54-9.18]30.8[27.59-34.24]19.1[16.47-22.10]10.4[8.54-12.69]13.2[11.19-15.58]19.9[18.09-21.86]8.2[6.28-10.53]31.5[28.66-34.47]18.2[15.57-21.06]10.5[8.51-12.86]13.2[11.35-15.27]18.5[16.19-21.10]6.7[4.88-9.19]35.6[32.28-39.06]17.1[14.52-20.01]10.1[8.63-11.88]12.5[10.36-15.10]17.9[16.19-19.77]13.9[10.91-17.61]39.9[36.30-43.70]16.5[14.23-19.06]9.1[7.55-10.96]9.5[8.00-11.26]11.0[9.34-12.93]Combined32.6[24.04-42.39]47.5[37.40-57.74]6.0[3.27-10.79]7.7 ![3.63-15.70]3.2 ![1.46-6.93]3.0 ![1.47-6.16]30.7[20.69-42.83]55.4[43.72-66.57]9.4 ![4.97-16.92]1.9 !![0.51-6.52]0.5 !![0.12-2.29]2.2 !![0.77-6.05]29.3[20.10-40.64]48.4[37.86-59.05]12.0 ![5.97-22.53]2.2 !![0.75-6.45]5.6 ![2.54-11.89]2.5 ![0.92-6.57]38.5[28.54-49.59]49.8[38.49-61.09]7.5 ![3.36-16.07]1.9 !![0.43-7.83]2.3 !![0.64-7.75]##School size categories - based on CCD frame variables (School)Less than 30052.3[46.64-57.88]41.9[36.18-47.82]3.4[2.14-5.52]2.1 ![0.85-5.06]0.1 !![0.02-1.00]0.2 !![0.02-1.13]52.0[46.69-57.35]43.4[38.07-48.80]3.1[1.72-5.62]1.0 !![0.33-2.84]0.2 !![0.02-1.38]0.3 !![0.08-1.25]56.7[49.91-63.25]37.8[31.02-45.05]4.1 ![2.11-7.77]0.6 ![0.25-1.70]0.8 !![0.12-4.98]##68.3[61.33-74.54]29.7[23.57-36.76]2.0 !![0.52-6.99]######300 - 49948.5[43.01-54.04]44.7[39.38-50.05]4.4[3.15-6.18]0.9 ![0.45-1.83]1.5[0.83-2.70]##47.8[43.06-52.50]44.1[39.25-49.04]5.7[3.59-8.90]1.5[0.96-2.29]0.9 ![0.35-2.25]0.1 !![0.01-0.63]46.6[41.45-51.88]45.7[40.75-50.70]4.8[3.48-6.54]1.6[0.91-2.67]0.6 ![0.25-1.38]0.7 ![0.31-1.80]60.1[54.73-65.21]35.8[30.42-41.47]2.2[1.26-3.70]1.4 ![0.63-3.02]0.4 ![0.14-0.97]0.2 !![0.06-0.96]500 - 99932.3[28.66-36.18]44.7[40.69-48.83]10.7[9.15-12.58]4.5[3.53-5.64]4.2[3.49-5.14]3.5[2.54-4.85]30.9[26.63-35.43]47.2[42.62-51.85]12.2[9.96-14.79]4.3[3.23-5.65]2.2[1.68-2.96]3.2[2.09-4.99]33.5[28.95-38.27]50.2[45.63-54.86]8.0[6.90-9.16]2.9[2.16-3.85]3.7[2.88-4.65]1.8[1.22-2.61]48.7[44.20-53.31]38.8[34.34-43.40]6.0[4.69-7.66]3.3[2.15-5.08]1.5[0.95-2.49]1.6[0.99-2.63]1,000 or more6.6[4.14-10.25]19.0[15.55-22.97]17.1[14.12-20.62]12.9[10.66-15.60]16.6[14.05-19.53]27.8[25.30-30.44]6.9[4.50-10.39]24.1[20.63-27.93]15.9[12.57-19.96]12.1[10.27-14.10]15.3[13.21-17.69]25.7[22.49-29.25]7.6[4.95-11.35]21.0[17.09-25.58]14.1[11.83-16.76]11.1[9.11-13.55]18.9[15.78-22.56]27.2[24.44-30.21]12.2[9.06-16.21]30.6[26.12-35.57]18.7[14.90-23.29]10.2[8.60-12.16]13.2[10.78-15.95]15.0[12.46-18.04]Level of crime where students liveHigh level of crime25.7[18.43-34.57]41.7[32.66-51.44]14.9[8.96-23.66]3.4 ![1.81-6.12]5.5[3.29-9.10]8.8[6.09-12.65]25.7[18.29-34.85]44.4[35.87-53.27]14.7[9.37-22.42]4.9 ![2.55-9.23]2.7 ![1.40-5.24]7.5[5.16-10.83]25.3[16.98-36.06]44.8[35.31-54.71]9.4[5.72-15.09]5.2 ![2.79-9.65]6.3 ![3.23-12.02]8.9[6.13-12.65]43.8[33.79-54.26]41.0[31.74-51.04]3.8 ![1.45-9.52]4.9 ![2.60-8.91]2.0 !![0.66-5.94]4.5[2.71-7.52]Moderate level of crime29.9[23.82-36.87]47.2[41.08-53.42]7.9[5.80-10.76]3.2[2.21-4.60]4.9[3.73-6.47]6.8[5.40-8.53]26.9[21.08-33.64]47.2[40.55-53.87]11.2[8.25-15.03]4.6[3.14-6.54]3.7[2.55-5.32]6.5[4.95-8.48]35.8[29.79-42.39]45.6[38.96-52.41]5.6[4.22-7.31]2.9[2.10-3.89]4.8[3.37-6.81]5.3[4.07-6.93]43.5[35.59-51.83]39.5[32.06-47.49]7.8[5.22-11.55]2.0[1.23-3.09]3.1[2.19-4.33]4.1[2.99-5.58]Low level of crime44.4[41.37-47.51]39.9[37.12-42.71]6.4[5.44-7.47]3.6[2.91-4.54]3.1[2.48-3.80]2.6[2.17-3.12]44.2[40.42-48.01]41.7[37.79-45.81]6.7[5.56-8.12]2.9[2.47-3.41]2.1[1.71-2.60]2.3[1.87-2.89]45.5[41.50-49.51]41.3[37.09-45.62]6.2[5.12-7.51]2.2[1.80-2.75]2.6[2.02-3.29]2.2[1.77-2.76]57.1[53.21-60.92]33.3[29.39-37.36]4.2[3.50-5.11]2.5[1.88-3.37]1.7[1.19-2.38]1.2[0.93-1.53]Students come from areas with very different levels of crime35.5[29.00-42.68]37.4[31.01-44.24]10.2[7.26-14.15]5.8[3.29-9.94]4.6[3.33-6.35]6.5[4.84-8.62]34.6[27.58-42.41]39.8[33.18-46.81]9.5[6.79-13.13]4.3[2.71-6.69]4.9[3.57-6.67]6.9[4.91-9.64]30.5[23.88-38.05]44.1[37.23-51.19]9.9[7.14-13.65]4.4[2.74-6.99]5.5[4.04-7.54]5.5[3.89-7.82]53.1[45.48-60.51]31.7[25.31-38.91]7.1[4.21-11.77]3.6[2.05-6.11]2.1[1.27-3.54]2.4[1.43-4.02]Urbanicity - Based on Urban-centric location of school - from CCD (School)City34.4[29.53-39.61]41.5[36.55-46.53]8.2[6.21-10.70]3.2[2.43-4.22]4.6[3.68-5.77]8.2[7.08-9.37]27.8[22.80-33.47]46.2[40.55-51.91]9.9[7.46-12.96]4.9[3.54-6.81]4.2[3.17-5.63]7.0[5.67-8.55]36.4[32.03-41.05]44.1[39.50-48.71]6.1[4.69-7.94]3.3[2.49-4.50]4.6[3.61-5.88]5.4[4.60-6.45]52.1[46.13-57.94]33.6[28.27-39.30]5.2[3.53-7.53]3.2[2.12-4.77]2.7[1.89-3.94]3.3[2.29-4.67]Suburb37.9[33.77-42.29]40.7[36.73-44.87]7.5[6.14-9.02]4.2[3.39-5.09]4.7[3.85-5.82]5.0[4.22-5.84]40.7[36.25-45.30]40.7[35.90-45.70]7.6[6.00-9.64]3.2[2.53-4.07]2.8[2.23-3.51]5.0[3.97-6.17]39.5[34.84-44.40]42.2[37.57-47.05]6.9[5.69-8.27]2.8[1.93-3.98]4.0[3.09-5.10]4.6[3.88-5.49]52.2[48.32-56.11]35.8[32.21-39.60]4.5[3.25-6.11]2.6[1.86-3.60]1.9[1.39-2.69]3.0[2.33-3.72]Town34.7[27.91-42.12]45.2[37.22-53.36]10.0[7.62-12.90]3.7[2.26-5.96]3.6[2.38-5.45]2.9[1.71-4.90]38.8[32.09-46.06]41.0[33.87-48.62]11.1[7.89-15.53]4.0[2.82-5.61]1.7[0.92-2.93]3.3[2.26-4.89]36.1[29.36-43.52]44.8[38.66-51.06]8.5[6.31-11.41]3.6[2.62-5.00]4.2[2.99-5.96]2.7[1.78-4.09]40.2[33.06-47.79]42.6[35.16-50.39]8.2[5.56-11.98]4.8[2.72-8.44]3.0 ![1.52-5.91]1.1 ![0.57-2.23]Rural45.3[40.66-50.03]40.0[34.71-45.50]7.3[5.82-9.03]3.9[2.45-6.26]2.3[1.62-3.17]1.2[0.87-1.77]43.5[39.21-47.96]42.8[38.22-47.50]7.3[5.55-9.57]2.6[1.88-3.59]2.3[1.72-2.97]1.5[1.06-2.09]45.2[40.81-49.66]41.5[36.93-46.29]6.4[4.65-8.76]2.2[1.50-3.16]2.5[1.62-3.84]2.2[1.46-3.28]59.6[54.15-64.82]32.0[27.12-37.30]5.0[3.67-6.80]1.4[0.86-2.40]1.1[0.63-1.80]0.9[0.51-1.54]# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.† Not applicable.# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END# Rounds to zero.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: INCPOL, FR_LVEL, FR_SIZE, C0560 and FR_URBAN. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: INCPOL06 (SSOCS:2006), FR_LVEL (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_SIZE (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), C0560 (SSOCS:2006, SSOCS:2008, SSOCS:2010, SSOCS:2016), FR_LOC4 (SSOCS:2006), INCPOL08 (SSOCS:2008), FR_URBAN (SSOCS:2008, SSOCS:2010, SSOCS:2016), INCPOL10 (SSOCS:2010) and INCPOL16 (SSOCS:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, 2005–06 School Survey on Crime and Safety (SSOCS), 2006, 2007–08 School Survey on Crime and Safety (SSOCS), 2008, 2009-10 School Survey on Crime and Safety (SSOCS), 2010 and 2015-16 School Survey on Crime and Safety (SSOCS), 2016.Computation by NCES TrendStats on 7/9/2018.mgbkddc9mgbkddc91Average Hours each week child receives non-relative care by Child currently has disability for years 2012 and 2016 Hours each week child receives non-relative care(Avg)EstimatesTotal201226.3201627.0Child currently has disabilityCurrently has a disability201225.2201626.1Does not currently have a disability201226.4201627.1Average Hours each week child receives non-relative care by Child currently has disability for years 2012 and 2016 Hours each week child receives non-relative care(Avg)EstimatesTotal201226.3201627.0Child currently has disabilityCurrently has a disability201225.2201626.1Does not currently have a disability201226.4201627.1Standard Error (BRR)Total20120.6020160.65Child currently has disabilityCurrently has a disability20122.2620161.95Does not currently have a disability20120.6320160.69Relative Standard Error (%)Total20122.2920162.39Child currently has disabilityCurrently has a disability20128.9720167.47Does not currently have a disability20122.3920162.54Weighted Sample Sizes (n/1,000s)Total20123,099.020162,792.5Child currently has disabilityCurrently has a disability2012251.32016268.4Does not currently have a disability20122,847.720162,524.1Average Hours each week child receives non-relative care by Child currently has disability for years 2012 and 2016 Hours each week child receives non-relative care(Avg)Amt.95% CIEstimatesTotal201226.3[25.09-27.48]201627.0[25.70-28.27]Child currently has disabilityCurrently has a disability201225.2[20.72-29.72]201626.1[22.19-29.94]Does not currently have a disability201226.4[25.12-27.63]201627.1[25.71-28.44]20122016 Hours each week child receives non-relative careHours each week child receives non-relative care (Avg)(Avg)EstimatesTotal26.327.0Child currently has disabilityCurrently has a disability25.226.1Does not currently have a disability26.427.120122016 Hours each week child receives non-relative careHours each week child receives non-relative care (Avg)(Avg)EstimatesTotal26.327.0Child currently has disabilityCurrently has a disability25.226.1Does not currently have a disability26.427.1Standard Error (BRR)Total0.600.65Child currently has disabilityCurrently has a disability2.261.95Does not currently have a disability0.630.69Relative Standard Error (%)Total2.292.39Child currently has disabilityCurrently has a disability8.977.47Does not currently have a disability2.392.54Weighted Sample Sizes (n/1,000s)Total3,099.02,792.5Child currently has disabilityCurrently has a disability251.3268.4Does not currently have a disability2,847.72,524.120122016 Hours each week child receives non-relative careHours each week child receives non-relative care (Avg)(Avg) Amt.95% CIAmt.95% CIEstimatesTotal26.3[25.09-27.48]27.0[25.70-28.27]Child currently has disabilityCurrently has a disability25.2[20.72-29.72]26.1[22.19-29.94]Does not currently have a disability26.4[25.12-27.63]27.1[25.71-28.44]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: NCHRS and DISABLTYX. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: NCHRS (ECPP:2012, ECPP:2016) and DISABLTYX (ECPP:2012, ECPP:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012 and National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016.Computation by NCES TrendStats on 10/24/2018.cembka12 cembka12 2Number of siblings with (Percent>1) by Educational attainment of child's parent or guardian for years 2012 and 2016 Number of siblings(%>1)EstimatesTotal201230.3201633.3Educational attainment of child's parent or guardianLess than high school credential201243.7201657.6High school graduate or equivalent201231.2201630.4Vocational/technical school after HS201229.5201634.6College graduate201224.4201625.6Graduate or professional school201224.7201626.8Number of siblings with (Percent>1) by Educational attainment of child's parent or guardian for years 2012 and 2016 Number of siblings(%>1)EstimatesTotal201230.3201633.3Educational attainment of child's parent or guardianLess than high school credential201243.7201657.6High school graduate or equivalent201231.2201630.4Vocational/technical school after HS201229.5201634.6College graduate201224.4201625.6Graduate or professional school201224.7201626.8Standard Error (BRR)Total20120.5520160.71Educational attainment of child's parent or guardianLess than high school credential20122.4520163.61High school graduate or equivalent20122.0720162.16Vocational/technical school after HS20121.1920161.35College graduate20121.1020161.25Graduate or professional school20121.2620161.77Relative Standard Error (%)Total20121.8220162.13Educational attainment of child's parent or guardianLess than high school credential20125.6020166.26High school graduate or equivalent20126.6220167.10Vocational/technical school after HS20124.0520163.91College graduate20124.5120164.87Graduate or professional school20125.1320166.60Weighted Sample Sizes (n/1,000s)Total201221,674.7201621,437.9Educational attainment of child's parent or guardianLess than high school credential20123,296.520162,779.0High school graduate or equivalent20124,613.520164,349.2Vocational/technical school after HS20126,215.020165,566.7College graduate20124,821.620165,790.7Graduate or professional school20122,728.120162,952.4Number of siblings with (Percent>1) by Educational attainment of child's parent or guardian for years 2012 and 2016 Number of siblings(%>1)Pct.95% CIEstimatesTotal201230.3[29.21-31.40]201633.3[31.86-34.68]Educational attainment of child's parent or guardianLess than high school credential201243.7[38.93-48.65]201657.6[50.35-64.62]High school graduate or equivalent201231.2[27.26-35.47]201630.4[26.31-34.90]Vocational/technical school after HS201229.5[27.17-31.92]201634.6[31.96-37.35]College graduate201224.4[22.31-26.70]201625.6[23.24-28.21]Graduate or professional school201224.7[22.25-27.28]201626.8[23.46-30.51]20122016 Number of siblingsNumber of siblings (%>1)(%>1)EstimatesTotal30.333.3Educational attainment of child's parent or guardianLess than high school credential43.757.6High school graduate or equivalent31.230.4Vocational/technical school after HS29.534.6College graduate24.425.6Graduate or professional school24.726.820122016 Number of siblingsNumber of siblings (%>1)(%>1)EstimatesTotal30.333.3Educational attainment of child's parent or guardianLess than high school credential43.757.6High school graduate or equivalent31.230.4Vocational/technical school after HS29.534.6College graduate24.425.6Graduate or professional school24.726.8Standard Error (BRR)Total0.550.71Educational attainment of child's parent or guardianLess than high school credential2.453.61High school graduate or equivalent2.072.16Vocational/technical school after HS1.191.35College graduate1.101.25Graduate or professional school1.261.77Relative Standard Error (%)Total1.822.13Educational attainment of child's parent or guardianLess than high school credential5.606.26High school graduate or equivalent6.627.10Vocational/technical school after HS4.053.91College graduate4.514.87Graduate or professional school5.136.60Weighted Sample Sizes (n/1,000s)Total21,674.721,437.9Educational attainment of child's parent or guardianLess than high school credential3,296.52,779.0High school graduate or equivalent4,613.54,349.2Vocational/technical school after HS6,215.05,566.7College graduate4,821.65,790.7Graduate or professional school2,728.12,952.420122016 Number of siblingsNumber of siblings (%>1)(%>1) Pct.95% CIPct.95% CIEstimatesTotal30.3[29.21-31.40]33.3[31.86-34.68]Educational attainment of child's parent or guardianLess than high school credential43.7[38.93-48.65]57.6[50.35-64.62]High school graduate or equivalent31.2[27.26-35.47]30.4[26.31-34.90]Vocational/technical school after HS29.5[27.17-31.92]34.6[31.96-37.35]College graduate24.4[22.31-26.70]25.6[23.24-28.21]Graduate or professional school24.7[22.25-27.28]26.8[23.46-30.51]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: NUMSIBSX and PAR1EDUC. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: NUMSIBSX (ECPP:2012, ECPP:2016) and PAR1EDUC (ECPP:2012, ECPP:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012 and National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016.Computation by NCES TrendStats on 10/24/2018.cembka1e cembka1e 3Average Number of household members younger than age 18 by Total household income for years 2012 and 2016 Number of household members younger than age 18(Avg)EstimatesTotal20122.320162.3Total household income$0 to $10,00020122.420162.5$10,001 to $20,00020122.520162.3$20,001 to $30,00020122.520162.4$30,001 to $40,00020122.520162.4$40,001 to $50,00020122.420162.3$50,001 to $60,00020122.120162.3$60,001 to $75,00020122.220162.3$75,001 to $100,00020122.120162.1$100,001 to $150,00020122.220162.1$150,001 or more20122.220162.1Average Number of household members younger than age 18 by Total household income for years 2012 and 2016 Number of household members younger than age 18(Avg)EstimatesTotal20122.320162.3Total household income$0 to $10,00020122.420162.5$10,001 to $20,00020122.520162.3$20,001 to $30,00020122.520162.4$30,001 to $40,00020122.520162.4$40,001 to $50,00020122.420162.3$50,001 to $60,00020122.120162.3$60,001 to $75,00020122.220162.3$75,001 to $100,00020122.120162.1$100,001 to $150,00020122.220162.1$150,001 or more20122.220162.1Standard Error (BRR)Total20120.0120160.02Total household income$0 to $10,00020120.0620160.09$10,001 to $20,00020120.0820160.09$20,001 to $30,00020120.0620160.14$30,001 to $40,00020120.0620160.10$40,001 to $50,00020120.0820160.12$50,001 to $60,00020120.0520160.08$60,001 to $75,00020120.0620160.08$75,001 to $100,00020120.0420160.07$100,001 to $150,00020120.0420160.04$150,001 or more20120.0520160.05Relative Standard Error (%)Total20120.5920160.94Total household income$0 to $10,00020122.7120163.49$10,001 to $20,00020123.0720163.68$20,001 to $30,00020122.4020165.65$30,001 to $40,00020122.4620164.33$40,001 to $50,00020123.4120165.12$50,001 to $60,00020122.4520163.46$60,001 to $75,00020122.6420163.58$75,001 to $100,00020122.0620163.16$100,001 to $150,00020121.8120161.70$150,001 or more20122.2020162.22Weighted Sample Sizes (n/1,000s)Total201221,674.7201621,437.9Total household income$0 to $10,00020121,773.520161,387.3$10,001 to $20,00020122,181.520161,676.6$20,001 to $30,00020122,225.720162,041.4$30,001 to $40,00020122,131.620161,928.0$40,001 to $50,00020121,890.020161,790.5$50,001 to $60,00020121,647.820161,637.3$60,001 to $75,00020122,233.020162,184.2$75,001 to $100,00020122,745.020162,882.0$100,001 to $150,00020122,821.820163,252.9$150,001 or more20122,024.820162,657.7Average Number of household members younger than age 18 by Total household income for years 2012 and 2016 Number of household members younger than age 18(Avg)Amt.95% CIEstimatesTotal20122.3[2.26-2.31]20162.3[2.21-2.29]Total household income$0 to $10,00020122.4[2.24-2.49]20162.5[2.36-2.71]$10,001 to $20,00020122.5[2.32-2.62]20162.3[2.16-2.50]$20,001 to $30,00020122.5[2.34-2.58]20162.4[2.17-2.72]$30,001 to $40,00020122.5[2.35-2.59]20162.4[2.18-2.59]$40,001 to $50,00020122.4[2.20-2.52]20162.3[2.07-2.54]$50,001 to $60,00020122.1[2.03-2.24]20162.3[2.10-2.42]$60,001 to $75,00020122.2[2.09-2.32]20162.3[2.09-2.41]$75,001 to $100,00020122.1[2.02-2.19]20162.1[2.00-2.27]$100,001 to $150,00020122.2[2.08-2.24]20162.1[2.02-2.16]$150,001 or more20122.2[2.08-2.27]20162.1[1.99-2.17]20122016 Number of household members younger than age 18Number of household members younger than age 18 (Avg)(Avg)EstimatesTotal2.32.3Total household income$0 to $10,0002.42.5$10,001 to $20,0002.52.3$20,001 to $30,0002.52.4$30,001 to $40,0002.52.4$40,001 to $50,0002.42.3$50,001 to $60,0002.12.3$60,001 to $75,0002.22.3$75,001 to $100,0002.12.1$100,001 to $150,0002.22.1$150,001 or more2.22.120122016 Number of household members younger than age 18Number of household members younger than age 18 (Avg)(Avg)EstimatesTotal2.32.3Total household income$0 to $10,0002.42.5$10,001 to $20,0002.52.3$20,001 to $30,0002.52.4$30,001 to $40,0002.52.4$40,001 to $50,0002.42.3$50,001 to $60,0002.12.3$60,001 to $75,0002.22.3$75,001 to $100,0002.12.1$100,001 to $150,0002.22.1$150,001 or more2.22.1Standard Error (BRR)Total0.010.02Total household income$0 to $10,0000.060.09$10,001 to $20,0000.080.09$20,001 to $30,0000.060.14$30,001 to $40,0000.060.10$40,001 to $50,0000.080.12$50,001 to $60,0000.050.08$60,001 to $75,0000.060.08$75,001 to $100,0000.040.07$100,001 to $150,0000.040.04$150,001 or more0.050.05Relative Standard Error (%)Total0.590.94Total household income$0 to $10,0002.713.49$10,001 to $20,0003.073.68$20,001 to $30,0002.405.65$30,001 to $40,0002.464.33$40,001 to $50,0003.415.12$50,001 to $60,0002.453.46$60,001 to $75,0002.643.58$75,001 to $100,0002.063.16$100,001 to $150,0001.811.70$150,001 or more2.202.22Weighted Sample Sizes (n/1,000s)Total21,674.721,437.9Total household income$0 to $10,0001,773.51,387.3$10,001 to $20,0002,181.51,676.6$20,001 to $30,0002,225.72,041.4$30,001 to $40,0002,131.61,928.0$40,001 to $50,0001,890.01,790.5$50,001 to $60,0001,647.81,637.3$60,001 to $75,0002,233.02,184.2$75,001 to $100,0002,745.02,882.0$100,001 to $150,0002,821.83,252.9$150,001 or more2,024.82,657.720122016 Number of household members younger than age 18Number of household members younger than age 18 (Avg)(Avg) Amt.95% CIAmt.95% CIEstimatesTotal2.3[2.26-2.31]2.3[2.21-2.29]Total household income$0 to $10,0002.4[2.24-2.49]2.5[2.36-2.71]$10,001 to $20,0002.5[2.32-2.62]2.3[2.16-2.50]$20,001 to $30,0002.5[2.34-2.58]2.4[2.17-2.72]$30,001 to $40,0002.5[2.35-2.59]2.4[2.18-2.59]$40,001 to $50,0002.4[2.20-2.52]2.3[2.07-2.54]$50,001 to $60,0002.1[2.03-2.24]2.3[2.10-2.42]$60,001 to $75,0002.2[2.09-2.32]2.3[2.09-2.41]$75,001 to $100,0002.1[2.02-2.19]2.1[2.00-2.27]$100,001 to $150,0002.2[2.08-2.24]2.1[2.02-2.16]$150,001 or more2.2[2.08-2.27]2.1[1.99-2.17]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: HHUNDR18X and TTLHHINC. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: HHUNDR18X (ECPP:2012, ECPP:2016) and TTLHHINC (ECPP:2012, ECPP:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012 and National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016.Computation by NCES TrendStats on 10/24/2018.cembka68 cembka68 4Average Amount household pays for program care and Average Amount household pays for care by relative by Census region where child lives for years 2012 and 2016 Amount household pays for program careAmount household pays for care by relative(Avg)(Avg)EstimatesTotal2012757.6152.020161,041.7263.7Census region where child livesNortheast20121,184.7132.520161,639.9688.7 !!South2012639.4215.0 !2016822.2243.4Midwest2012574.1108.62016933.9147.0West2012783.1127.220161,029.1227.0Average Amount household pays for program care and Average Amount household pays for care by relative by Census region where child lives for years 2012 and 2016 Amount household pays for program careAmount household pays for care by relative(Avg)(Avg)EstimatesTotal2012757.6152.020161,041.7263.7Census region where child livesNortheast20121,184.7132.520161,639.9688.7 !!South2012639.4215.0 !2016822.2243.4Midwest2012574.1108.62016933.9147.0West2012783.1127.220161,029.1227.0Standard Error (BRR)Total201241.7120.66201670.9652.99Census region where child livesNortheast2012130.6224.962016183.60485.93South201279.1564.59201685.3770.27Midwest201273.7117.252016145.2627.96West201292.3215.372016140.8032.13Relative Standard Error (%)Total20125.5113.6020166.8120.09Census region where child livesNortheast201211.0318.84201611.2070.55South201212.3830.04201610.3828.87Midwest201212.8415.89201615.5519.02West201211.7912.08201613.6814.15Weighted Sample Sizes (n/1,000s)Total20125,347.91,495.820165,724.11,292.0Census region where child livesNortheast20121,022.7240.320161,058.0133.2South20122,068.2467.620162,185.1458.4Midwest20121,194.3284.620161,280.0271.0West20121,062.7503.420161,201.1429.4Average Amount household pays for program care and Average Amount household pays for care by relative by Census region where child lives for years 2012 and 2016 Amount household pays for program careAmount household pays for care by relative(Avg)(Avg)Amt.95% CIAmt.95% CIEstimatesTotal2012757.6[674.64-840.65]152.0[110.85-193.07]20161,041.7[900.49-1,182.92]263.7[158.24-369.13]Census region where child livesNortheast20121,184.7[924.74-1,444.60]132.5[82.83-182.16]20161,639.9[1,274.53-2,005.27]688.7 !![-278.25-1,655.74]South2012639.4[481.89-796.91]215.0 ![86.45-343.53]2016822.2[652.31-992.10]243.4[103.59-383.27]Midwest2012574.1[427.44-720.80]108.6[74.22-142.90]2016933.9[644.80-1,222.93]147.0[91.39-202.67]West2012783.1[599.35-966.77]127.2[96.65-157.82]20161,029.1[748.87-1,309.24]227.0[163.08-290.97]20122016 Amount household pays for program careAmount household pays for care by relativeAmount household pays for program careAmount household pays for care by relative (Avg)(Avg)(Avg)(Avg)EstimatesTotal757.6152.01,041.7263.7Census region where child livesNortheast1,184.7132.51,639.9688.7 !!South639.4215.0 !822.2243.4Midwest574.1108.6933.9147.0West783.1127.21,029.1227.020122016 Amount household pays for program careAmount household pays for care by relativeAmount household pays for program careAmount household pays for care by relative (Avg)(Avg)(Avg)(Avg)EstimatesTotal757.6152.01,041.7263.7Census region where child livesNortheast1,184.7132.51,639.9688.7 !!South639.4215.0 !822.2243.4Midwest574.1108.6933.9147.0West783.1127.21,029.1227.0Standard Error (BRR)Total41.7120.6670.9652.99Census region where child livesNortheast130.6224.96183.60485.93South79.1564.5985.3770.27Midwest73.7117.25145.2627.96West92.3215.37140.8032.13Relative Standard Error (%)Total5.5113.606.8120.09Census region where child livesNortheast11.0318.8411.2070.55South12.3830.0410.3828.87Midwest12.8415.8915.5519.02West11.7912.0813.6814.15Weighted Sample Sizes (n/1,000s)Total5,347.91,495.85,724.11,292.0Census region where child livesNortheast1,022.7240.31,058.0133.2South2,068.2467.62,185.1458.4Midwest1,194.3284.61,280.0271.0West1,062.7503.41,201.1429.420122016 Amount household pays for program careAmount household pays for care by relativeAmount household pays for program careAmount household pays for care by relative (Avg)(Avg)(Avg)(Avg) Amt.95% CIAmt.95% CIAmt.95% CIAmt.95% CIEstimatesTotal757.6[674.64-840.65]152.0[110.85-193.07]1,041.7[900.49-1,182.92]263.7[158.24-369.13]Census region where child livesNortheast1,184.7[924.74-1,444.60]132.5[82.83-182.16]1,639.9[1,274.53-2,005.27]688.7 !![-278.25-1,655.74]South639.4[481.89-796.91]215.0 ![86.45-343.53]822.2[652.31-992.10]243.4[103.59-383.27]Midwest574.1[427.44-720.80]108.6[74.22-142.90]933.9[644.80-1,222.93]147.0[91.39-202.67]West783.1[599.35-966.77]127.2[96.65-157.82]1,029.1[748.87-1,309.24]227.0[163.08-290.97]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: CPCOST, RCCOST and CENREG. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: CPCOST (ECPP:2012, ECPP:2016), RCCOST (ECPP:2012, ECPP:2016) and CENREG (ECPP:2012, ECPP:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012 and National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016.Computation by NCES TrendStats on 10/24/2018.cembkafb cembkafb 5Median First parent or guardian age by Race and ethnicity of child for years 2012 and 2016 First parent or guardian age(Median)EstimatesTotal201232.0201633.0Race and ethnicity of childWhite, non-Hispanic201232.0201633.0Black, non-Hispanic201232.0201634.0Hispanic201231.0201632.0All other and multiple races, non-Hispanic201234.0201635.0Median First parent or guardian age by Race and ethnicity of child for years 2012 and 2016 First parent or guardian age(Median)EstimatesTotal201232.0201633.0Race and ethnicity of childWhite, non-Hispanic201232.0201633.0Black, non-Hispanic201232.0201634.0Hispanic201231.0201632.0All other and multiple races, non-Hispanic201234.0201635.0Standard Error (BRR)Total2012^2016^Race and ethnicity of childWhite, non-Hispanic2012^2016^Black, non-Hispanic2012^2016^Hispanic2012^2016^All other and multiple races, non-Hispanic2012^20160.99Relative Standard Error (%)Total2012^2016^Race and ethnicity of childWhite, non-Hispanic2012^2016^Black, non-Hispanic2012^2016^Hispanic2012^2016^All other and multiple races, non-Hispanic2012^20162.84Weighted Sample Sizes (n/1,000s)Total201221,674.7201621,437.9Race and ethnicity of childWhite, non-Hispanic201210,892.6201610,803.7Black, non-Hispanic20122,889.520162,836.6Hispanic20125,469.520165,419.8All other and multiple races, non-Hispanic20122,423.120162,377.9Median First parent or guardian age by Race and ethnicity of child for years 2012 and 2016 First parent or guardian age(Median)Amt.95% CIEstimatesTotal201232.0[32.00-32.00]201633.0[33.00-33.00]Race and ethnicity of childWhite, non-Hispanic201232.0[32.00-32.00]201633.0[33.00-33.00]Black, non-Hispanic201232.0[32.00-32.00]201634.0[34.00-34.00]Hispanic201231.0[31.00-31.00]201632.0[32.00-32.00]All other and multiple races, non-Hispanic201234.0[34.00-34.00]201635.0[33.02-36.98]20122016 First parent or guardian ageFirst parent or guardian age (Median)(Median)EstimatesTotal32.033.0Race and ethnicity of childWhite, non-Hispanic32.033.0Black, non-Hispanic32.034.0Hispanic31.032.0All other and multiple races, non-Hispanic34.035.020122016 First parent or guardian ageFirst parent or guardian age (Median)(Median)EstimatesTotal32.033.0Race and ethnicity of childWhite, non-Hispanic32.033.0Black, non-Hispanic32.034.0Hispanic31.032.0All other and multiple races, non-Hispanic34.035.0Standard Error (BRR)Total^^Race and ethnicity of childWhite, non-Hispanic^^Black, non-Hispanic^^Hispanic^^All other and multiple races, non-Hispanic^0.99Relative Standard Error (%)Total^^Race and ethnicity of childWhite, non-Hispanic^^Black, non-Hispanic^^Hispanic^^All other and multiple races, non-Hispanic^2.84Weighted Sample Sizes (n/1,000s)Total21,674.721,437.9Race and ethnicity of childWhite, non-Hispanic10,892.610,803.7Black, non-Hispanic2,889.52,836.6Hispanic5,469.55,419.8All other and multiple races, non-Hispanic2,423.12,377.920122016 First parent or guardian ageFirst parent or guardian age (Median)(Median) Amt.95% CIAmt.95% CIEstimatesTotal32.0[32.00-32.00]33.0[33.00-33.00]Race and ethnicity of childWhite, non-Hispanic32.0[32.00-32.00]33.0[33.00-33.00]Black, non-Hispanic32.0[32.00-32.00]34.0[34.00-34.00]Hispanic31.0[31.00-31.00]32.0[32.00-32.00]All other and multiple races, non-Hispanic34.0[34.00-34.00]35.0[33.02-36.98]^ Standard error of quantile, as estimated by Woodruff method, is zero. Use caution in hypothesis testing.STDERR-SOURCE-ENDCONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: P1AGE and RACEETHN. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: P1AGE (ECPP:2012, ECPP:2016) and RACEETHN (ECPP:2012, ECPP:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012 and National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016.Computation by NCES TrendStats on 10/24/2018.cembka04 cembka04 1Enrolled in language program by Language spoken by child at home for years 2012 and 2016 Enrolled in language programYesNoTotalEstimatesTotal201211.388.7100%201611.089.0100%Language spoken by child at homeChild has not started to speak2012‡‡100%2016‡‡100%English2012‡‡100%2016‡‡100%Spanish201215.984.1100%20169.190.9100%A language other than English or Spanish20129.590.5100%201621.4 !78.6100%English and Spanish equally201210.090.0100%20169.590.5100%English and another language equally20123.4 !96.6100%20167.3 !92.7100%Enrolled in language program by Language spoken by child at home for years 2012 and 2016 Enrolled in language programYesNoTotalEstimatesTotal201211.388.7100%201611.089.0100%Language spoken by child at homeChild has not started to speak2012‡‡100%2016‡‡100%English2012‡‡100%2016‡‡100%Spanish201215.984.1100%20169.190.9100%A language other than English or Spanish20129.590.5100%201621.4 !78.6100%English and Spanish equally201210.090.0100%20169.590.5100%English and another language equally20123.4 !96.6100%20167.3 !92.7100%Standard Error (BRR)Total20121.281.28 20161.961.96 Language spoken by child at homeChild has not started to speak2012‡‡ 2016‡‡ English2012‡‡ 2016‡‡ Spanish20122.462.46 20162.042.04 A language other than English or Spanish20122.612.61 201610.2310.23 English and Spanish equally20122.242.24 20162.222.22 English and another language equally20121.651.65 20162.772.77 Relative Standard Error (%)Total201211.331.44 201617.922.20 Language spoken by child at homeChild has not started to speak2012‡‡ 2016‡‡ English2012‡‡ 2016‡‡ Spanish201215.442.92 201622.522.24 A language other than English or Spanish201227.542.88 201647.7813.01 English and Spanish equally201222.322.49 201623.342.45 English and another language equally201248.071.71 201637.892.99 Weighted Sample Sizes (n/1,000s)Total20123,108.1 20163,035.4 Language spoken by child at homeChild has not started to speak2012‡ 2016‡ English2012‡ 2016‡ Spanish20121,308.2 20161,009.4 A language other than English or Spanish2012460.2 2016489.4 English and Spanish equally2012791.7 20161,091.2 English and another language equally2012548.1 2016445.4 Enrolled in language program by Language spoken by child at home for years 2012 and 2016 Enrolled in language programYesNoTotalPct.95% CIPct.95% CI EstimatesTotal201211.3[8.96-14.05]88.7[85.95-91.04]100%201611.0[7.62-15.52]89.0[84.48-92.38]100%Language spoken by child at homeChild has not started to speak2012—†—†100%2016—†—†100%English2012—†—†100%2016—†—†100%Spanish201215.9[11.61-21.43]84.1[78.57-88.39]100%20169.1[5.74-14.03]90.9[85.97-94.26]100%A language other than English or Spanish20129.5[5.40-16.06]90.5[83.94-94.60]100%201621.4 ![7.51-47.73]78.6[52.27-92.49]100%English and Spanish equally201210.0[6.37-15.43]90.0[84.57-93.63]100%20169.5[5.92-14.94]90.5[85.06-94.08]100%English and another language equally20123.4 ![1.30-8.72]96.6[91.28-98.70]100%20167.3 ![3.38-15.12]92.7[84.88-96.62]100%20122016 Enrolled in language programEnrolled in language program YesNoYesNoEstimatesTotal11.388.711.089.0Language spoken by child at homeChild has not started to speak‡‡‡‡English‡‡‡‡Spanish15.984.19.190.9A language other than English or Spanish9.590.521.478.6English and Spanish equally10.090.09.590.5English and another language equally3.496.67.392.720122016 Enrolled in language programEnrolled in language program YesNoYesNoEstimatesTotal11.388.711.089.0Language spoken by child at homeChild has not started to speak‡‡‡‡English‡‡‡‡Spanish15.984.19.190.9A language other than English or Spanish9.590.521.478.6English and Spanish equally10.090.09.590.5English and another language equally3.496.67.392.7Standard Error (BRR)Total1.281.281.961.96Language spoken by child at homeChild has not started to speak‡‡‡‡English‡‡‡‡Spanish2.462.462.042.04A language other than English or Spanish2.612.6110.2310.23English and Spanish equally2.242.242.222.22English and another language equally1.651.652.772.77Relative Standard Error (%)Total11.331.4417.922.20Language spoken by child at homeChild has not started to speak‡‡‡‡English‡‡‡‡Spanish15.442.9222.522.24A language other than English or Spanish27.542.8847.7813.01English and Spanish equally22.322.4923.342.45English and another language equally48.071.7137.892.99Weighted Sample Sizes (n/1,000s)Total3,108.1 3,035.4 Language spoken by child at homeChild has not started to speak‡ ‡ English‡ ‡ Spanish1,308.2 1,009.4 A language other than English or Spanish460.2 489.4 English and Spanish equally791.7 1,091.2 English and another language equally548.1 445.4 20122016 Enrolled in language programEnrolled in language program YesNoYesNo Pct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal11.3[8.96-14.05]88.7[85.95-91.04]11.0[7.62-15.52]89.0[84.48-92.38]Language spoken by child at homeChild has not started to speak—†—†—†—†English—†—†—†—†Spanish15.9[11.61-21.43]84.1[78.57-88.39]9.1[5.74-14.03]90.9[85.97-94.26]A language other than English or Spanish9.5[5.40-16.06]90.5[83.94-94.60]21.4 ![7.51-47.73]78.6[52.27-92.49]English and Spanish equally10.0[6.37-15.43]90.0[84.57-93.63]9.5[5.92-14.94]90.5[85.06-94.08]English and another language equally3.4 ![1.30-8.72]96.6[91.28-98.70]7.3 ![3.38-15.12]92.7[84.88-96.62]— Not available.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met.— Not available.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met.STDERR-SOURCE-END— Not available.† Not applicable.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: CENGLPRG and CSPEAKX. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: CENGLPRG (ECPP:2012, ECPP:2016) and CSPEAKX (ECPP:2012, ECPP:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012 and National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016.Computation by NCES TrendStats on 10/24/2018.cembkahc9 cembkahc9 2Language spoken most often at home by first parent or guardian by Work status of child's first parent or guardian and Work status of child's second parent or guardian for years 2012 and 2016 Language spoken most often at home by first parent or guardianEnglishSpanishA language other than English or SpanishEnglish and Spanish equallyEnglish and another language equallyTotalEstimatesTotal201222.438.214.913.611.0100%201624.035.913.913.812.4100%Work status of child's first parent or guardianWorking 35 hours or more per week201228.129.718.911.711.6100%201627.728.416.612.115.1100%Working less than 35 hours per week201220.037.211.417.813.6100%201625.540.24.5 !21.18.7100%Looking for work20128.853.17.8 !22.18.3 !100%201644.232.210.9 !10.2 !!2.4 !!100%Not in the labor force201218.946.212.512.59.9100%201616.046.113.314.110.5100%Work status of child's second parent or guardianWorking 35 hours or more per week201226.035.214.413.211.2100%201626.839.012.712.49.1100%Working less than 35 hours per week201213.442.311.215.617.5100%201620.336.37.2 !9.5 !26.7 !100%Looking for work201224.041.112.4 !15.3 !7.2 !100%201622.5 !!34.1 !27.5 !11.8 !!4.1 !!100%Not in the labor force201215.838.022.611.212.4100%201616.429.524.412.717.1100%Language spoken most often at home by first parent or guardian by Work status of child's first parent or guardian and Work status of child's second parent or guardian for years 2012 and 2016 Language spoken most often at home by first parent or guardianEnglishSpanishA language other than English or SpanishEnglish and Spanish equallyEnglish and another language equallyTotalEstimatesTotal201222.438.214.913.611.0100%201624.035.913.913.812.4100%Work status of child's first parent or guardianWorking 35 hours or more per week201228.129.718.911.711.6100%201627.728.416.612.115.1100%Working less than 35 hours per week201220.037.211.417.813.6100%201625.540.24.5 !21.18.7100%Looking for work20128.853.17.8 !22.18.3 !100%201644.232.210.9 !10.2 !!2.4 !!100%Not in the labor force201218.946.212.512.59.9100%201616.046.113.314.110.5100%Work status of child's second parent or guardianWorking 35 hours or more per week201226.035.214.413.211.2100%201626.839.012.712.49.1100%Working less than 35 hours per week201213.442.311.215.617.5100%201620.336.37.2 !9.5 !26.7 !100%Looking for work201224.041.112.4 !15.3 !7.2 !100%201622.5 !!34.1 !27.5 !11.8 !!4.1 !!100%Not in the labor force201215.838.022.611.212.4100%201616.429.524.412.717.1100%Standard Error (BRR)Total20121.161.351.061.060.94 20161.652.011.191.301.19 Work status of child's first parent or guardianWorking 35 hours or more per week20122.022.161.891.191.05 20162.062.422.261.841.66 Working less than 35 hours per week20122.824.172.802.943.59 20164.385.941.454.572.42 Looking for work20122.475.712.774.843.67 20169.828.214.946.131.59 Not in the labor force20122.082.801.832.181.55 20162.664.322.462.642.76 Work status of child's second parent or guardianWorking 35 hours or more per week20121.522.101.381.491.34 20162.222.671.531.761.67 Working less than 35 hours per week20122.925.912.974.005.03 20165.0810.042.953.528.68 Looking for work20125.987.544.945.173.47 201613.5011.2113.055.983.05 Not in the labor force20122.643.653.232.591.71 20163.335.534.032.663.33 Relative Standard Error (%)Total20125.203.547.147.778.49 20166.865.598.539.439.62 Work status of child's first parent or guardianWorking 35 hours or more per week20127.177.309.9810.199.00 20167.438.5413.5915.1310.95 Working less than 35 hours per week201214.1211.2224.5616.4626.30 201617.1914.7731.8421.6827.82 Looking for work201227.9910.7535.5321.9344.47 201622.2125.4645.4160.0165.46 Not in the labor force201211.006.0514.6117.4715.70 201616.699.3718.4718.6926.33 Work status of child's second parent or guardianWorking 35 hours or more per week20125.855.989.5711.2311.93 20168.296.8312.0714.1918.34 Working less than 35 hours per week201221.7413.9626.4725.6828.82 201625.0327.6440.8137.1332.52 Looking for work201224.8918.3339.9533.9148.01 201660.0932.8347.4650.4575.18 Not in the labor force201216.719.5914.3123.1913.77 201620.2618.7716.5521.0319.47 Weighted Sample Sizes (n/1,000s)Total20125,331.6 20165,362.2 Work status of child's first parent or guardianWorking 35 hours or more per week20122,405.8 20162,725.5 Working less than 35 hours per week2012683.4 2016619.4 Looking for work2012447.6 2016181.3 Not in the labor force20121,794.8 20161,836.0 Work status of child's second parent or guardianWorking 35 hours or more per week20122,676.0 20162,987.4 Working less than 35 hours per week2012451.3 2016421.8 Looking for work2012298.4 2016119.5 Not in the labor force2012819.1 2016992.7 Language spoken most often at home by first parent or guardian by Work status of child's first parent or guardian and Work status of child's second parent or guardian for years 2012 and 2016 Language spoken most often at home by first parent or guardianEnglishSpanishA language other than English or SpanishEnglish and Spanish equallyEnglish and another language equallyTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal201222.4[20.13-24.75]38.2[35.51-40.89]14.9[12.88-17.11]13.6[11.63-15.85]11.0[9.29-13.02]100%201624.0[20.88-27.43]35.9[32.05-40.03]13.9[11.71-16.44]13.8[11.39-16.57]12.4[10.19-14.94]100%Work status of child's first parent or guardianWorking 35 hours or more per week201228.1[24.30-32.31]29.7[25.53-34.13]18.9[15.45-22.98]11.7[9.49-14.24]11.6[9.70-13.88]100%201627.7[23.83-32.02]28.4[23.82-33.45]16.6[12.59-21.60]12.1[8.92-16.28]15.1[12.12-18.73]100%Working less than 35 hours per week201220.0[14.96-26.21]37.2[29.29-45.75]11.4[6.89-18.23]17.8[12.72-24.44]13.6[7.93-22.44]100%201625.5[17.78-35.13]40.2[29.13-52.34]4.5 ![2.39-8.46]21.1[13.38-31.55]8.7[4.95-14.90]100%Looking for work20128.8[4.99-15.12]53.1[41.76-64.08]7.8 ![3.78-15.39]22.1[13.91-33.12]8.3 ![3.32-19.12]100%201644.2[26.42-63.67]32.2[18.38-50.12]10.9 ![4.24-25.17]10.2 !![2.92-30.08]2.4 !![0.65-8.66]100%Not in the labor force201218.9[15.08-23.36]46.2[40.74-51.82]12.5[9.31-16.63]12.5[8.76-17.52]9.9[7.19-13.43]100%201616.0[11.34-21.99]46.1[37.71-54.72]13.3[9.15-19.05]14.1[9.63-20.22]10.5[6.12-17.37]100%Work status of child's second parent or guardianWorking 35 hours or more per week201226.0[23.06-29.10]35.2[31.12-39.47]14.4[11.89-17.41]13.2[10.54-16.47]11.2[8.80-14.14]100%201626.8[22.62-31.45]39.0[33.87-44.44]12.7[9.91-16.02]12.4[9.31-16.35]9.1[6.28-13.02]100%Working less than 35 hours per week201213.4[8.60-20.35]42.3[31.20-54.31]11.2[6.52-18.60]15.6[9.14-25.24]17.5[9.56-29.78]100%201620.3[11.99-32.24]36.3[19.38-57.48]7.2 ![3.15-15.77]9.5 ![4.43-19.16]26.7 ![13.08-46.80]100%Looking for work201224.0[14.14-37.76]41.1[27.32-56.48]12.4 ![5.39-25.90]15.3 ![7.51-28.54]7.2 ![2.71-17.93]100%201622.5 !![5.84-57.54]34.1 ![16.13-58.30]27.5 ![9.34-58.24]11.8 !![4.12-29.56]4.1 !![0.88-16.73]100%Not in the labor force201215.8[11.21-21.76]38.0[31.09-45.51]22.6[16.81-29.65]11.2[6.97-17.47]12.4[9.39-16.23]100%201616.4[10.82-24.14]29.5[19.74-41.49]24.4[17.24-33.23]12.7[8.24-18.96]17.1[11.45-24.78]100%20122016 Language spoken most often at home by first parent or guardianLanguage spoken most often at home by first parent or guardian EnglishSpanishA language other than English or SpanishEnglish and Spanish equallyEnglish and another language equallyEnglishSpanishA language other than English or SpanishEnglish and Spanish equallyEnglish and another language equallyEstimatesTotal22.438.214.913.611.024.035.913.913.812.4Work status of child's first parent or guardianWorking 35 hours or more per week28.129.718.911.711.627.728.416.612.115.1Working less than 35 hours per week20.037.211.417.813.625.540.24.521.18.7Looking for work8.853.17.822.18.344.232.210.910.22.4Not in the labor force18.946.212.512.59.916.046.113.314.110.5Work status of child's second parent or guardianWorking 35 hours or more per week26.035.214.413.211.226.839.012.712.49.1Working less than 35 hours per week13.442.311.215.617.520.336.37.29.526.7Looking for work24.041.112.415.37.222.534.127.511.84.1Not in the labor force15.838.022.611.212.416.429.524.412.717.120122016 Language spoken most often at home by first parent or guardianLanguage spoken most often at home by first parent or guardian EnglishSpanishA language other than English or SpanishEnglish and Spanish equallyEnglish and another language equallyEnglishSpanishA language other than English or SpanishEnglish and Spanish equallyEnglish and another language equallyEstimatesTotal22.438.214.913.611.024.035.913.913.812.4Work status of child's first parent or guardianWorking 35 hours or more per week28.129.718.911.711.627.728.416.612.115.1Working less than 35 hours per week20.037.211.417.813.625.540.24.521.18.7Looking for work8.853.17.822.18.344.232.210.910.22.4Not in the labor force18.946.212.512.59.916.046.113.314.110.5Work status of child's second parent or guardianWorking 35 hours or more per week26.035.214.413.211.226.839.012.712.49.1Working less than 35 hours per week13.442.311.215.617.520.336.37.29.526.7Looking for work24.041.112.415.37.222.534.127.511.84.1Not in the labor force15.838.022.611.212.416.429.524.412.717.1Standard Error (BRR)Total1.161.351.061.060.941.652.011.191.301.19Work status of child's first parent or guardianWorking 35 hours or more per week2.022.161.891.191.052.062.422.261.841.66Working less than 35 hours per week2.824.172.802.943.594.385.941.454.572.42Looking for work2.475.712.774.843.679.828.214.946.131.59Not in the labor force2.082.801.832.181.552.664.322.462.642.76Work status of child's second parent or guardianWorking 35 hours or more per week1.522.101.381.491.342.222.671.531.761.67Working less than 35 hours per week2.925.912.974.005.035.0810.042.953.528.68Looking for work5.987.544.945.173.4713.5011.2113.055.983.05Not in the labor force2.643.653.232.591.713.335.534.032.663.33Relative Standard Error (%)Total5.203.547.147.778.496.865.598.539.439.62Work status of child's first parent or guardianWorking 35 hours or more per week7.177.309.9810.199.007.438.5413.5915.1310.95Working less than 35 hours per week14.1211.2224.5616.4626.3017.1914.7731.8421.6827.82Looking for work27.9910.7535.5321.9344.4722.2125.4645.4160.0165.46Not in the labor force11.006.0514.6117.4715.7016.699.3718.4718.6926.33Work status of child's second parent or guardianWorking 35 hours or more per week5.855.989.5711.2311.938.296.8312.0714.1918.34Working less than 35 hours per week21.7413.9626.4725.6828.8225.0327.6440.8137.1332.52Looking for work24.8918.3339.9533.9148.0160.0932.8347.4650.4575.18Not in the labor force16.719.5914.3123.1913.7720.2618.7716.5521.0319.47Weighted Sample Sizes (n/1,000s)Total5,331.6 5,362.2 Work status of child's first parent or guardianWorking 35 hours or more per week2,405.8 2,725.5 Working less than 35 hours per week683.4 619.4 Looking for work447.6 181.3 Not in the labor force1,794.8 1,836.0 Work status of child's second parent or guardianWorking 35 hours or more per week2,676.0 2,987.4 Working less than 35 hours per week451.3 421.8 Looking for work298.4 119.5 Not in the labor force819.1 992.7 20122016 Language spoken most often at home by first parent or guardianLanguage spoken most often at home by first parent or guardian EnglishSpanishA language other than English or SpanishEnglish and Spanish equallyEnglish and another language equallyEnglishSpanishA language other than English or SpanishEnglish and Spanish equallyEnglish and another language equally Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal22.4[20.13-24.75]38.2[35.51-40.89]14.9[12.88-17.11]13.6[11.63-15.85]11.0[9.29-13.02]24.0[20.88-27.43]35.9[32.05-40.03]13.9[11.71-16.44]13.8[11.39-16.57]12.4[10.19-14.94]Work status of child's first parent or guardianWorking 35 hours or more per week28.1[24.30-32.31]29.7[25.53-34.13]18.9[15.45-22.98]11.7[9.49-14.24]11.6[9.70-13.88]27.7[23.83-32.02]28.4[23.82-33.45]16.6[12.59-21.60]12.1[8.92-16.28]15.1[12.12-18.73]Working less than 35 hours per week20.0[14.96-26.21]37.2[29.29-45.75]11.4[6.89-18.23]17.8[12.72-24.44]13.6[7.93-22.44]25.5[17.78-35.13]40.2[29.13-52.34]4.5 ![2.39-8.46]21.1[13.38-31.55]8.7[4.95-14.90]Looking for work8.8[4.99-15.12]53.1[41.76-64.08]7.8 ![3.78-15.39]22.1[13.91-33.12]8.3 ![3.32-19.12]44.2[26.42-63.67]32.2[18.38-50.12]10.9 ![4.24-25.17]10.2 !![2.92-30.08]2.4 !![0.65-8.66]Not in the labor force18.9[15.08-23.36]46.2[40.74-51.82]12.5[9.31-16.63]12.5[8.76-17.52]9.9[7.19-13.43]16.0[11.34-21.99]46.1[37.71-54.72]13.3[9.15-19.05]14.1[9.63-20.22]10.5[6.12-17.37]Work status of child's second parent or guardianWorking 35 hours or more per week26.0[23.06-29.10]35.2[31.12-39.47]14.4[11.89-17.41]13.2[10.54-16.47]11.2[8.80-14.14]26.8[22.62-31.45]39.0[33.87-44.44]12.7[9.91-16.02]12.4[9.31-16.35]9.1[6.28-13.02]Working less than 35 hours per week13.4[8.60-20.35]42.3[31.20-54.31]11.2[6.52-18.60]15.6[9.14-25.24]17.5[9.56-29.78]20.3[11.99-32.24]36.3[19.38-57.48]7.2 ![3.15-15.77]9.5 ![4.43-19.16]26.7 ![13.08-46.80]Looking for work24.0[14.14-37.76]41.1[27.32-56.48]12.4 ![5.39-25.90]15.3 ![7.51-28.54]7.2 ![2.71-17.93]22.5 !![5.84-57.54]34.1 ![16.13-58.30]27.5 ![9.34-58.24]11.8 !![4.12-29.56]4.1 !![0.88-16.73]Not in the labor force15.8[11.21-21.76]38.0[31.09-45.51]22.6[16.81-29.65]11.2[6.97-17.47]12.4[9.39-16.23]16.4[10.82-24.14]29.5[19.74-41.49]24.4[17.24-33.23]12.7[8.24-18.96]17.1[11.45-24.78]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: P1SPEAK, PAR1EMPL and PAR2EMPL. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: P1SPEAK (ECPP:2012, ECPP:2016), PAR1EMPL (ECPP:2012, ECPP:2016) and PAR2EMPL (ECPP:2012, ECPP:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012 and National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016.Computation by NCES TrendStats on 10/24/2018.cembkaf6 cembkaf6 3Employer pays for program care by Program type for years 2012 and 2016 Employer pays for program careYesNoTotalEstimatesTotal20121.798.3100%20162.797.3100%Program typeDay Care20122.297.8100%20163.0 !97.0100%Preschool20121.498.6100%20162.697.4100%Prekindergarten20121.2 !98.8100%20162.5 !97.5100%Employer pays for program care by Program type for years 2012 and 2016 Employer pays for program careYesNoTotalEstimatesTotal20121.798.3100%20162.797.3100%Program typeDay Care20122.297.8100%20163.0 !97.0100%Preschool20121.498.6100%20162.697.4100%Prekindergarten20121.2 !98.8100%20162.5 !97.5100%Standard Error (BRR)Total20120.290.29 20160.480.48 Program typeDay Care20120.500.50 20160.920.92 Preschool20120.380.38 20160.680.68 Prekindergarten20120.510.51 20161.111.11 Relative Standard Error (%)Total201217.000.29 201617.640.50 Program typeDay Care201222.700.51 201631.210.95 Preschool201226.910.39 201625.990.70 Prekindergarten201242.600.52 201645.261.14 Weighted Sample Sizes (n/1,000s)Total20125,347.9 20165,724.1 Program typeDay Care20122,102.9 20162,269.3 Preschool20122,494.6 20162,550.2 Prekindergarten2012750.3 2016904.6 Employer pays for program care by Program type for years 2012 and 2016 Employer pays for program careYesNoTotalPct.95% CIPct.95% CI EstimatesTotal20121.7[1.21-2.38]98.3[97.62-98.79]100%20162.7[1.92-3.87]97.3[96.13-98.08]100%Program typeDay Care20122.2[1.40-3.44]97.8[96.56-98.60]100%20163.0 ![1.58-5.46]97.0[94.54-98.42]100%Preschool20121.4[0.83-2.42]98.6[97.58-99.17]100%20162.6[1.56-4.39]97.4[95.61-98.44]100%Prekindergarten20121.2 ![0.51-2.79]98.8[97.21-99.49]100%20162.5 ![0.99-5.95]97.5[94.05-99.01]100%20122016 Employer pays for program careEmployer pays for program care YesNoYesNoEstimatesTotal1.798.32.797.3Program typeDay Care2.297.83.097.0Preschool1.498.62.697.4Prekindergarten1.298.82.597.520122016 Employer pays for program careEmployer pays for program care YesNoYesNoEstimatesTotal1.798.32.797.3Program typeDay Care2.297.83.097.0Preschool1.498.62.697.4Prekindergarten1.298.82.597.5Standard Error (BRR)Total0.290.290.480.48Program typeDay Care0.500.500.920.92Preschool0.380.380.680.68Prekindergarten0.510.511.111.11Relative Standard Error (%)Total17.000.2917.640.50Program typeDay Care22.700.5131.210.95Preschool26.910.3925.990.70Prekindergarten42.600.5245.261.14Weighted Sample Sizes (n/1,000s)Total5,347.9 5,724.1 Program typeDay Care2,102.9 2,269.3 Preschool2,494.6 2,550.2 Prekindergarten750.3 904.6 20122016 Employer pays for program careEmployer pays for program care YesNoYesNo Pct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal1.7[1.21-2.38]98.3[97.62-98.79]2.7[1.92-3.87]97.3[96.13-98.08]Program typeDay Care2.2[1.40-3.44]97.8[96.56-98.60]3.0 ![1.58-5.46]97.0[94.54-98.42]Preschool1.4[0.83-2.42]98.6[97.58-99.17]2.6[1.56-4.39]97.4[95.61-98.44]Prekindergarten1.2 ![0.51-2.79]98.8[97.21-99.49]2.5 ![0.99-5.95]97.5[94.05-99.01]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: CPEMPL and CPTYPE. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: CPEMPL (ECPP:2012, ECPP:2016) and CPTYPE (ECPP:2012, ECPP:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012 and National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016.Computation by NCES TrendStats on 10/24/2018.cembka9c cembka9c 4Reason for wanting program by Zip code classification by community type for years 2012 and 2016 Reason for wanting programTo provide care when a parent was at work or schoolTo prepare child for schoolTo provide cultural or language learningTo make time for running errands or relaxingSome other reasonDid not have care in the past yearTotalEstimatesTotal201237.821.71.41.82.235.2100%201639.821.32.21.32.433.0100%Zip code classification by community typeCity201238.522.62.01.91.933.0100%201641.819.22.11.72.632.6100%Suburb201238.321.91.31.52.035.0100%201639.322.92.81.22.131.7100%Town201236.022.51.5 !1.1 !2.7 !36.2100%201637.322.12.1 !!1.1 !2.1 !35.3100%Rural201236.619.40.72.42.438.6100%201638.420.70.8 !1.2 !3.135.9100%Reason for wanting program by Zip code classification by community type for years 2012 and 2016 Reason for wanting programTo provide care when a parent was at work or schoolTo prepare child for schoolTo provide cultural or language learningTo make time for running errands or relaxingSome other reasonDid not have care in the past yearTotalEstimatesTotal201237.821.71.41.82.235.2100%201639.821.32.21.32.433.0100%Zip code classification by community typeCity201238.522.62.01.91.933.0100%201641.819.22.11.72.632.6100%Suburb201238.321.91.31.52.035.0100%201639.322.92.81.22.131.7100%Town201236.022.51.5 !1.1 !2.7 !36.2100%201637.322.12.1 !!1.1 !2.1 !35.3100%Rural201236.619.40.72.42.438.6100%201638.420.70.8 !1.2 !3.135.9100%Standard Error (BRR)Total20120.660.560.160.220.220.75 20160.890.640.330.210.300.80 Zip code classification by community typeCity20121.161.030.340.370.311.29 20161.541.040.330.390.621.53 Suburb20121.130.920.280.320.341.13 20161.261.150.730.280.351.29 Town20122.161.630.500.420.862.39 20162.892.581.350.411.003.05 Rural20121.391.280.170.620.571.64 20161.972.110.290.470.752.25 Relative Standard Error (%)Total20121.752.5711.0512.3510.102.14 20162.233.0215.1415.9312.312.42 Zip code classification by community typeCity20123.014.5716.6419.5515.883.91 20163.695.3915.8223.2323.474.69 Suburb20122.944.2022.0521.1116.783.22 20163.225.0026.0123.8116.854.08 Town20125.997.2433.0538.7132.126.59 20167.7511.7064.8737.7047.648.63 Rural20123.806.6025.6826.0123.334.26 20165.1310.2137.8538.3624.326.28 Weighted Sample Sizes (n/1,000s)Total201221,674.7 201621,437.9 Zip code classification by community typeCity20127,164.8 20167,247.9 Suburb20127,681.9 20168,749.4 Town20122,208.5 20161,932.9 Rural20124,619.5 20163,507.8 Reason for wanting program by Zip code classification by community type for years 2012 and 2016 Reason for wanting programTo provide care when a parent was at work or schoolTo prepare child for schoolTo provide cultural or language learningTo make time for running errands or relaxingSome other reasonDid not have care in the past yearTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal201237.8[36.47-39.10]21.7[20.57-22.79]1.4[1.13-1.76]1.8[1.39-2.27]2.2[1.76-2.63]35.2[33.74-36.73]100%201639.8[38.05-41.58]21.3[20.00-22.56]2.2[1.60-2.91]1.3[0.97-1.83]2.4[1.90-3.11]33.0[31.45-34.63]100%Zip code classification by community typeCity201238.5[36.23-40.83]22.6[20.64-24.76]2.0[1.45-2.81]1.9[1.27-2.76]1.9[1.41-2.65]33.0[30.52-35.66]100%201641.8[38.78-44.91]19.2[17.27-21.40]2.1[1.51-2.83]1.7[1.04-2.63]2.6[1.64-4.17]32.6[29.62-35.70]100%Suburb201238.3[36.08-40.57]21.9[20.11-23.77]1.3[0.81-1.95]1.5[1.00-2.32]2.0[1.47-2.86]35.0[32.76-37.25]100%201639.3[36.78-41.81]22.9[20.74-25.31]2.8[1.67-4.68]1.2[0.73-1.87]2.1[1.50-2.92]31.7[29.20-34.35]100%Town201236.0[31.83-40.39]22.5[19.44-25.93]1.5 ![0.78-2.92]1.1 ![0.51-2.36]2.7 ![1.40-5.01]36.2[31.59-41.07]100%201637.3[31.75-43.21]22.1[17.37-27.66]2.1 !![0.57-7.36]1.1 ![0.51-2.28]2.1 ![0.81-5.35]35.3[29.53-41.62]100%Rural201236.6[33.88-39.42]19.4[16.94-22.03]0.7[0.41-1.13]2.4[1.41-3.95]2.4[1.52-3.85]38.6[35.35-41.87]100%201638.4[34.54-42.36]20.7[16.80-25.22]0.8 ![0.36-1.62]1.2 ![0.57-2.61]3.1[1.89-4.96]35.9[31.53-40.46]100%20122016 Reason for wanting programReason for wanting program To provide care when a parent was at work or schoolTo prepare child for schoolTo provide cultural or language learningTo make time for running errands or relaxingSome other reasonDid not have care in the past yearTo provide care when a parent was at work or schoolTo prepare child for schoolTo provide cultural or language learningTo make time for running errands or relaxingSome other reasonDid not have care in the past yearEstimatesTotal37.821.71.41.82.235.239.821.32.21.32.433.0Zip code classification by community typeCity38.522.62.01.91.933.041.819.22.11.72.632.6Suburb38.321.91.31.52.035.039.322.92.81.22.131.7Town36.022.51.51.12.736.237.322.12.11.12.135.3Rural36.619.40.72.42.438.638.420.70.81.23.135.920122016 Reason for wanting programReason for wanting program To provide care when a parent was at work or schoolTo prepare child for schoolTo provide cultural or language learningTo make time for running errands or relaxingSome other reasonDid not have care in the past yearTo provide care when a parent was at work or schoolTo prepare child for schoolTo provide cultural or language learningTo make time for running errands or relaxingSome other reasonDid not have care in the past yearEstimatesTotal37.821.71.41.82.235.239.821.32.21.32.433.0Zip code classification by community typeCity38.522.62.01.91.933.041.819.22.11.72.632.6Suburb38.321.91.31.52.035.039.322.92.81.22.131.7Town36.022.51.51.12.736.237.322.12.11.12.135.3Rural36.619.40.72.42.438.638.420.70.81.23.135.9Standard Error (BRR)Total0.660.560.160.220.220.750.890.640.330.210.300.80Zip code classification by community typeCity1.161.030.340.370.311.291.541.040.330.390.621.53Suburb1.130.920.280.320.341.131.261.150.730.280.351.29Town2.161.630.500.420.862.392.892.581.350.411.003.05Rural1.391.280.170.620.571.641.972.110.290.470.752.25Relative Standard Error (%)Total1.752.5711.0512.3510.102.142.233.0215.1415.9312.312.42Zip code classification by community typeCity3.014.5716.6419.5515.883.913.695.3915.8223.2323.474.69Suburb2.944.2022.0521.1116.783.223.225.0026.0123.8116.854.08Town5.997.2433.0538.7132.126.597.7511.7064.8737.7047.648.63Rural3.806.6025.6826.0123.334.265.1310.2137.8538.3624.326.28Weighted Sample Sizes (n/1,000s)Total21,674.7 21,437.9 Zip code classification by community typeCity7,164.8 7,247.9 Suburb7,681.9 8,749.4 Town2,208.5 1,932.9 Rural4,619.5 3,507.8 20122016 Reason for wanting programReason for wanting program To provide care when a parent was at work or schoolTo prepare child for schoolTo provide cultural or language learningTo make time for running errands or relaxingSome other reasonDid not have care in the past yearTo provide care when a parent was at work or schoolTo prepare child for schoolTo provide cultural or language learningTo make time for running errands or relaxingSome other reasonDid not have care in the past year Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal37.8[36.47-39.10]21.7[20.57-22.79]1.4[1.13-1.76]1.8[1.39-2.27]2.2[1.76-2.63]35.2[33.74-36.73]39.8[38.05-41.58]21.3[20.00-22.56]2.2[1.60-2.91]1.3[0.97-1.83]2.4[1.90-3.11]33.0[31.45-34.63]Zip code classification by community typeCity38.5[36.23-40.83]22.6[20.64-24.76]2.0[1.45-2.81]1.9[1.27-2.76]1.9[1.41-2.65]33.0[30.52-35.66]41.8[38.78-44.91]19.2[17.27-21.40]2.1[1.51-2.83]1.7[1.04-2.63]2.6[1.64-4.17]32.6[29.62-35.70]Suburb38.3[36.08-40.57]21.9[20.11-23.77]1.3[0.81-1.95]1.5[1.00-2.32]2.0[1.47-2.86]35.0[32.76-37.25]39.3[36.78-41.81]22.9[20.74-25.31]2.8[1.67-4.68]1.2[0.73-1.87]2.1[1.50-2.92]31.7[29.20-34.35]Town36.0[31.83-40.39]22.5[19.44-25.93]1.5 ![0.78-2.92]1.1 ![0.51-2.36]2.7 ![1.40-5.01]36.2[31.59-41.07]37.3[31.75-43.21]22.1[17.37-27.66]2.1 !![0.57-7.36]1.1 ![0.51-2.28]2.1 ![0.81-5.35]35.3[29.53-41.62]Rural36.6[33.88-39.42]19.4[16.94-22.03]0.7[0.41-1.13]2.4[1.41-3.95]2.4[1.52-3.85]38.6[35.35-41.87]38.4[34.54-42.36]20.7[16.80-25.22]0.8 ![0.36-1.62]1.2 ![0.57-2.61]3.1[1.89-4.96]35.9[31.53-40.46]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: MAINRESN and ZIPLOCL. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: MAINRESN (ECPP:2012, ECPP:2016) and ZIPLOCL (ECPP:2012, ECPP:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012 and National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016.Computation by NCES TrendStats on 10/24/2018.cembka6b cembka6b 5Child has nonparental care at least once a week by Educational attainment of child's parent or guardian for years 2012 and 2016 Child has nonparental care at least once a weekCurrently participates in any care or program arrangement that occurs at least once each weekDoes not currently participate at least once each weekTotalEstimatesTotal201260.439.6100%201659.540.5100%Educational attainment of child's parent or guardianLess than high school credential201244.056.0100%201640.859.2100%High school graduate or equivalent201254.445.6100%201650.449.6100%Vocational/technical school after HS201260.339.7100%201660.739.3100%College graduate201269.430.6100%201665.834.2100%Graduate or professional school201274.725.3100%201676.024.0100%Child has nonparental care at least once a week by Educational attainment of child's parent or guardian for years 2012 and 2016 Child has nonparental care at least once a weekCurrently participates in any care or program arrangement that occurs at least once each weekDoes not currently participate at least once each weekTotalEstimatesTotal201260.439.6100%201659.540.5100%Educational attainment of child's parent or guardianLess than high school credential201244.056.0100%201640.859.2100%High school graduate or equivalent201254.445.6100%201650.449.6100%Vocational/technical school after HS201260.339.7100%201660.739.3100%College graduate201269.430.6100%201665.834.2100%Graduate or professional school201274.725.3100%201676.024.0100%Standard Error (BRR)Total20120.780.78 20160.960.96 Educational attainment of child's parent or guardianLess than high school credential20122.442.44 20164.214.21 High school graduate or equivalent20122.062.06 20162.492.49 Vocational/technical school after HS20121.291.29 20161.341.34 College graduate20121.411.41 20161.491.49 Graduate or professional school20121.441.44 20161.571.57 Relative Standard Error (%)Total20121.291.97 20161.612.37 Educational attainment of child's parent or guardianLess than high school credential20125.544.36 201610.327.11 High school graduate or equivalent20123.794.51 20164.945.01 Vocational/technical school after HS20122.143.26 20162.213.41 College graduate20122.034.60 20162.264.35 Graduate or professional school20121.935.68 20162.076.54 Weighted Sample Sizes (n/1,000s)Total201221,674.7 201621,437.9 Educational attainment of child's parent or guardianLess than high school credential20123,296.5 20162,779.0 High school graduate or equivalent20124,613.5 20164,349.2 Vocational/technical school after HS20126,215.0 20165,566.7 College graduate20124,821.6 20165,790.7 Graduate or professional school20122,728.1 20162,952.4 Child has nonparental care at least once a week by Educational attainment of child's parent or guardian for years 2012 and 2016 Child has nonparental care at least once a weekCurrently participates in any care or program arrangement that occurs at least once each weekDoes not currently participate at least once each weekTotalPct.95% CIPct.95% CI EstimatesTotal201260.4[58.83-61.93]39.6[38.07-41.17]100%201659.5[57.58-61.40]40.5[38.60-42.42]100%Educational attainment of child's parent or guardianLess than high school credential201244.0[39.23-48.91]56.0[51.09-60.77]100%201640.8[32.75-49.35]59.2[50.65-67.25]100%High school graduate or equivalent201254.4[50.23-58.41]45.6[41.59-49.77]100%201650.4[45.42-55.29]49.6[44.71-54.58]100%Vocational/technical school after HS201260.3[57.72-62.87]39.7[37.13-42.28]100%201660.7[57.98-63.31]39.3[36.69-42.02]100%College graduate201269.4[66.49-72.09]30.6[27.91-33.51]100%201665.8[62.83-68.74]34.2[31.26-37.17]100%Graduate or professional school201274.7[71.71-77.44]25.3[22.56-28.29]100%201676.0[72.69-78.94]24.0[21.06-27.31]100%20122016 Child has nonparental care at least once a weekChild has nonparental care at least once a week Currently participates in any care or program arrangement that occurs at least once each weekDoes not currently participate at least once each weekCurrently participates in any care or program arrangement that occurs at least once each weekDoes not currently participate at least once each weekEstimatesTotal60.439.659.540.5Educational attainment of child's parent or guardianLess than high school credential44.056.040.859.2High school graduate or equivalent54.445.650.449.6Vocational/technical school after HS60.339.760.739.3College graduate69.430.665.834.2Graduate or professional school74.725.376.024.020122016 Child has nonparental care at least once a weekChild has nonparental care at least once a week Currently participates in any care or program arrangement that occurs at least once each weekDoes not currently participate at least once each weekCurrently participates in any care or program arrangement that occurs at least once each weekDoes not currently participate at least once each weekEstimatesTotal60.439.659.540.5Educational attainment of child's parent or guardianLess than high school credential44.056.040.859.2High school graduate or equivalent54.445.650.449.6Vocational/technical school after HS60.339.760.739.3College graduate69.430.665.834.2Graduate or professional school74.725.376.024.0Standard Error (BRR)Total0.780.780.960.96Educational attainment of child's parent or guardianLess than high school credential2.442.444.214.21High school graduate or equivalent2.062.062.492.49Vocational/technical school after HS1.291.291.341.34College graduate1.411.411.491.49Graduate or professional school1.441.441.571.57Relative Standard Error (%)Total1.291.971.612.37Educational attainment of child's parent or guardianLess than high school credential5.544.3610.327.11High school graduate or equivalent3.794.514.945.01Vocational/technical school after HS2.143.262.213.41College graduate2.034.602.264.35Graduate or professional school1.935.682.076.54Weighted Sample Sizes (n/1,000s)Total21,674.7 21,437.9 Educational attainment of child's parent or guardianLess than high school credential3,296.5 2,779.0 High school graduate or equivalent4,613.5 4,349.2 Vocational/technical school after HS6,215.0 5,566.7 College graduate4,821.6 5,790.7 Graduate or professional school2,728.1 2,952.4 20122016 Child has nonparental care at least once a weekChild has nonparental care at least once a week Currently participates in any care or program arrangement that occurs at least once each weekDoes not currently participate at least once each weekCurrently participates in any care or program arrangement that occurs at least once each weekDoes not currently participate at least once each week Pct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal60.4[58.83-61.93]39.6[38.07-41.17]59.5[57.58-61.40]40.5[38.60-42.42]Educational attainment of child's parent or guardianLess than high school credential44.0[39.23-48.91]56.0[51.09-60.77]40.8[32.75-49.35]59.2[50.65-67.25]High school graduate or equivalent54.4[50.23-58.41]45.6[41.59-49.77]50.4[45.42-55.29]49.6[44.71-54.58]Vocational/technical school after HS60.3[57.72-62.87]39.7[37.13-42.28]60.7[57.98-63.31]39.3[36.69-42.02]College graduate69.4[66.49-72.09]30.6[27.91-33.51]65.8[62.83-68.74]34.2[31.26-37.17]Graduate or professional school74.7[71.71-77.44]25.3[22.56-28.29]76.0[72.69-78.94]24.0[21.06-27.31]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: ANYCARE2X and PAR1EDUC. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: ANYCARE2X (ECPP:2012, ECPP:2016) and PAR1EDUC (ECPP:2012, ECPP:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2012 and National Household Education Surveys Program, Early Childhood Program Participation (ECPP), 2016.Computation by NCES TrendStats on 10/24/2018.cembkafa2 cembkafa2 1Average Age of child when first moved to US by Detailed race and ethnicity of child and Specific relationship of first parent or guardian to child for years 2012 and 2016 Age of child when first moved to US(Avg)EstimatesTotal20124.720165.6Detailed race and ethnicity of childWhite, non-Hispanic20123.120164.9Black, non-Hispanic20124.320166.2Hispanic20125.220165.8Asian or Pacific Islander, non-Hispanic20125.520165.6All other races and multiple races, non-Hispanic20122.7 !2016‡Specific relationship of first parent or guardian to childBirth or adoptive mother20124.320165.5Birth or adoptive father20125.220165.5Step or foster mother2012‡2016‡Step or foster father2012‡2016‡Grandmother or other female guardian20123.82016‡Grandfather or other male guardian2012‡2016‡Average Age of child when first moved to US by Detailed race and ethnicity of child and Specific relationship of first parent or guardian to child for years 2012 and 2016 Age of child when first moved to US(Avg)EstimatesTotal20124.720165.6Detailed race and ethnicity of childWhite, non-Hispanic20123.120164.9Black, non-Hispanic20124.320166.2Hispanic20125.220165.8Asian or Pacific Islander, non-Hispanic20125.520165.6All other races and multiple races, non-Hispanic20122.7 !2016‡Specific relationship of first parent or guardian to childBirth or adoptive mother20124.320165.5Birth or adoptive father20125.220165.5Step or foster mother2012‡2016‡Step or foster father2012‡2016‡Grandmother or other female guardian20123.82016‡Grandfather or other male guardian2012‡2016‡Standard Error (BRR)Total20120.2020160.25Detailed race and ethnicity of childWhite, non-Hispanic20120.3020160.49Black, non-Hispanic20120.6620160.79Hispanic20120.2820160.40Asian or Pacific Islander, non-Hispanic20120.3520160.37All other races and multiple races, non-Hispanic20121.192016‡Specific relationship of first parent or guardian to childBirth or adoptive mother20120.2620160.32Birth or adoptive father20120.2820160.46Step or foster mother2012‡2016‡Step or foster father2012‡2016‡Grandmother or other female guardian20120.892016‡Grandfather or other male guardian2012‡2016‡Relative Standard Error (%)Total20124.2120164.55Detailed race and ethnicity of childWhite, non-Hispanic20129.64201610.01Black, non-Hispanic201215.51201612.81Hispanic20125.3520166.96Asian or Pacific Islander, non-Hispanic20126.4020166.60All other races and multiple races, non-Hispanic201244.332016‡Specific relationship of first parent or guardian to childBirth or adoptive mother20125.9620165.88Birth or adoptive father20125.2920168.29Step or foster mother2012‡2016‡Step or foster father2012‡2016‡Grandmother or other female guardian201223.332016‡Grandfather or other male guardian2012‡2016‡Weighted Sample Sizes (n/1,000s)Total20124,161.420163,150.2Detailed race and ethnicity of childWhite, non-Hispanic2012763.22016541.6Black, non-Hispanic2012613.92016426.4Hispanic20121,754.420161,344.3Asian or Pacific Islander, non-Hispanic2012870.42016745.7All other races and multiple races, non-Hispanic2012159.52016‡Specific relationship of first parent or guardian to childBirth or adoptive mother20122,460.320161,811.4Birth or adoptive father20121,445.220161,181.7Step or foster mother2012‡2016‡Step or foster father2012‡2016‡Grandmother or other female guardian2012132.42016‡Grandfather or other male guardian2012‡2016‡Average Age of child when first moved to US by Detailed race and ethnicity of child and Specific relationship of first parent or guardian to child for years 2012 and 2016 Age of child when first moved to US(Avg)Amt.95% CIEstimatesTotal20124.7[4.27-5.05]20165.6[5.07-6.08]Detailed race and ethnicity of childWhite, non-Hispanic20123.1[2.52-3.72]20164.9[3.89-5.82]Black, non-Hispanic20124.3[2.94-5.56]20166.2[4.60-7.76]Hispanic20125.2[4.68-5.79]20165.8[4.96-6.56]Asian or Pacific Islander, non-Hispanic20125.5[4.81-6.21]20165.6[4.86-6.33]All other races and multiple races, non-Hispanic20122.7 ![0.32-5.06]2016‡‡Specific relationship of first parent or guardian to childBirth or adoptive mother20124.3[3.82-4.85]20165.5[4.83-6.12]Birth or adoptive father20125.2[4.67-5.77]20165.5[4.60-6.42]Step or foster mother2012‡‡2016‡‡Step or foster father2012‡‡2016‡‡Grandmother or other female guardian20123.8[2.03-5.56]2016‡‡Grandfather or other male guardian2012‡‡2016‡‡20122016 Age of child when first moved to USAge of child when first moved to US (Avg)(Avg)EstimatesTotal4.75.6Detailed race and ethnicity of childWhite, non-Hispanic3.14.9Black, non-Hispanic4.36.2Hispanic5.25.8Asian or Pacific Islander, non-Hispanic5.55.6All other races and multiple races, non-Hispanic2.7 !‡Specific relationship of first parent or guardian to childBirth or adoptive mother4.35.5Birth or adoptive father5.25.5Step or foster mother‡‡Step or foster father‡‡Grandmother or other female guardian3.8‡Grandfather or other male guardian‡‡20122016 Age of child when first moved to USAge of child when first moved to US (Avg)(Avg)EstimatesTotal4.75.6Detailed race and ethnicity of childWhite, non-Hispanic3.14.9Black, non-Hispanic4.36.2Hispanic5.25.8Asian or Pacific Islander, non-Hispanic5.55.6All other races and multiple races, non-Hispanic2.7 !‡Specific relationship of first parent or guardian to childBirth or adoptive mother4.35.5Birth or adoptive father5.25.5Step or foster mother‡‡Step or foster father‡‡Grandmother or other female guardian3.8‡Grandfather or other male guardian‡‡Standard Error (BRR)Total0.200.25Detailed race and ethnicity of childWhite, non-Hispanic0.300.49Black, non-Hispanic0.660.79Hispanic0.280.40Asian or Pacific Islander, non-Hispanic0.350.37All other races and multiple races, non-Hispanic1.19‡Specific relationship of first parent or guardian to childBirth or adoptive mother0.260.32Birth or adoptive father0.280.46Step or foster mother‡‡Step or foster father‡‡Grandmother or other female guardian0.89‡Grandfather or other male guardian‡‡Relative Standard Error (%)Total4.214.55Detailed race and ethnicity of childWhite, non-Hispanic9.6410.01Black, non-Hispanic15.5112.81Hispanic5.356.96Asian or Pacific Islander, non-Hispanic6.406.60All other races and multiple races, non-Hispanic44.33‡Specific relationship of first parent or guardian to childBirth or adoptive mother5.965.88Birth or adoptive father5.298.29Step or foster mother‡‡Step or foster father‡‡Grandmother or other female guardian23.33‡Grandfather or other male guardian‡‡Weighted Sample Sizes (n/1,000s)Total4,161.43,150.2Detailed race and ethnicity of childWhite, non-Hispanic763.2541.6Black, non-Hispanic613.9426.4Hispanic1,754.41,344.3Asian or Pacific Islander, non-Hispanic870.4745.7All other races and multiple races, non-Hispanic159.5‡Specific relationship of first parent or guardian to childBirth or adoptive mother2,460.31,811.4Birth or adoptive father1,445.21,181.7Step or foster mother‡‡Step or foster father‡‡Grandmother or other female guardian132.4‡Grandfather or other male guardian‡‡20122016 Age of child when first moved to USAge of child when first moved to US (Avg)(Avg) Amt.95% CIAmt.95% CIEstimatesTotal4.7[4.27-5.05]5.6[5.07-6.08]Detailed race and ethnicity of childWhite, non-Hispanic3.1[2.52-3.72]4.9[3.89-5.82]Black, non-Hispanic4.3[2.94-5.56]6.2[4.60-7.76]Hispanic5.2[4.68-5.79]5.8[4.96-6.56]Asian or Pacific Islander, non-Hispanic5.5[4.81-6.21]5.6[4.86-6.33]All other races and multiple races, non-Hispanic2.7 ![0.32-5.06]‡‡Specific relationship of first parent or guardian to childBirth or adoptive mother4.3[3.82-4.85]5.5[4.83-6.12]Birth or adoptive father5.2[4.67-5.77]5.5[4.60-6.42]Step or foster mother‡‡‡‡Step or foster father‡‡‡‡Grandmother or other female guardian3.8[2.03-5.56]‡‡Grandfather or other male guardian‡‡‡‡! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.‡ Reporting standards not met.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: CMOVEAGE, RACEETH2 and PAR1TYPE. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: CMOVEAGE (PFI:2012, PFI:2016), RACEETH2 (PFI:2012, PFI:2016) and PAR1TYPE (PFI:2012, PFI:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012 and National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES TrendStats on 11/29/2018.cmnbkp11cmnbkp112Hours spent doing homework with (Percent>5) by Adult's feelings about amount of homework assigned and Child's feelings about amount of homework assigned for years 2012 and 2016 Hours spent doing homework(%>5)EstimatesTotal201230.1201630.4Adult's feelings about amount of homework assignedThe amount is about right201230.5201629.9It's too much201260.6201661.2It's too little201214.2201613.7Child's feelings about amount of homework assignedThe amount is about right201226.7201625.9It's too much201244.1201646.2It's too little201215.1201617.7Hours spent doing homework with (Percent>5) by Adult's feelings about amount of homework assigned and Child's feelings about amount of homework assigned for years 2012 and 2016 Hours spent doing homework(%>5)EstimatesTotal201230.1201630.4Adult's feelings about amount of homework assignedThe amount is about right201230.5201629.9It's too much201260.6201661.2It's too little201214.2201613.7Child's feelings about amount of homework assignedThe amount is about right201226.7201625.9It's too much201244.1201646.2It's too little201215.1201617.7Standard Error (BRR)Total20120.5420160.64Adult's feelings about amount of homework assignedThe amount is about right20120.6620160.74It's too much20121.8520161.89It's too little20120.9520161.21Child's feelings about amount of homework assignedThe amount is about right20120.6720160.75It's too much20120.8520161.23It's too little20122.2820163.19Relative Standard Error (%)Total20121.8020162.09Adult's feelings about amount of homework assignedThe amount is about right20122.1720162.47It's too much20123.0520163.09It's too little20126.7020168.81Child's feelings about amount of homework assignedThe amount is about right20122.5020162.90It's too much20121.9320162.67It's too little201215.10201618.02Weighted Sample Sizes (n/1,000s)Total201252,215.3201651,161.9Adult's feelings about amount of homework assignedThe amount is about right201238,765.0201636,200.9It's too much20124,964.320166,534.5It's too little20126,399.620165,328.7Child's feelings about amount of homework assignedThe amount is about right201233,713.4201630,671.9It's too much201214,680.3201615,932.5It's too little20121,735.220161,459.7Hours spent doing homework with (Percent>5) by Adult's feelings about amount of homework assigned and Child's feelings about amount of homework assigned for years 2012 and 2016 Hours spent doing homework(%>5)Pct.95% CIEstimatesTotal201230.1[29.06-31.22]201630.4[29.15-31.68]Adult's feelings about amount of homework assignedThe amount is about right201230.5[29.18-31.82]201629.9[28.46-31.39]It's too much201260.6[56.86-64.21]201661.2[57.35-64.87]It's too little201214.2[12.40-16.18]201613.7[11.48-16.30]Child's feelings about amount of homework assignedThe amount is about right201226.7[25.36-28.02]201625.9[24.40-27.38]It's too much201244.1[42.46-45.84]201646.2[43.77-48.67]It's too little201215.1[11.11-20.24]201617.7[12.20-24.93]20122016 Hours spent doing homeworkHours spent doing homework (%>5)(%>5)EstimatesTotal30.130.4Adult's feelings about amount of homework assignedThe amount is about right30.529.9It's too much60.661.2It's too little14.213.7Child's feelings about amount of homework assignedThe amount is about right26.725.9It's too much44.146.2It's too little15.117.720122016 Hours spent doing homeworkHours spent doing homework (%>5)(%>5)EstimatesTotal30.130.4Adult's feelings about amount of homework assignedThe amount is about right30.529.9It's too much60.661.2It's too little14.213.7Child's feelings about amount of homework assignedThe amount is about right26.725.9It's too much44.146.2It's too little15.117.7Standard Error (BRR)Total0.540.64Adult's feelings about amount of homework assignedThe amount is about right0.660.74It's too much1.851.89It's too little0.951.21Child's feelings about amount of homework assignedThe amount is about right0.670.75It's too much0.851.23It's too little2.283.19Relative Standard Error (%)Total1.802.09Adult's feelings about amount of homework assignedThe amount is about right2.172.47It's too much3.053.09It's too little6.708.81Child's feelings about amount of homework assignedThe amount is about right2.502.90It's too much1.932.67It's too little15.1018.02Weighted Sample Sizes (n/1,000s)Total52,215.351,161.9Adult's feelings about amount of homework assignedThe amount is about right38,765.036,200.9It's too much4,964.36,534.5It's too little6,399.65,328.7Child's feelings about amount of homework assignedThe amount is about right33,713.430,671.9It's too much14,680.315,932.5It's too little1,735.21,459.720122016 Hours spent doing homeworkHours spent doing homework (%>5)(%>5) Pct.95% CIPct.95% CIEstimatesTotal30.1[29.06-31.22]30.4[29.15-31.68]Adult's feelings about amount of homework assignedThe amount is about right30.5[29.18-31.82]29.9[28.46-31.39]It's too much60.6[56.86-64.21]61.2[57.35-64.87]It's too little14.2[12.40-16.18]13.7[11.48-16.30]Child's feelings about amount of homework assignedThe amount is about right26.7[25.36-28.02]25.9[24.40-27.38]It's too much44.1[42.46-45.84]46.2[43.77-48.67]It's too little15.1[11.11-20.24]17.7[12.20-24.93]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: FHWKHRS, FHAMOUNT and FHCAMT. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: FHWKHRS (PFI:2012, PFI:2016), FHAMOUNT (PFI:2012, PFI:2016) and FHCAMT (PFI:2012, PFI:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012 and National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES TrendStats on 11/29/2018.cmnbkpf4ccmnbkpf4c3In the past week, number of times the family has eaten the evening meal together with (Percent<5) by Child's grades for years 2012 and 2016 In the past week, number of times the family has eaten the evening meal together(%<5)EstimatesTotal201238.2201638.2Child's gradesMostly A's201236.5201638.1Mostly B's201241.2201641.5Mostly C's201247.7201639.4Mostly D's or lower201248.1201649.3School does not give these grades201228.9201629.0In the past week, number of times the family has eaten the evening meal together with (Percent<5) by Child's grades for years 2012 and 2016 In the past week, number of times the family has eaten the evening meal together(%<5)EstimatesTotal201238.2201638.2Child's gradesMostly A's201236.5201638.1Mostly B's201241.2201641.5Mostly C's201247.7201639.4Mostly D's or lower201248.1201649.3School does not give these grades201228.9201629.0Standard Error (BRR)Total20120.5320160.67Child's gradesMostly A's20120.8320160.95Mostly B's20120.7920161.44Mostly C's20121.5720162.52Mostly D's or lower20124.2220165.71School does not give these grades20121.5920161.61Relative Standard Error (%)Total20121.3920161.76Child's gradesMostly A's20122.2720162.50Mostly B's20121.9220163.47Mostly C's20123.2920166.38Mostly D's or lower20128.78201611.58School does not give these grades20125.5120165.53Weighted Sample Sizes (n/1,000s)Total201252,215.3201651,161.9Child's gradesMostly A's201221,850.5201621,367.2Mostly B's201215,792.2201615,000.6Mostly C's20125,667.820165,638.2Mostly D's or lower20121,087.820161,360.5School does not give these grades20127,816.920167,795.5In the past week, number of times the family has eaten the evening meal together with (Percent<5) by Child's grades for years 2012 and 2016 In the past week, number of times the family has eaten the evening meal together(%<5)Pct.95% CIEstimatesTotal201238.2[37.15-39.26]201638.2[36.82-39.49]Child's gradesMostly A's201236.5[34.81-38.11]201638.1[36.22-40.01]Mostly B's201241.2[39.59-42.73]201641.5[38.60-44.32]Mostly C's201247.7[44.56-50.79]201639.4[34.44-44.46]Mostly D's or lower201248.1[39.70-56.50]201649.3[37.94-60.67]School does not give these grades201228.9[25.72-32.06]201629.0[25.83-32.23]20122016 In the past week, number of times the family has eaten the evening meal togetherIn the past week, number of times the family has eaten the evening meal together (%<5)(%<5)EstimatesTotal38.238.2Child's gradesMostly A's36.538.1Mostly B's41.241.5Mostly C's47.739.4Mostly D's or lower48.149.3School does not give these grades28.929.020122016 In the past week, number of times the family has eaten the evening meal togetherIn the past week, number of times the family has eaten the evening meal together (%<5)(%<5)EstimatesTotal38.238.2Child's gradesMostly A's36.538.1Mostly B's41.241.5Mostly C's47.739.4Mostly D's or lower48.149.3School does not give these grades28.929.0Standard Error (BRR)Total0.530.67Child's gradesMostly A's0.830.95Mostly B's0.791.44Mostly C's1.572.52Mostly D's or lower4.225.71School does not give these grades1.591.61Relative Standard Error (%)Total1.391.76Child's gradesMostly A's2.272.50Mostly B's1.923.47Mostly C's3.296.38Mostly D's or lower8.7811.58School does not give these grades5.515.53Weighted Sample Sizes (n/1,000s)Total52,215.351,161.9Child's gradesMostly A's21,850.521,367.2Mostly B's15,792.215,000.6Mostly C's5,667.85,638.2Mostly D's or lower1,087.81,360.5School does not give these grades7,816.97,795.520122016 In the past week, number of times the family has eaten the evening meal togetherIn the past week, number of times the family has eaten the evening meal together (%<5)(%<5) Pct.95% CIPct.95% CIEstimatesTotal38.2[37.15-39.26]38.2[36.82-39.49]Child's gradesMostly A's36.5[34.81-38.11]38.1[36.22-40.01]Mostly B's41.2[39.59-42.73]41.5[38.60-44.32]Mostly C's47.7[44.56-50.79]39.4[34.44-44.46]Mostly D's or lower48.1[39.70-56.50]49.3[37.94-60.67]School does not give these grades28.9[25.72-32.06]29.0[25.83-32.23]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: FODINNERX and SEGRADES. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: FODINNERX (PFI:2012, PFI:2016) and SEGRADES (PFI:2012, PFI:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012 and National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES TrendStats on 11/29/2018.cmnbkp9dcmnbkp9d4Average>0 Total people in household by Zip code classification by community type for years 2012 and 2016 Total people in household(Avg>0)EstimatesTotal20124.520164.5Zip code classification by community typeCity - Large20124.520164.7City - Midsize20124.520164.5City - Small20124.420164.5Suburb - Large20124.620164.5Suburb - Midsize20124.520164.4Suburb - Small20124.620164.4Town - Fringe20124.320164.2Town - Distant20124.620164.4Town - Remote20124.720164.4Rural - Fringe20124.420164.6Rural - Distant20124.520164.5Rural - Remote20124.320164.6Average>0 Total people in household by Zip code classification by community type for years 2012 and 2016 Total people in household(Avg>0)EstimatesTotal20124.520164.5Zip code classification by community typeCity - Large20124.520164.7City - Midsize20124.520164.5City - Small20124.420164.5Suburb - Large20124.620164.5Suburb - Midsize20124.520164.4Suburb - Small20124.620164.4Town - Fringe20124.320164.2Town - Distant20124.620164.4Town - Remote20124.720164.4Rural - Fringe20124.420164.6Rural - Distant20124.520164.5Rural - Remote20124.320164.6Standard Error (BRR)Total20120.0120160.02Zip code classification by community typeCity - Large20120.0420160.10City - Midsize20120.0720160.10City - Small20120.0620160.11Suburb - Large20120.0220160.03Suburb - Midsize20120.1220160.11Suburb - Small20120.1320160.10Town - Fringe20120.1120160.08Town - Distant20120.0920160.09Town - Remote20120.2420160.13Rural - Fringe20120.0420160.07Rural - Distant20120.0720160.08Rural - Remote20120.0920160.17Relative Standard Error (%)Total20120.2420160.40Zip code classification by community typeCity - Large20120.9720162.18City - Midsize20121.5620162.25City - Small20121.2720162.33Suburb - Large20120.5220160.72Suburb - Midsize20122.6120162.39Suburb - Small20122.8020162.37Town - Fringe20122.4520162.00Town - Distant20121.8420162.11Town - Remote20125.0720163.00Rural - Fringe20120.8220161.55Rural - Distant20121.4620161.86Rural - Remote20122.0220163.57Weighted Sample Sizes (n/1,000s)Total201252,215.3201651,161.9Zip code classification by community typeCity - Large20128,542.420168,821.9City - Midsize20123,285.120163,797.5City - Small20123,618.920163,658.6Suburb - Large201216,499.2201619,584.4Suburb - Midsize20122,009.020161,923.7Suburb - Small20121,231.220161,106.6Town - Fringe2012761.120161,168.3Town - Distant20122,601.720161,889.3Town - Remote20121,515.12016984.3Rural - Fringe20126,971.120164,294.3Rural - Distant20124,192.020163,225.9Rural - Remote2012988.42016707.0Average>0 Total people in household by Zip code classification by community type for years 2012 and 2016 Total people in household(Avg>0)Amt.95% CIEstimatesTotal20124.5[4.50-4.54]20164.5[4.51-4.58]Zip code classification by community typeCity - Large20124.5[4.44-4.62]20164.7[4.54-4.95]City - Midsize20124.5[4.37-4.65]20164.5[4.35-4.75]City - Small20124.4[4.31-4.53]20164.5[4.31-4.73]Suburb - Large20124.6[4.52-4.61]20164.5[4.46-4.59]Suburb - Midsize20124.5[4.31-4.78]20164.4[4.21-4.63]Suburb - Small20124.6[4.34-4.85]20164.4[4.20-4.62]Town - Fringe20124.3[4.14-4.56]20164.2[4.05-4.39]Town - Distant20124.6[4.45-4.79]20164.4[4.22-4.59]Town - Remote20124.7[4.24-5.19]20164.4[4.12-4.64]Rural - Fringe20124.4[4.35-4.49]20164.6[4.41-4.69]Rural - Distant20124.5[4.34-4.60]20164.5[4.38-4.72]Rural - Remote20124.3[4.16-4.51]20164.6[4.29-4.95]20122016 Total people in householdTotal people in household (Avg>0)(Avg>0)EstimatesTotal4.54.5Zip code classification by community typeCity - Large4.54.7City - Midsize4.54.5City - Small4.44.5Suburb - Large4.64.5Suburb - Midsize4.54.4Suburb - Small4.64.4Town - Fringe4.34.2Town - Distant4.64.4Town - Remote4.74.4Rural - Fringe4.44.6Rural - Distant4.54.5Rural - Remote4.34.620122016 Total people in householdTotal people in household (Avg>0)(Avg>0)EstimatesTotal4.54.5Zip code classification by community typeCity - Large4.54.7City - Midsize4.54.5City - Small4.44.5Suburb - Large4.64.5Suburb - Midsize4.54.4Suburb - Small4.64.4Town - Fringe4.34.2Town - Distant4.64.4Town - Remote4.74.4Rural - Fringe4.44.6Rural - Distant4.54.5Rural - Remote4.34.6Standard Error (BRR)Total0.010.02Zip code classification by community typeCity - Large0.040.10City - Midsize0.070.10City - Small0.060.11Suburb - Large0.020.03Suburb - Midsize0.120.11Suburb - Small0.130.10Town - Fringe0.110.08Town - Distant0.090.09Town - Remote0.240.13Rural - Fringe0.040.07Rural - Distant0.070.08Rural - Remote0.090.17Relative Standard Error (%)Total0.240.40Zip code classification by community typeCity - Large0.972.18City - Midsize1.562.25City - Small1.272.33Suburb - Large0.520.72Suburb - Midsize2.612.39Suburb - Small2.802.37Town - Fringe2.452.00Town - Distant1.842.11Town - Remote5.073.00Rural - Fringe0.821.55Rural - Distant1.461.86Rural - Remote2.023.57Weighted Sample Sizes (n/1,000s)Total52,215.351,161.9Zip code classification by community typeCity - Large8,542.48,821.9City - Midsize3,285.13,797.5City - Small3,618.93,658.6Suburb - Large16,499.219,584.4Suburb - Midsize2,009.01,923.7Suburb - Small1,231.21,106.6Town - Fringe761.11,168.3Town - Distant2,601.71,889.3Town - Remote1,515.1984.3Rural - Fringe6,971.14,294.3Rural - Distant4,192.03,225.9Rural - Remote988.4707.020122016 Total people in householdTotal people in household (Avg>0)(Avg>0) Amt.95% CIAmt.95% CIEstimatesTotal4.5[4.50-4.54]4.5[4.51-4.58]Zip code classification by community typeCity - Large4.5[4.44-4.62]4.7[4.54-4.95]City - Midsize4.5[4.37-4.65]4.5[4.35-4.75]City - Small4.4[4.31-4.53]4.5[4.31-4.73]Suburb - Large4.6[4.52-4.61]4.5[4.46-4.59]Suburb - Midsize4.5[4.31-4.78]4.4[4.21-4.63]Suburb - Small4.6[4.34-4.85]4.4[4.20-4.62]Town - Fringe4.3[4.14-4.56]4.2[4.05-4.39]Town - Distant4.6[4.45-4.79]4.4[4.22-4.59]Town - Remote4.7[4.24-5.19]4.4[4.12-4.64]Rural - Fringe4.4[4.35-4.49]4.6[4.41-4.69]Rural - Distant4.5[4.34-4.60]4.5[4.38-4.72]Rural - Remote4.3[4.16-4.51]4.6[4.29-4.95]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: HHTOTALXX and ZIPLOCL. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: HHTOTALX (PFI:2012), ZIPLOCL (PFI:2012, PFI:2016) and HHTOTALXX (PFI:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012 and National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES TrendStats on 11/29/2018.cmnbkpbdcmnbkpbd5Median Number of times an adult in child's household participated in school meetings by Satisfaction with school and Satisfaction with teachers for years 2012 and 2016 Number of times an adult in child's household participated in school meetings(Median)EstimatesTotal20125.020165.0Satisfaction with schoolVery satisfied20125.020165.0Somewhat satisfied20124.020164.0Somewhat dissatisfied20124.020164.0Very dissatisfied20124.020165.0Satisfaction with teachersVery satisfied20125.020165.0Somewhat satisfied20124.020164.0 !!Somewhat dissatisfied20124.020164.0 !!Very dissatisfied20124.0 !!20165.0Median Number of times an adult in child's household participated in school meetings by Satisfaction with school and Satisfaction with teachers for years 2012 and 2016 Number of times an adult in child's household participated in school meetings(Median)EstimatesTotal20125.020165.0Satisfaction with schoolVery satisfied20125.020165.0Somewhat satisfied20124.020164.0Somewhat dissatisfied20124.020164.0Very dissatisfied20124.020165.0Satisfaction with teachersVery satisfied20125.020165.0Somewhat satisfied20124.020164.0 !!Somewhat dissatisfied20124.020164.0 !!Very dissatisfied20124.0 !!20165.0Standard Error (BRR)Total2012^2016^Satisfaction with schoolVery satisfied2012^2016^Somewhat satisfied2012^2016^Somewhat dissatisfied2012^2016^Very dissatisfied2012^2016^Satisfaction with teachersVery satisfied2012^2016^Somewhat satisfied2012^20162.63Somewhat dissatisfied2012^20164.66Very dissatisfied20122.222016^Relative Standard Error (%)Total2012^2016^Satisfaction with schoolVery satisfied2012^2016^Somewhat satisfied2012^2016^Somewhat dissatisfied2012^2016^Very dissatisfied2012^2016^Satisfaction with teachersVery satisfied2012^2016^Somewhat satisfied2012^201665.73Somewhat dissatisfied2012^2016116.53Very dissatisfied201255.552016^Weighted Sample Sizes (n/1,000s)Total201252,215.3201651,161.9Satisfaction with schoolVery satisfied201230,878.9201630,933.0Somewhat satisfied201216,812.6201616,214.3Somewhat dissatisfied20123,312.920162,964.2Very dissatisfied20121,210.920161,050.5Satisfaction with teachersVery satisfied201231,077.7201631,172.7Somewhat satisfied201216,928.7201616,282.4Somewhat dissatisfied20123,300.420162,938.3Very dissatisfied2012908.52016768.6Median Number of times an adult in child's household participated in school meetings by Satisfaction with school and Satisfaction with teachers for years 2012 and 2016 Number of times an adult in child's household participated in school meetings(Median)Amt.95% CIEstimatesTotal20125.0[5.00-5.00]20165.0[5.00-5.00]Satisfaction with schoolVery satisfied20125.0[5.00-5.00]20165.0[5.00-5.00]Somewhat satisfied20124.0[4.00-4.00]20164.0[4.00-4.00]Somewhat dissatisfied20124.0[4.00-4.00]20164.0[4.00-4.00]Very dissatisfied20124.0[4.00-4.00]20165.0[5.00-5.00]Satisfaction with teachersVery satisfied20125.0[5.00-5.00]20165.0[5.00-5.00]Somewhat satisfied20124.0[4.00-4.00]20164.0 !![-1.23-9.23]Somewhat dissatisfied20124.0[4.00-4.00]20164.0 !![-5.28-13.28]Very dissatisfied20124.0 !![-0.42-8.42]20165.0[5.00-5.00]20122016 Number of times an adult in child's household participated in school meetingsNumber of times an adult in child's household participated in school meetings (Median)(Median)EstimatesTotal5.05.0Satisfaction with schoolVery satisfied5.05.0Somewhat satisfied4.04.0Somewhat dissatisfied4.04.0Very dissatisfied4.05.0Satisfaction with teachersVery satisfied5.05.0Somewhat satisfied4.04.0 !!Somewhat dissatisfied4.04.0 !!Very dissatisfied4.0 !!5.020122016 Number of times an adult in child's household participated in school meetingsNumber of times an adult in child's household participated in school meetings (Median)(Median)EstimatesTotal5.05.0Satisfaction with schoolVery satisfied5.05.0Somewhat satisfied4.04.0Somewhat dissatisfied4.04.0Very dissatisfied4.05.0Satisfaction with teachersVery satisfied5.05.0Somewhat satisfied4.04.0 !!Somewhat dissatisfied4.04.0 !!Very dissatisfied4.0 !!5.0Standard Error (BRR)Total^^Satisfaction with schoolVery satisfied^^Somewhat satisfied^^Somewhat dissatisfied^^Very dissatisfied^^Satisfaction with teachersVery satisfied^^Somewhat satisfied^2.63Somewhat dissatisfied^4.66Very dissatisfied2.22^Relative Standard Error (%)Total^^Satisfaction with schoolVery satisfied^^Somewhat satisfied^^Somewhat dissatisfied^^Very dissatisfied^^Satisfaction with teachersVery satisfied^^Somewhat satisfied^65.73Somewhat dissatisfied^116.53Very dissatisfied55.55^Weighted Sample Sizes (n/1,000s)Total52,215.351,161.9Satisfaction with schoolVery satisfied30,878.930,933.0Somewhat satisfied16,812.616,214.3Somewhat dissatisfied3,312.92,964.2Very dissatisfied1,210.91,050.5Satisfaction with teachersVery satisfied31,077.731,172.7Somewhat satisfied16,928.716,282.4Somewhat dissatisfied3,300.42,938.3Very dissatisfied908.5768.620122016 Number of times an adult in child's household participated in school meetingsNumber of times an adult in child's household participated in school meetings (Median)(Median) Amt.95% CIAmt.95% CIEstimatesTotal5.0[5.00-5.00]5.0[5.00-5.00]Satisfaction with schoolVery satisfied5.0[5.00-5.00]5.0[5.00-5.00]Somewhat satisfied4.0[4.00-4.00]4.0[4.00-4.00]Somewhat dissatisfied4.0[4.00-4.00]4.0[4.00-4.00]Very dissatisfied4.0[4.00-4.00]5.0[5.00-5.00]Satisfaction with teachersVery satisfied5.0[5.00-5.00]5.0[5.00-5.00]Somewhat satisfied4.0[4.00-4.00]4.0 !![-1.23-9.23]Somewhat dissatisfied4.0[4.00-4.00]4.0 !![-5.28-13.28]Very dissatisfied4.0 !![-0.42-8.42]5.0[5.00-5.00]!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.^ Standard error of quantile, as estimated by Woodruff method, is zero. Use caution in hypothesis testing.STDERR-SOURCE-END!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: FSFREQ, FCSCHOOL and FCTEACHR. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: FSFREQ (PFI:2012, PFI:2016), FCSCHOOL (PFI:2012, PFI:2016) and FCTEACHR (PFI:2012, PFI:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012 and National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES TrendStats on 11/29/2018.cmnbkp8bcmnbkp8b1Child's family received newsletters from the school by Total school enrollment of students, Child Sex and Zip code classification by community type for years 2012 and 2016 Child's family received newsletters from the schoolYesNoTotalEstimatesTotal201286.713.3100%201689.410.6100%Total school enrollment of studentsUnder 300201287.612.4100%201689.710.3100%300-599201287.712.3100%201691.28.8100%600-999201288.012.0100%201689.210.8100%1,000 or more201283.516.5100%201686.913.1100%Child SexMale201285.614.4100%201688.811.2100%Female201287.912.1100%201690.010.0100%Zip code classification by community typeCity201283.516.5100%201688.111.9100%Suburb201289.710.3100%201692.17.9100%Town201285.514.5100%201685.914.1100%Rural201286.513.5100%201686.014.0100%Child's family received newsletters from the school by Total school enrollment of students, Child Sex and Zip code classification by community type for years 2012 and 2016 Child's family received newsletters from the schoolYesNoTotalEstimatesTotal201286.713.3100%201689.410.6100%Total school enrollment of studentsUnder 300201287.612.4100%201689.710.3100%300-599201287.712.3100%201691.28.8100%600-999201288.012.0100%201689.210.8100%1,000 or more201283.516.5100%201686.913.1100%Child SexMale201285.614.4100%201688.811.2100%Female201287.912.1100%201690.010.0100%Zip code classification by community typeCity201283.516.5100%201688.111.9100%Suburb201289.710.3100%201692.17.9100%Town201285.514.5100%201685.914.1100%Rural201286.513.5100%201686.014.0100%Standard Error (BRR)Total20120.380.38 20160.440.44 Total school enrollment of studentsUnder 30020121.081.08 20161.481.48 300-59920120.760.76 20160.690.69 600-99920120.670.67 20160.820.82 1,000 or more20120.870.87 20161.081.08 Child SexMale20120.600.60 20160.690.69 Female20120.460.46 20160.540.54 Zip code classification by community typeCity20120.770.77 20160.940.94 Suburb20120.600.60 20160.530.53 Town20121.241.24 20161.631.63 Rural20121.031.03 20161.111.11 Relative Standard Error (%)Total20120.432.82 20160.504.17 Total school enrollment of studentsUnder 30020121.238.70 20161.6514.40 300-59920120.866.14 20160.767.81 600-99920120.765.53 20160.927.64 1,000 or more20121.045.29 20161.248.24 Child SexMale20120.704.15 20160.786.19 Female20120.533.85 20160.605.41 Zip code classification by community typeCity20120.934.69 20161.067.85 Suburb20120.675.79 20160.576.71 Town20121.458.52 20161.8911.55 Rural20121.197.60 20161.297.94 Weighted Sample Sizes (n/1,000s)Total201252,215.3 201651,161.9 Total school enrollment of studentsUnder 30020125,897.9 20165,799.9 300-599201217,363.8 201616,952.9 600-999201215,654.7 201615,125.9 1,000 or more201213,044.9 201612,990.4 Child SexMale201226,981.8 201626,494.9 Female201225,233.4 201624,667.0 Zip code classification by community typeCity201215,446.5 201616,278.0 Suburb201219,739.4 201622,614.7 Town20124,877.9 20164,041.9 Rural201212,151.5 20168,227.3 Child's family received newsletters from the school by Total school enrollment of students, Child Sex and Zip code classification by community type for years 2012 and 2016 Child's family received newsletters from the schoolYesNoTotalPct.95% CIPct.95% CI EstimatesTotal201286.7[85.94-87.44]13.3[12.56-14.06]100%201689.4[88.44-90.21]10.6[9.79-11.56]100%Total school enrollment of studentsUnder 300201287.6[85.31-89.60]12.4[10.40-14.69]100%201689.7[86.35-92.30]10.3[7.70-13.65]100%300-599201287.7[86.07-89.08]12.3[10.92-13.93]100%201691.2[89.68-92.44]8.8[7.56-10.32]100%600-999201288.0[86.58-89.23]12.0[10.77-13.42]100%201689.2[87.49-90.76]10.8[9.24-12.51]100%1,000 or more201283.5[81.72-85.19]16.5[14.81-18.28]100%201686.9[84.57-88.88]13.1[11.12-15.43]100%Child SexMale201285.6[84.33-86.72]14.4[13.28-15.67]100%201688.8[87.33-90.10]11.2[9.90-12.67]100%Female201287.9[86.98-88.83]12.1[11.17-13.02]100%201690.0[88.83-90.99]10.0[9.01-11.17]100%Zip code classification by community typeCity201283.5[81.91-84.99]16.5[15.01-18.09]100%201688.1[86.06-89.80]11.9[10.20-13.94]100%Suburb201289.7[88.40-90.79]10.3[9.21-11.60]100%201692.1[90.99-93.10]7.9[6.90-9.01]100%Town201285.5[82.86-87.78]14.5[12.22-17.14]100%201685.9[82.37-88.86]14.1[11.14-17.63]100%Rural201286.5[84.30-88.39]13.5[11.61-15.70]100%201686.0[83.68-88.10]14.0[11.90-16.32]100%20122016 Child's family received newsletters from the schoolChild's family received newsletters from the school YesNoYesNoEstimatesTotal86.713.389.410.6Total school enrollment of studentsUnder 30087.612.489.710.3300-59987.712.391.28.8600-99988.012.089.210.81,000 or more83.516.586.913.1Child SexMale85.614.488.811.2Female87.912.190.010.0Zip code classification by community typeCity83.516.588.111.9Suburb89.710.392.17.9Town85.514.585.914.1Rural86.513.586.014.020122016 Child's family received newsletters from the schoolChild's family received newsletters from the school YesNoYesNoEstimatesTotal86.713.389.410.6Total school enrollment of studentsUnder 30087.612.489.710.3300-59987.712.391.28.8600-99988.012.089.210.81,000 or more83.516.586.913.1Child SexMale85.614.488.811.2Female87.912.190.010.0Zip code classification by community typeCity83.516.588.111.9Suburb89.710.392.17.9Town85.514.585.914.1Rural86.513.586.014.0Standard Error (BRR)Total0.380.380.440.44Total school enrollment of studentsUnder 3001.081.081.481.48300-5990.760.760.690.69600-9990.670.670.820.821,000 or more0.870.871.081.08Child SexMale0.600.600.690.69Female0.460.460.540.54Zip code classification by community typeCity0.770.770.940.94Suburb0.600.600.530.53Town1.241.241.631.63Rural1.031.031.111.11Relative Standard Error (%)Total0.432.820.504.17Total school enrollment of studentsUnder 3001.238.701.6514.40300-5990.866.140.767.81600-9990.765.530.927.641,000 or more1.045.291.248.24Child SexMale0.704.150.786.19Female0.533.850.605.41Zip code classification by community typeCity0.934.691.067.85Suburb0.675.790.576.71Town1.458.521.8911.55Rural1.197.601.297.94Weighted Sample Sizes (n/1,000s)Total52,215.3 51,161.9 Total school enrollment of studentsUnder 3005,897.9 5,799.9 300-59917,363.8 16,952.9 600-99915,654.7 15,125.9 1,000 or more13,044.9 12,990.4 Child SexMale26,981.8 26,494.9 Female25,233.4 24,667.0 Zip code classification by community typeCity15,446.5 16,278.0 Suburb19,739.4 22,614.7 Town4,877.9 4,041.9 Rural12,151.5 8,227.3 20122016 Child's family received newsletters from the schoolChild's family received newsletters from the school YesNoYesNo Pct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal86.7[85.94-87.44]13.3[12.56-14.06]89.4[88.44-90.21]10.6[9.79-11.56]Total school enrollment of studentsUnder 30087.6[85.31-89.60]12.4[10.40-14.69]89.7[86.35-92.30]10.3[7.70-13.65]300-59987.7[86.07-89.08]12.3[10.92-13.93]91.2[89.68-92.44]8.8[7.56-10.32]600-99988.0[86.58-89.23]12.0[10.77-13.42]89.2[87.49-90.76]10.8[9.24-12.51]1,000 or more83.5[81.72-85.19]16.5[14.81-18.28]86.9[84.57-88.88]13.1[11.12-15.43]Child SexMale85.6[84.33-86.72]14.4[13.28-15.67]88.8[87.33-90.10]11.2[9.90-12.67]Female87.9[86.98-88.83]12.1[11.17-13.02]90.0[88.83-90.99]10.0[9.01-11.17]Zip code classification by community typeCity83.5[81.91-84.99]16.5[15.01-18.09]88.1[86.06-89.80]11.9[10.20-13.94]Suburb89.7[88.40-90.79]10.3[9.21-11.60]92.1[90.99-93.10]7.9[6.90-9.01]Town85.5[82.86-87.78]14.5[12.22-17.14]85.9[82.37-88.86]14.1[11.14-17.63]Rural86.5[84.30-88.39]13.5[11.61-15.70]86.0[83.68-88.10]14.0[11.90-16.32]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: SNUMST.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: FSMEMO, SNUMST, CSEX and ZIPLOCL. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: FSMEMOSX (PFI:2012), S12NUMST (PFI:2012), CSEX (PFI:2012, PFI:2016), ZIPLOCL (PFI:2012, PFI:2016), FSMEMO (PFI:2016) and S16NUMST (PFI:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012 and National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES TrendStats on 11/29/2018.cmnbkpca8cmnbkpca82Time spent doing homework by Total school enrollment of students, Child Sex and Zip code classification by community type for years 2012 and 2016 Time spent doing homeworkStudent does homework outside of schoolStudent does not do homework outside of schoolTotalEstimatesTotal201296.04.0100%201693.96.1100%Total school enrollment of studentsUnder 300201293.26.8100%201689.011.0100%300-599201296.13.9100%201694.35.7100%600-999201296.63.4100%201695.14.9100%1,000 or more201296.53.5100%201694.35.7100%Child SexMale201295.44.6100%201692.67.4100%Female201296.63.4100%201695.44.6100%Zip code classification by community typeCity201295.84.2100%201693.16.9100%Suburb201296.73.3100%201695.44.6100%Town201296.53.5100%201691.48.6100%Rural201294.95.1100%201693.07.0100%Time spent doing homework by Total school enrollment of students, Child Sex and Zip code classification by community type for years 2012 and 2016 Time spent doing homeworkStudent does homework outside of schoolStudent does not do homework outside of schoolTotalEstimatesTotal201296.04.0100%201693.96.1100%Total school enrollment of studentsUnder 300201293.26.8100%201689.011.0100%300-599201296.13.9100%201694.35.7100%600-999201296.63.4100%201695.14.9100%1,000 or more201296.53.5100%201694.35.7100%Child SexMale201295.44.6100%201692.67.4100%Female201296.63.4100%201695.44.6100%Zip code classification by community typeCity201295.84.2100%201693.16.9100%Suburb201296.73.3100%201695.44.6100%Town201296.53.5100%201691.48.6100%Rural201294.95.1100%201693.07.0100%Standard Error (BRR)Total20120.270.27 20160.340.34 Total school enrollment of studentsUnder 30020120.880.88 20161.971.97 300-59920120.500.50 20160.480.48 600-99920120.550.55 20160.590.59 1,000 or more20120.330.33 20160.540.54 Child SexMale20120.410.41 20160.620.62 Female20120.340.34 20160.380.38 Zip code classification by community typeCity20120.400.40 20160.780.78 Suburb20120.450.45 20160.430.43 Town20120.550.55 20161.311.31 Rural20120.770.77 20160.830.83 Relative Standard Error (%)Total20120.286.69 20160.365.66 Total school enrollment of studentsUnder 30020120.9512.98 20162.2217.90 300-59920120.5212.82 20160.518.42 600-99920120.5716.41 20160.6212.14 1,000 or more20120.349.35 20160.589.50 Child SexMale20120.428.83 20160.678.41 Female20120.3610.21 20160.408.32 Zip code classification by community typeCity20120.419.53 20160.8411.41 Suburb20120.4613.53 20160.459.29 Town20120.5715.74 20161.4415.26 Rural20120.8115.13 20160.8911.71 Weighted Sample Sizes (n/1,000s)Total201252,215.3 201651,161.9 Total school enrollment of studentsUnder 30020125,897.9 20165,799.9 300-599201217,363.8 201616,952.9 600-999201215,654.7 201615,125.9 1,000 or more201213,044.9 201612,990.4 Child SexMale201226,981.8 201626,494.9 Female201225,233.4 201624,667.0 Zip code classification by community typeCity201215,446.5 201616,278.0 Suburb201219,739.4 201622,614.7 Town20124,877.9 20164,041.9 Rural201212,151.5 20168,227.3 Time spent doing homework by Total school enrollment of students, Child Sex and Zip code classification by community type for years 2012 and 2016 Time spent doing homeworkStudent does homework outside of schoolStudent does not do homework outside of schoolTotalPct.95% CIPct.95% CI EstimatesTotal201296.0[95.44-96.50]4.0[3.50-4.56]100%201693.9[93.23-94.59]6.1[5.41-6.77]100%Total school enrollment of studentsUnder 300201293.2[91.23-94.77]6.8[5.23-8.77]100%201689.0[84.38-92.32]11.0[7.68-15.62]100%300-599201296.1[94.94-96.96]3.9[3.04-5.06]100%201694.3[93.27-95.19]5.7[4.81-6.73]100%600-999201296.6[95.36-97.58]3.4[2.42-4.64]100%201695.1[93.78-96.16]4.9[3.84-6.22]100%1,000 or more201296.5[95.77-97.08]3.5[2.92-4.23]100%201694.3[93.10-95.27]5.7[4.73-6.90]100%Child SexMale201295.4[94.53-96.15]4.6[3.85-5.47]100%201692.6[91.27-93.75]7.4[6.25-8.73]100%Female201296.6[95.88-97.26]3.4[2.74-4.12]100%201695.4[94.56-96.09]4.6[3.91-5.44]100%Zip code classification by community typeCity201295.8[94.98-96.57]4.2[3.43-5.02]100%201693.1[91.39-94.53]6.9[5.47-8.61]100%Suburb201296.7[95.66-97.47]3.3[2.53-4.34]100%201695.4[94.42-96.14]4.6[3.86-5.58]100%Town201296.5[95.27-97.47]3.5[2.53-4.73]100%201691.4[88.41-93.68]8.6[6.32-11.59]100%Rural201294.9[93.12-96.23]5.1[3.77-6.88]100%201693.0[91.12-94.43]7.0[5.57-8.88]100%20122016 Time spent doing homeworkTime spent doing homework Student does homework outside of schoolStudent does not do homework outside of schoolStudent does homework outside of schoolStudent does not do homework outside of schoolEstimatesTotal96.04.093.96.1Total school enrollment of studentsUnder 30093.26.889.011.0300-59996.13.994.35.7600-99996.63.495.14.91,000 or more96.53.594.35.7Child SexMale95.44.692.67.4Female96.63.495.44.6Zip code classification by community typeCity95.84.293.16.9Suburb96.73.395.44.6Town96.53.591.48.6Rural94.95.193.07.020122016 Time spent doing homeworkTime spent doing homework Student does homework outside of schoolStudent does not do homework outside of schoolStudent does homework outside of schoolStudent does not do homework outside of schoolEstimatesTotal96.04.093.96.1Total school enrollment of studentsUnder 30093.26.889.011.0300-59996.13.994.35.7600-99996.63.495.14.91,000 or more96.53.594.35.7Child SexMale95.44.692.67.4Female96.63.495.44.6Zip code classification by community typeCity95.84.293.16.9Suburb96.73.395.44.6Town96.53.591.48.6Rural94.95.193.07.0Standard Error (BRR)Total0.270.270.340.34Total school enrollment of studentsUnder 3000.880.881.971.97300-5990.500.500.480.48600-9990.550.550.590.591,000 or more0.330.330.540.54Child SexMale0.410.410.620.62Female0.340.340.380.38Zip code classification by community typeCity0.400.400.780.78Suburb0.450.450.430.43Town0.550.551.311.31Rural0.770.770.830.83Relative Standard Error (%)Total0.286.690.365.66Total school enrollment of studentsUnder 3000.9512.982.2217.90300-5990.5212.820.518.42600-9990.5716.410.6212.141,000 or more0.349.350.589.50Child SexMale0.428.830.678.41Female0.3610.210.408.32Zip code classification by community typeCity0.419.530.8411.41Suburb0.4613.530.459.29Town0.5715.741.4415.26Rural0.8115.130.8911.71Weighted Sample Sizes (n/1,000s)Total52,215.3 51,161.9 Total school enrollment of studentsUnder 3005,897.9 5,799.9 300-59917,363.8 16,952.9 600-99915,654.7 15,125.9 1,000 or more13,044.9 12,990.4 Child SexMale26,981.8 26,494.9 Female25,233.4 24,667.0 Zip code classification by community typeCity15,446.5 16,278.0 Suburb19,739.4 22,614.7 Town4,877.9 4,041.9 Rural12,151.5 8,227.3 20122016 Time spent doing homeworkTime spent doing homework Student does homework outside of schoolStudent does not do homework outside of schoolStudent does homework outside of schoolStudent does not do homework outside of school Pct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal96.0[95.44-96.50]4.0[3.50-4.56]93.9[93.23-94.59]6.1[5.41-6.77]Total school enrollment of studentsUnder 30093.2[91.23-94.77]6.8[5.23-8.77]89.0[84.38-92.32]11.0[7.68-15.62]300-59996.1[94.94-96.96]3.9[3.04-5.06]94.3[93.27-95.19]5.7[4.81-6.73]600-99996.6[95.36-97.58]3.4[2.42-4.64]95.1[93.78-96.16]4.9[3.84-6.22]1,000 or more96.5[95.77-97.08]3.5[2.92-4.23]94.3[93.10-95.27]5.7[4.73-6.90]Child SexMale95.4[94.53-96.15]4.6[3.85-5.47]92.6[91.27-93.75]7.4[6.25-8.73]Female96.6[95.88-97.26]3.4[2.74-4.12]95.4[94.56-96.09]4.6[3.91-5.44]Zip code classification by community typeCity95.8[94.98-96.57]4.2[3.43-5.02]93.1[91.39-94.53]6.9[5.47-8.61]Suburb96.7[95.66-97.47]3.3[2.53-4.34]95.4[94.42-96.14]4.6[3.86-5.58]Town96.5[95.27-97.47]3.5[2.53-4.73]91.4[88.41-93.68]8.6[6.32-11.59]Rural94.9[93.12-96.23]5.1[3.77-6.88]93.0[91.12-94.43]7.0[5.57-8.88]STDERR-SOURCE-ENDCONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: SNUMST.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: FHHOME, SNUMST, CSEX and ZIPLOCL. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: FHHOME (PFI:2012, PFI:2016), S12NUMST (PFI:2012), CSEX (PFI:2012, PFI:2016), ZIPLOCL (PFI:2012, PFI:2016) and S16NUMST (PFI:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012 and National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES TrendStats on 11/29/2018.cmnbkpebcmnbkpeb3Satisfaction with teachers by Child Sex, Zip code classification by community type and Detailed race and ethnicity of child for years 2012 and 2016 Satisfaction with teachersVery satisfiedSomewhat satisfiedSomewhat dissatisfiedVery dissatisfiedTotalEstimatesTotal201259.532.46.31.7100%201660.931.85.71.5100%Child SexMale201259.232.26.81.8100%201660.531.56.31.7100%Female201259.832.75.81.7100%201661.432.25.11.3100%Zip code classification by community typeCity201259.531.76.82.0100%201660.532.15.81.7100%Suburb201259.733.35.51.4100%201661.831.06.01.3100%Town201261.429.66.82.2100%201660.032.75.41.9100%Rural201258.532.96.71.9100%201660.033.15.21.6100%Detailed race and ethnicity of childWhite, non-Hispanic201261.531.35.51.7100%201663.929.35.31.5100%Black, non-Hispanic201252.236.88.92.1100%201651.738.96.72.7100%Hispanic201260.531.06.71.8100%201661.131.96.20.8100%Asian or Pacific Islander, non-Hispanic201258.135.36.00.6 !100%201659.635.24.50.7 !100%All other races and multiple races, non-Hispanic201257.334.55.82.3100%201658.432.26.92.5 !100%Satisfaction with teachers by Child Sex, Zip code classification by community type and Detailed race and ethnicity of child for years 2012 and 2016 Satisfaction with teachersVery satisfiedSomewhat satisfiedSomewhat dissatisfiedVery dissatisfiedTotalEstimatesTotal201259.532.46.31.7100%201660.931.85.71.5100%Child SexMale201259.232.26.81.8100%201660.531.56.31.7100%Female201259.832.75.81.7100%201661.432.25.11.3100%Zip code classification by community typeCity201259.531.76.82.0100%201660.532.15.81.7100%Suburb201259.733.35.51.4100%201661.831.06.01.3100%Town201261.429.66.82.2100%201660.032.75.41.9100%Rural201258.532.96.71.9100%201660.033.15.21.6100%Detailed race and ethnicity of childWhite, non-Hispanic201261.531.35.51.7100%201663.929.35.31.5100%Black, non-Hispanic201252.236.88.92.1100%201651.738.96.72.7100%Hispanic201260.531.06.71.8100%201661.131.96.20.8100%Asian or Pacific Islander, non-Hispanic201258.135.36.00.6 !100%201659.635.24.50.7 !100%All other races and multiple races, non-Hispanic201257.334.55.82.3100%201658.432.26.92.5 !100%Standard Error (BRR)Total20120.520.500.270.14 20160.590.620.290.16 Child SexMale20120.680.630.370.17 20160.810.830.500.23 Female20120.930.840.350.24 20160.920.930.300.21 Zip code classification by community typeCity20121.020.980.550.27 20161.371.220.590.29 Suburb20120.880.830.400.18 20160.910.970.460.23 Town20121.931.611.010.49 20162.112.320.880.52 Rural20121.040.960.600.29 20161.691.560.570.34 Detailed race and ethnicity of childWhite, non-Hispanic20120.670.610.300.21 20160.790.800.370.22 Black, non-Hispanic20121.831.660.950.38 20162.172.060.820.70 Hispanic20121.281.080.650.33 20161.451.370.730.17 Asian or Pacific Islander, non-Hispanic20122.342.280.980.27 20163.102.850.860.32 All other races and multiple races, non-Hispanic20122.662.540.730.58 20162.612.091.170.75 Relative Standard Error (%)Total20120.881.534.208.09 20160.971.945.0710.42 Child SexMale20121.151.965.469.52 20161.332.637.8513.40 Female20121.552.576.0414.29 20161.492.885.8816.49 Zip code classification by community typeCity20121.713.088.1113.90 20162.263.8010.2717.49 Suburb20121.482.507.1913.40 20161.473.127.6718.50 Town20123.145.4514.8522.56 20163.527.1016.2328.12 Rural20121.792.928.9615.73 20162.824.7010.9420.53 Detailed race and ethnicity of childWhite, non-Hispanic20121.081.955.3912.20 20161.242.716.9515.09 Black, non-Hispanic20123.514.5010.6018.46 20164.205.2912.2825.90 Hispanic20122.113.479.6918.62 20162.374.2811.9019.91 Asian or Pacific Islander, non-Hispanic20124.026.4616.5442.64 20165.218.0819.1946.14 All other races and multiple races, non-Hispanic20124.647.3712.5325.16 20164.476.5017.1130.28 Weighted Sample Sizes (n/1,000s)Total201252,215.3 201651,161.9 Child SexMale201226,981.8 201626,494.9 Female201225,233.4 201624,667.0 Zip code classification by community typeCity201215,446.5 201616,278.0 Suburb201219,739.4 201622,614.7 Town20124,877.9 20164,041.9 Rural201212,151.5 20168,227.3 Detailed race and ethnicity of childWhite, non-Hispanic201226,910.3 201625,702.7 Black, non-Hispanic20127,464.2 20167,139.1 Hispanic201212,112.8 201612,281.0 Asian or Pacific Islander, non-Hispanic20122,886.0 20163,198.9 All other races and multiple races, non-Hispanic20122,842.0 20162,840.2 Satisfaction with teachers by Child Sex, Zip code classification by community type and Detailed race and ethnicity of child for years 2012 and 2016 Satisfaction with teachersVery satisfiedSomewhat satisfiedSomewhat dissatisfiedVery dissatisfiedTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal201259.5[58.47-60.55]32.4[31.44-33.41]6.3[5.81-6.87]1.7[1.48-2.04]100%201660.9[59.74-62.10]31.8[30.61-33.06]5.7[5.19-6.35]1.5[1.22-1.85]100%Child SexMale201259.2[57.84-60.56]32.2[30.92-33.44]6.8[6.13-7.62]1.8[1.47-2.15]100%201660.5[58.86-62.07]31.5[29.87-33.16]6.3[5.41-7.39]1.7[1.30-2.22]100%Female201259.8[57.98-61.68]32.7[31.04-34.38]5.8[5.11-6.50]1.7[1.28-2.25]100%201661.4[59.58-63.22]32.2[30.37-34.05]5.1[4.55-5.75]1.3[0.93-1.79]100%Zip code classification by community typeCity201259.5[57.42-61.46]31.7[29.83-33.72]6.8[5.80-8.01]2.0[1.50-2.60]100%201660.5[57.72-63.15]32.1[29.71-34.56]5.8[4.69-7.06]1.7[1.18-2.38]100%Suburb201259.7[57.98-61.49]33.3[31.70-35.02]5.5[4.81-6.40]1.4[1.05-1.79]100%201661.8[59.94-63.54]31.0[29.12-32.96]6.0[5.13-6.96]1.3[0.87-1.82]100%Town201261.4[57.54-65.20]29.6[26.49-32.90]6.8[5.05-9.11]2.2[1.38-3.38]100%201660.0[55.72-64.11]32.7[28.27-37.50]5.4[3.93-7.49]1.9[1.06-3.23]100%Rural201258.5[56.36-60.51]32.9[31.04-34.87]6.7[5.64-8.05]1.9[1.37-2.56]100%201660.0[56.61-63.35]33.1[30.10-36.29]5.2[4.19-6.47]1.6[1.09-2.47]100%Detailed race and ethnicity of childWhite, non-Hispanic201261.5[60.14-62.79]31.3[30.10-32.53]5.5[4.97-6.16]1.7[1.32-2.15]100%201663.9[62.27-65.43]29.3[27.78-30.95]5.3[4.63-6.10]1.5[1.09-1.99]100%Black, non-Hispanic201252.2[48.54-55.81]36.8[33.60-40.19]8.9[7.21-10.99]2.1[1.43-2.98]100%201651.7[47.39-56.01]38.9[34.91-43.10]6.7[5.20-8.48]2.7[1.61-4.51]100%Hispanic201260.5[57.98-63.05]31.0[28.91-33.19]6.7[5.49-8.07]1.8[1.23-2.58]100%201661.1[58.14-63.91]31.9[29.26-34.69]6.2[4.86-7.81]0.8[0.57-1.25]100%Asian or Pacific Islander, non-Hispanic201258.1[53.42-62.71]35.3[30.89-39.95]6.0[4.27-8.24]0.6 ![0.27-1.45]100%201659.6[53.30-65.60]35.2[29.78-41.06]4.5[3.05-6.54]0.7 ![0.28-1.75]100%All other races and multiple races, non-Hispanic201257.3[51.96-62.52]34.5[29.65-39.74]5.8[4.54-7.47]2.3[1.40-3.81]100%201658.4[53.17-63.52]32.2[28.20-36.52]6.9[4.86-9.60]2.5 ![1.35-4.51]100%20122016 Satisfaction with teachersSatisfaction with teachers Very satisfiedSomewhat satisfiedSomewhat dissatisfiedVery dissatisfiedVery satisfiedSomewhat satisfiedSomewhat dissatisfiedVery dissatisfiedEstimatesTotal59.532.46.31.760.931.85.71.5Child SexMale59.232.26.81.860.531.56.31.7Female59.832.75.81.761.432.25.11.3Zip code classification by community typeCity59.531.76.82.060.532.15.81.7Suburb59.733.35.51.461.831.06.01.3Town61.429.66.82.260.032.75.41.9Rural58.532.96.71.960.033.15.21.6Detailed race and ethnicity of childWhite, non-Hispanic61.531.35.51.763.929.35.31.5Black, non-Hispanic52.236.88.92.151.738.96.72.7Hispanic60.531.06.71.861.131.96.20.8Asian or Pacific Islander, non-Hispanic58.135.36.00.659.635.24.50.7All other races and multiple races, non-Hispanic57.334.55.82.358.432.26.92.520122016 Satisfaction with teachersSatisfaction with teachers Very satisfiedSomewhat satisfiedSomewhat dissatisfiedVery dissatisfiedVery satisfiedSomewhat satisfiedSomewhat dissatisfiedVery dissatisfiedEstimatesTotal59.532.46.31.760.931.85.71.5Child SexMale59.232.26.81.860.531.56.31.7Female59.832.75.81.761.432.25.11.3Zip code classification by community typeCity59.531.76.82.060.532.15.81.7Suburb59.733.35.51.461.831.06.01.3Town61.429.66.82.260.032.75.41.9Rural58.532.96.71.960.033.15.21.6Detailed race and ethnicity of childWhite, non-Hispanic61.531.35.51.763.929.35.31.5Black, non-Hispanic52.236.88.92.151.738.96.72.7Hispanic60.531.06.71.861.131.96.20.8Asian or Pacific Islander, non-Hispanic58.135.36.00.659.635.24.50.7All other races and multiple races, non-Hispanic57.334.55.82.358.432.26.92.5Standard Error (BRR)Total0.520.500.270.140.590.620.290.16Child SexMale0.680.630.370.170.810.830.500.23Female0.930.840.350.240.920.930.300.21Zip code classification by community typeCity1.020.980.550.271.371.220.590.29Suburb0.880.830.400.180.910.970.460.23Town1.931.611.010.492.112.320.880.52Rural1.040.960.600.291.691.560.570.34Detailed race and ethnicity of childWhite, non-Hispanic0.670.610.300.210.790.800.370.22Black, non-Hispanic1.831.660.950.382.172.060.820.70Hispanic1.281.080.650.331.451.370.730.17Asian or Pacific Islander, non-Hispanic2.342.280.980.273.102.850.860.32All other races and multiple races, non-Hispanic2.662.540.730.582.612.091.170.75Relative Standard Error (%)Total0.881.534.208.090.971.945.0710.42Child SexMale1.151.965.469.521.332.637.8513.40Female1.552.576.0414.291.492.885.8816.49Zip code classification by community typeCity1.713.088.1113.902.263.8010.2717.49Suburb1.482.507.1913.401.473.127.6718.50Town3.145.4514.8522.563.527.1016.2328.12Rural1.792.928.9615.732.824.7010.9420.53Detailed race and ethnicity of childWhite, non-Hispanic1.081.955.3912.201.242.716.9515.09Black, non-Hispanic3.514.5010.6018.464.205.2912.2825.90Hispanic2.113.479.6918.622.374.2811.9019.91Asian or Pacific Islander, non-Hispanic4.026.4616.5442.645.218.0819.1946.14All other races and multiple races, non-Hispanic4.647.3712.5325.164.476.5017.1130.28Weighted Sample Sizes (n/1,000s)Total52,215.3 51,161.9 Child SexMale26,981.8 26,494.9 Female25,233.4 24,667.0 Zip code classification by community typeCity15,446.5 16,278.0 Suburb19,739.4 22,614.7 Town4,877.9 4,041.9 Rural12,151.5 8,227.3 Detailed race and ethnicity of childWhite, non-Hispanic26,910.3 25,702.7 Black, non-Hispanic7,464.2 7,139.1 Hispanic12,112.8 12,281.0 Asian or Pacific Islander, non-Hispanic2,886.0 3,198.9 All other races and multiple races, non-Hispanic2,842.0 2,840.2 20122016 Satisfaction with teachersSatisfaction with teachers Very satisfiedSomewhat satisfiedSomewhat dissatisfiedVery dissatisfiedVery satisfiedSomewhat satisfiedSomewhat dissatisfiedVery dissatisfied Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal59.5[58.47-60.55]32.4[31.44-33.41]6.3[5.81-6.87]1.7[1.48-2.04]60.9[59.74-62.10]31.8[30.61-33.06]5.7[5.19-6.35]1.5[1.22-1.85]Child SexMale59.2[57.84-60.56]32.2[30.92-33.44]6.8[6.13-7.62]1.8[1.47-2.15]60.5[58.86-62.07]31.5[29.87-33.16]6.3[5.41-7.39]1.7[1.30-2.22]Female59.8[57.98-61.68]32.7[31.04-34.38]5.8[5.11-6.50]1.7[1.28-2.25]61.4[59.58-63.22]32.2[30.37-34.05]5.1[4.55-5.75]1.3[0.93-1.79]Zip code classification by community typeCity59.5[57.42-61.46]31.7[29.83-33.72]6.8[5.80-8.01]2.0[1.50-2.60]60.5[57.72-63.15]32.1[29.71-34.56]5.8[4.69-7.06]1.7[1.18-2.38]Suburb59.7[57.98-61.49]33.3[31.70-35.02]5.5[4.81-6.40]1.4[1.05-1.79]61.8[59.94-63.54]31.0[29.12-32.96]6.0[5.13-6.96]1.3[0.87-1.82]Town61.4[57.54-65.20]29.6[26.49-32.90]6.8[5.05-9.11]2.2[1.38-3.38]60.0[55.72-64.11]32.7[28.27-37.50]5.4[3.93-7.49]1.9[1.06-3.23]Rural58.5[56.36-60.51]32.9[31.04-34.87]6.7[5.64-8.05]1.9[1.37-2.56]60.0[56.61-63.35]33.1[30.10-36.29]5.2[4.19-6.47]1.6[1.09-2.47]Detailed race and ethnicity of childWhite, non-Hispanic61.5[60.14-62.79]31.3[30.10-32.53]5.5[4.97-6.16]1.7[1.32-2.15]63.9[62.27-65.43]29.3[27.78-30.95]5.3[4.63-6.10]1.5[1.09-1.99]Black, non-Hispanic52.2[48.54-55.81]36.8[33.60-40.19]8.9[7.21-10.99]2.1[1.43-2.98]51.7[47.39-56.01]38.9[34.91-43.10]6.7[5.20-8.48]2.7[1.61-4.51]Hispanic60.5[57.98-63.05]31.0[28.91-33.19]6.7[5.49-8.07]1.8[1.23-2.58]61.1[58.14-63.91]31.9[29.26-34.69]6.2[4.86-7.81]0.8[0.57-1.25]Asian or Pacific Islander, non-Hispanic58.1[53.42-62.71]35.3[30.89-39.95]6.0[4.27-8.24]0.6 ![0.27-1.45]59.6[53.30-65.60]35.2[29.78-41.06]4.5[3.05-6.54]0.7 ![0.28-1.75]All other races and multiple races, non-Hispanic57.3[51.96-62.52]34.5[29.65-39.74]5.8[4.54-7.47]2.3[1.40-3.81]58.4[53.17-63.52]32.2[28.20-36.52]6.9[4.86-9.60]2.5 ![1.35-4.51]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.CONFINT-SOURCE-ENDFor TrendStats the names of the variables used in this table are: FCTEACHR, CSEX, ZIPLOCL and RACEETH2. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: FCTEACHR (PFI:2012, PFI:2016), CSEX (PFI:2012, PFI:2016), ZIPLOCL (PFI:2012, PFI:2016) and RACEETH2 (PFI:2012, PFI:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012 and National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES TrendStats on 11/29/2018.cmnbkp66cmnbkp664Expectations for child's future education by Child Sex, Zip code classification by community type and Detailed race and ethnicity of child, for [Current grade or year of school (Sixth grade,Seventh grade,Eighth grade,Ninth grade,Tenth grade,Eleventh grade,Twelfth grade)] for years 2012 and 2016 Expectations for child's future educationComplete less than a high school diplomaGraduate from high schoolAttend a vocational or technical school after high schoolAttend two or more years of collegeEarn a Bachelor’s degreeEarn a graduate degree or professional degree beyond a Bachelor’sTotalEstimatesTotal20121.19.08.317.527.736.5100%20161.18.67.614.828.939.0100%Child SexMale20121.211.411.017.027.631.7100%20161.49.610.415.029.334.3100%Female20121.06.45.417.927.841.6100%20160.87.64.614.528.444.0100%Zip code classification by community typeCity20121.410.16.616.425.440.0100%20161.68.86.413.428.841.0100%Suburb20120.7 !7.26.516.630.338.8100%20160.86.76.012.830.243.5100%Town20121.0 !11.113.319.226.529.0100%20160.8 !!10.311.323.722.031.9100%Rural20121.5 !9.611.319.626.831.1100%20161.412.812.618.328.626.2100%Detailed race and ethnicity of childWhite, non-Hispanic20121.08.58.818.230.832.6100%20161.48.68.916.032.133.1100%Black, non-Hispanic20121.5 !12.88.419.718.039.6100%20161.2 !13.99.014.518.343.2100%Hispanic20121.28.18.815.826.639.6100%20160.7 !7.35.913.827.245.1100%Asian or Pacific Islander, non-Hispanic20121.0 !4.2 !1.8 !9.428.255.5100%20160.1 !!2.0 !0.8 !!6.933.756.5100%All other races and multiple races, non-Hispanic20120.4 !!12.0 !6.219.026.835.6100%20161.3 !!8.46.215.628.140.3100%Expectations for child's future education by Child Sex, Zip code classification by community type and Detailed race and ethnicity of child, for [Current grade or year of school (Sixth grade,Seventh grade,Eighth grade,Ninth grade,Tenth grade,Eleventh grade,Twelfth grade)] for years 2012 and 2016 Expectations for child's future educationComplete less than a high school diplomaGraduate from high schoolAttend a vocational or technical school after high schoolAttend two or more years of collegeEarn a Bachelor’s degreeEarn a graduate degree or professional degree beyond a Bachelor’sTotalEstimatesTotal20121.19.08.317.527.736.5100%20161.18.67.614.828.939.0100%Child SexMale20121.211.411.017.027.631.7100%20161.49.610.415.029.334.3100%Female20121.06.45.417.927.841.6100%20160.87.64.614.528.444.0100%Zip code classification by community typeCity20121.410.16.616.425.440.0100%20161.68.86.413.428.841.0100%Suburb20120.7 !7.26.516.630.338.8100%20160.86.76.012.830.243.5100%Town20121.0 !11.113.319.226.529.0100%20160.8 !!10.311.323.722.031.9100%Rural20121.5 !9.611.319.626.831.1100%20161.412.812.618.328.626.2100%Detailed race and ethnicity of childWhite, non-Hispanic20121.08.58.818.230.832.6100%20161.48.68.916.032.133.1100%Black, non-Hispanic20121.5 !12.88.419.718.039.6100%20161.2 !13.99.014.518.343.2100%Hispanic20121.28.18.815.826.639.6100%20160.7 !7.35.913.827.245.1100%Asian or Pacific Islander, non-Hispanic20121.0 !4.2 !1.8 !9.428.255.5100%20160.1 !!2.0 !0.8 !!6.933.756.5100%All other races and multiple races, non-Hispanic20120.4 !!12.0 !6.219.026.835.6100%20161.3 !!8.46.215.628.140.3100%Standard Error (BRR)Total20120.160.560.470.590.600.79 20160.170.580.440.520.660.80 Child SexMale20120.250.870.760.830.830.90 20160.290.910.700.800.881.11 Female20120.200.470.420.780.831.19 20160.190.750.430.781.121.21 Zip code classification by community typeCity20120.320.950.741.071.081.32 20160.441.240.751.071.281.68 Suburb20120.220.850.730.781.011.22 20160.190.750.740.740.951.19 Town20120.321.941.431.772.021.99 20160.451.541.493.122.332.54 Rural20120.491.271.041.301.291.29 20160.431.411.171.421.441.48 Detailed race and ethnicity of childWhite, non-Hispanic20120.250.600.450.760.830.82 20160.280.670.560.820.871.00 Black, non-Hispanic20120.512.021.161.761.271.93 20160.502.161.361.301.712.46 Hispanic20120.360.991.200.971.381.84 20160.281.261.061.331.561.93 Asian or Pacific Islander, non-Hispanic20120.441.270.611.693.093.09 20160.130.800.491.823.183.43 All other races and multiple races, non-Hispanic20120.313.751.092.802.842.93 20160.781.981.332.552.633.92 Relative Standard Error (%)Total201214.516.275.723.382.162.17 201615.116.735.753.532.302.04 Child SexMale201220.247.646.914.883.002.84 201620.139.516.675.353.003.23 Female201220.147.347.864.373.002.87 201623.909.849.445.343.922.76 Zip code classification by community typeCity201222.479.4311.166.504.243.29 201627.4714.0511.648.004.444.09 Suburb201233.6611.8111.224.743.323.16 201624.8211.0912.485.743.142.74 Town201233.1717.5210.779.247.646.87 201659.0115.0113.1813.1510.557.97 Rural201232.1213.179.166.644.814.15 201629.7310.989.277.765.045.63 Detailed race and ethnicity of childWhite, non-Hispanic201224.417.015.134.162.702.52 201620.007.766.355.112.713.02 Black, non-Hispanic201233.2515.7813.808.907.054.89 201642.6715.5815.128.949.385.70 Hispanic201228.9312.1913.736.185.214.66 201638.9317.2517.949.695.744.28 Asian or Pacific Islander, non-Hispanic201245.1130.3834.3218.0810.985.57 2016100.3640.8559.2826.189.466.07 All other races and multiple races, non-Hispanic201272.3631.2617.4514.7510.618.23 201657.7623.6421.5416.319.359.73 Weighted Sample Sizes (n/1,000s)Total201226,350.2 201626,032.5 Child SexMale201213,578.6 201613,538.9 Female201212,771.7 201612,493.6 Zip code classification by community typeCity20127,757.8 20167,881.7 Suburb201210,079.6 201611,788.5 Town20122,391.2 20162,029.1 Rural20126,121.6 20164,333.1 Detailed race and ethnicity of childWhite, non-Hispanic201214,072.4 201613,482.9 Black, non-Hispanic20123,738.1 20163,656.6 Hispanic20125,932.7 20166,074.7 Asian or Pacific Islander, non-Hispanic20121,346.3 20161,440.5 All other races and multiple races, non-Hispanic20121,260.8 20161,377.8 Expectations for child's future education by Child Sex, Zip code classification by community type and Detailed race and ethnicity of child, for [Current grade or year of school (Sixth grade,Seventh grade,Eighth grade,Ninth grade,Tenth grade,Eleventh grade,Twelfth grade)] for years 2012 and 2016 Expectations for child's future educationComplete less than a high school diplomaGraduate from high schoolAttend a vocational or technical school after high schoolAttend two or more years of collegeEarn a Bachelor’s degreeEarn a graduate degree or professional degree beyond a Bachelor’sTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal20121.1[0.84-1.49]9.0[7.92-10.17]8.3[7.38-9.27]17.5[16.32-18.67]27.7[26.52-28.89]36.5[34.91-38.06]100%20161.1[0.84-1.53]8.6[7.56-9.88]7.6[6.80-8.55]14.8[13.75-15.82]28.9[27.56-30.21]39.0[37.40-40.56]100%Child SexMale20121.2[0.83-1.85]11.4[9.81-13.30]11.0[9.58-12.62]17.0[15.44-18.74]27.6[25.99-29.29]31.7[29.91-33.49]100%20161.4[0.96-2.14]9.6[7.93-11.58]10.4[9.14-11.91]15.0[13.43-16.61]29.3[27.54-31.04]34.3[32.13-36.55]100%Female20121.0[0.66-1.48]6.4[5.50-7.36]5.4[4.60-6.28]17.9[16.42-19.54]27.8[26.14-29.45]41.6[39.22-43.96]100%20160.8[0.50-1.29]7.6[6.25-9.24]4.6[3.79-5.52]14.5[13.07-16.16]28.4[26.28-30.72]44.0[41.62-46.45]100%Zip code classification by community typeCity20121.4[0.92-2.24]10.1[8.35-12.16]6.6[5.30-8.26]16.4[14.42-18.68]25.4[23.30-27.58]40.0[37.43-42.66]100%20161.6[0.92-2.73]8.8[6.62-11.58]6.4[5.09-8.08]13.4[11.43-15.71]28.8[26.28-31.36]41.0[37.73-44.40]100%Suburb20120.7 ![0.34-1.29]7.2[5.70-9.11]6.5[5.21-8.14]16.6[15.05-18.18]30.3[28.31-32.31]38.8[36.37-41.23]100%20160.8[0.48-1.28]6.7[5.40-8.39]6.0[4.64-7.62]12.8[11.42-14.35]30.2[28.36-32.14]43.5[41.14-45.88]100%Town20121.0 ![0.50-1.86]11.1[7.76-15.55]13.3[10.70-16.43]19.2[15.91-22.97]26.5[22.65-30.68]29.0[25.20-33.12]100%20160.8 !![0.23-2.45]10.3[7.60-13.79]11.3[8.69-14.67]23.7[18.07-30.47]22.0[17.75-27.00]31.9[27.03-37.11]100%Rural20121.5 ![0.81-2.90]9.6[7.40-12.48]11.3[9.40-13.53]19.6[17.12-22.30]26.8[24.35-29.48]31.1[28.59-33.72]100%20161.4[0.79-2.59]12.8[10.25-15.86]12.6[10.48-15.15]18.3[15.62-21.27]28.6[25.83-31.57]26.2[23.41-29.29]100%Detailed race and ethnicity of childWhite, non-Hispanic20121.0[0.64-1.69]8.5[7.41-9.79]8.8[7.98-9.79]18.2[16.76-19.78]30.8[29.15-32.46]32.6[30.98-34.24]100%20161.4[0.93-2.07]8.6[7.34-9.99]8.9[7.83-10.08]16.0[14.45-17.71]32.1[30.36-33.81]33.1[31.11-35.08]100%Black, non-Hispanic20121.5 ![0.79-2.95]12.8[9.28-17.36]8.4[6.37-11.03]19.7[16.47-23.46]18.0[15.59-20.64]39.6[35.79-43.48]100%20161.2 ![0.50-2.75]13.9[10.09-18.74]9.0[6.64-12.11]14.5[12.10-17.27]18.3[15.10-21.93]43.2[38.37-48.13]100%Hispanic20121.2[0.69-2.18]8.1[6.34-10.30]8.8[6.65-11.48]15.8[13.92-17.80]26.6[23.89-29.40]39.6[35.97-43.30]100%20160.7 ![0.33-1.55]7.3[5.17-10.27]5.9[4.14-8.44]13.8[11.32-16.65]27.2[24.19-30.39]45.1[41.27-48.94]100%Asian or Pacific Islander, non-Hispanic20121.0 ![0.40-2.38]4.2 ![2.26-7.55]1.8 ![0.89-3.49]9.4[6.50-13.33]28.2[22.44-34.71]55.5[49.33-61.58]100%20160.1 !![0.02-0.95]2.0 ![0.86-4.38]0.8 !![0.25-2.68]6.9[4.08-11.54]33.7[27.65-40.27]56.5[49.58-63.13]100%All other races and multiple races, non-Hispanic20120.4 !![0.10-1.80]12.0 ![6.30-21.66]6.2[4.39-8.78]19.0[14.02-25.17]26.8[21.50-32.77]35.6[30.01-41.62]100%20161.3 !![0.42-4.20]8.4[5.19-13.26]6.2[4.01-9.45]15.6[11.19-21.39]28.1[23.21-33.66]40.3[32.81-48.29]100%20122016 Expectations for child's future educationExpectations for child's future education Complete less than a high school diplomaGraduate from high schoolAttend a vocational or technical school after high schoolAttend two or more years of collegeEarn a Bachelor’s degreeEarn a graduate degree or professional degree beyond a Bachelor’sComplete less than a high school diplomaGraduate from high schoolAttend a vocational or technical school after high schoolAttend two or more years of collegeEarn a Bachelor’s degreeEarn a graduate degree or professional degree beyond a Bachelor’sEstimatesTotal1.19.08.317.527.736.51.18.67.614.828.939.0Child SexMale1.211.411.017.027.631.71.49.610.415.029.334.3Female1.06.45.417.927.841.60.87.64.614.528.444.0Zip code classification by community typeCity1.410.16.616.425.440.01.68.86.413.428.841.0Suburb0.77.26.516.630.338.80.86.76.012.830.243.5Town1.011.113.319.226.529.00.810.311.323.722.031.9Rural1.59.611.319.626.831.11.412.812.618.328.626.2Detailed race and ethnicity of childWhite, non-Hispanic1.08.58.818.230.832.61.48.68.916.032.133.1Black, non-Hispanic1.512.88.419.718.039.61.213.99.014.518.343.2Hispanic1.28.18.815.826.639.60.77.35.913.827.245.1Asian or Pacific Islander, non-Hispanic1.04.21.89.428.255.50.12.00.86.933.756.5All other races and multiple races, non-Hispanic0.412.06.219.026.835.61.38.46.215.628.140.320122016 Expectations for child's future educationExpectations for child's future education Complete less than a high school diplomaGraduate from high schoolAttend a vocational or technical school after high schoolAttend two or more years of collegeEarn a Bachelor’s degreeEarn a graduate degree or professional degree beyond a Bachelor’sComplete less than a high school diplomaGraduate from high schoolAttend a vocational or technical school after high schoolAttend two or more years of collegeEarn a Bachelor’s degreeEarn a graduate degree or professional degree beyond a Bachelor’sEstimatesTotal1.19.08.317.527.736.51.18.67.614.828.939.0Child SexMale1.211.411.017.027.631.71.49.610.415.029.334.3Female1.06.45.417.927.841.60.87.64.614.528.444.0Zip code classification by community typeCity1.410.16.616.425.440.01.68.86.413.428.841.0Suburb0.77.26.516.630.338.80.86.76.012.830.243.5Town1.011.113.319.226.529.00.810.311.323.722.031.9Rural1.59.611.319.626.831.11.412.812.618.328.626.2Detailed race and ethnicity of childWhite, non-Hispanic1.08.58.818.230.832.61.48.68.916.032.133.1Black, non-Hispanic1.512.88.419.718.039.61.213.99.014.518.343.2Hispanic1.28.18.815.826.639.60.77.35.913.827.245.1Asian or Pacific Islander, non-Hispanic1.04.21.89.428.255.50.12.00.86.933.756.5All other races and multiple races, non-Hispanic0.412.06.219.026.835.61.38.46.215.628.140.3Standard Error (BRR)Total0.160.560.470.590.600.790.170.580.440.520.660.80Child SexMale0.250.870.760.830.830.900.290.910.700.800.881.11Female0.200.470.420.780.831.190.190.750.430.781.121.21Zip code classification by community typeCity0.320.950.741.071.081.320.441.240.751.071.281.68Suburb0.220.850.730.781.011.220.190.750.740.740.951.19Town0.321.941.431.772.021.990.451.541.493.122.332.54Rural0.491.271.041.301.291.290.431.411.171.421.441.48Detailed race and ethnicity of childWhite, non-Hispanic0.250.600.450.760.830.820.280.670.560.820.871.00Black, non-Hispanic0.512.021.161.761.271.930.502.161.361.301.712.46Hispanic0.360.991.200.971.381.840.281.261.061.331.561.93Asian or Pacific Islander, non-Hispanic0.441.270.611.693.093.090.130.800.491.823.183.43All other races and multiple races, non-Hispanic0.313.751.092.802.842.930.781.981.332.552.633.92Relative Standard Error (%)Total14.516.275.723.382.162.1715.116.735.753.532.302.04Child SexMale20.247.646.914.883.002.8420.139.516.675.353.003.23Female20.147.347.864.373.002.8723.909.849.445.343.922.76Zip code classification by community typeCity22.479.4311.166.504.243.2927.4714.0511.648.004.444.09Suburb33.6611.8111.224.743.323.1624.8211.0912.485.743.142.74Town33.1717.5210.779.247.646.8759.0115.0113.1813.1510.557.97Rural32.1213.179.166.644.814.1529.7310.989.277.765.045.63Detailed race and ethnicity of childWhite, non-Hispanic24.417.015.134.162.702.5220.007.766.355.112.713.02Black, non-Hispanic33.2515.7813.808.907.054.8942.6715.5815.128.949.385.70Hispanic28.9312.1913.736.185.214.6638.9317.2517.949.695.744.28Asian or Pacific Islander, non-Hispanic45.1130.3834.3218.0810.985.57100.3640.8559.2826.189.466.07All other races and multiple races, non-Hispanic72.3631.2617.4514.7510.618.2357.7623.6421.5416.319.359.73Weighted Sample Sizes (n/1,000s)Total26,350.2 26,032.5 Child SexMale13,578.6 13,538.9 Female12,771.7 12,493.6 Zip code classification by community typeCity7,757.8 7,881.7 Suburb10,079.6 11,788.5 Town2,391.2 2,029.1 Rural6,121.6 4,333.1 Detailed race and ethnicity of childWhite, non-Hispanic14,072.4 13,482.9 Black, non-Hispanic3,738.1 3,656.6 Hispanic5,932.7 6,074.7 Asian or Pacific Islander, non-Hispanic1,346.3 1,440.5 All other races and multiple races, non-Hispanic1,260.8 1,377.8 20122016 Expectations for child's future educationExpectations for child's future education Complete less than a high school diplomaGraduate from high schoolAttend a vocational or technical school after high schoolAttend two or more years of collegeEarn a Bachelor’s degreeEarn a graduate degree or professional degree beyond a Bachelor’sComplete less than a high school diplomaGraduate from high schoolAttend a vocational or technical school after high schoolAttend two or more years of collegeEarn a Bachelor’s degreeEarn a graduate degree or professional degree beyond a Bachelor’s Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal1.1[0.84-1.49]9.0[7.92-10.17]8.3[7.38-9.27]17.5[16.32-18.67]27.7[26.52-28.89]36.5[34.91-38.06]1.1[0.84-1.53]8.6[7.56-9.88]7.6[6.80-8.55]14.8[13.75-15.82]28.9[27.56-30.21]39.0[37.40-40.56]Child SexMale1.2[0.83-1.85]11.4[9.81-13.30]11.0[9.58-12.62]17.0[15.44-18.74]27.6[25.99-29.29]31.7[29.91-33.49]1.4[0.96-2.14]9.6[7.93-11.58]10.4[9.14-11.91]15.0[13.43-16.61]29.3[27.54-31.04]34.3[32.13-36.55]Female1.0[0.66-1.48]6.4[5.50-7.36]5.4[4.60-6.28]17.9[16.42-19.54]27.8[26.14-29.45]41.6[39.22-43.96]0.8[0.50-1.29]7.6[6.25-9.24]4.6[3.79-5.52]14.5[13.07-16.16]28.4[26.28-30.72]44.0[41.62-46.45]Zip code classification by community typeCity1.4[0.92-2.24]10.1[8.35-12.16]6.6[5.30-8.26]16.4[14.42-18.68]25.4[23.30-27.58]40.0[37.43-42.66]1.6[0.92-2.73]8.8[6.62-11.58]6.4[5.09-8.08]13.4[11.43-15.71]28.8[26.28-31.36]41.0[37.73-44.40]Suburb0.7 ![0.34-1.29]7.2[5.70-9.11]6.5[5.21-8.14]16.6[15.05-18.18]30.3[28.31-32.31]38.8[36.37-41.23]0.8[0.48-1.28]6.7[5.40-8.39]6.0[4.64-7.62]12.8[11.42-14.35]30.2[28.36-32.14]43.5[41.14-45.88]Town1.0 ![0.50-1.86]11.1[7.76-15.55]13.3[10.70-16.43]19.2[15.91-22.97]26.5[22.65-30.68]29.0[25.20-33.12]0.8 !![0.23-2.45]10.3[7.60-13.79]11.3[8.69-14.67]23.7[18.07-30.47]22.0[17.75-27.00]31.9[27.03-37.11]Rural1.5 ![0.81-2.90]9.6[7.40-12.48]11.3[9.40-13.53]19.6[17.12-22.30]26.8[24.35-29.48]31.1[28.59-33.72]1.4[0.79-2.59]12.8[10.25-15.86]12.6[10.48-15.15]18.3[15.62-21.27]28.6[25.83-31.57]26.2[23.41-29.29]Detailed race and ethnicity of childWhite, non-Hispanic1.0[0.64-1.69]8.5[7.41-9.79]8.8[7.98-9.79]18.2[16.76-19.78]30.8[29.15-32.46]32.6[30.98-34.24]1.4[0.93-2.07]8.6[7.34-9.99]8.9[7.83-10.08]16.0[14.45-17.71]32.1[30.36-33.81]33.1[31.11-35.08]Black, non-Hispanic1.5 ![0.79-2.95]12.8[9.28-17.36]8.4[6.37-11.03]19.7[16.47-23.46]18.0[15.59-20.64]39.6[35.79-43.48]1.2 ![0.50-2.75]13.9[10.09-18.74]9.0[6.64-12.11]14.5[12.10-17.27]18.3[15.10-21.93]43.2[38.37-48.13]Hispanic1.2[0.69-2.18]8.1[6.34-10.30]8.8[6.65-11.48]15.8[13.92-17.80]26.6[23.89-29.40]39.6[35.97-43.30]0.7 ![0.33-1.55]7.3[5.17-10.27]5.9[4.14-8.44]13.8[11.32-16.65]27.2[24.19-30.39]45.1[41.27-48.94]Asian or Pacific Islander, non-Hispanic1.0 ![0.40-2.38]4.2 ![2.26-7.55]1.8 ![0.89-3.49]9.4[6.50-13.33]28.2[22.44-34.71]55.5[49.33-61.58]0.1 !![0.02-0.95]2.0 ![0.86-4.38]0.8 !![0.25-2.68]6.9[4.08-11.54]33.7[27.65-40.27]56.5[49.58-63.13]All other races and multiple races, non-Hispanic0.4 !![0.10-1.80]12.0 ![6.30-21.66]6.2[4.39-8.78]19.0[14.02-25.17]26.8[21.50-32.77]35.6[30.01-41.62]1.3 !![0.42-4.20]8.4[5.19-13.26]6.2[4.01-9.45]15.6[11.19-21.39]28.1[23.21-33.66]40.3[32.81-48.29]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.!! Interpret data with caution. Estimate is unstable because the standard error represents more than 50 percent of the estimate.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: GRADE.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: SEFUTUREX, CSEX, ZIPLOCL, RACEETH2 and GRADE. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: SEFUTUREX (PFI:2012, PFI:2016), CSEX (PFI:2012, PFI:2016), ZIPLOCL (PFI:2012, PFI:2016), RACEETH2 (PFI:2012, PFI:2016), GRADEBT (PFI:2012) and GRADE (PFI:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012 and National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES TrendStats on 11/29/2018.cmnbkpcacmnbkpca5Family type, including same sex partners by Child Sex, Zip code classification by community type and Detailed race and ethnicity of child, for [Current grade or year of school (Sixth grade,Seventh grade,Eighth grade,Ninth grade,Tenth grade,Eleventh grade,Twelfth grade)] for years 2012 and 2016 Family type, including same sex partnersTwo parents and sibling(s)Two parents, no siblingOne parent and sibling(s)One parent, no siblingOtherTotalEstimatesTotal201254.812.019.59.83.9100%201658.99.818.68.54.3100%Child SexMale201254.612.119.79.74.0100%201660.79.417.88.04.0100%Female201255.111.919.39.93.7100%201656.910.219.58.94.5100%Zip code classification by community typeCity201249.39.724.112.14.8100%201654.48.722.29.94.8100%Suburb201258.211.918.98.32.8100%201662.39.617.27.53.4100%Town201251.812.519.910.65.3100%201653.311.220.910.34.4100%Rural201257.514.914.69.04.0100%201660.711.414.97.55.6100%Detailed race and ethnicity of childWhite, non-Hispanic201260.513.614.38.92.6100%201664.411.414.37.02.9100%Black, non-Hispanic201229.89.033.617.010.7100%201632.27.333.515.511.5100%Hispanic201257.59.322.47.53.3100%201661.57.621.37.32.3100%Asian or Pacific Islander, non-Hispanic201260.015.414.97.91.8 !100%201672.312.27.94.82.9100%All other races and multiple races, non-Hispanic201247.311.826.511.23.1100%201650.67.920.413.37.8100%Family type, including same sex partners by Child Sex, Zip code classification by community type and Detailed race and ethnicity of child, for [Current grade or year of school (Sixth grade,Seventh grade,Eighth grade,Ninth grade,Tenth grade,Eleventh grade,Twelfth grade)] for years 2012 and 2016 Family type, including same sex partnersTwo parents and sibling(s)Two parents, no siblingOne parent and sibling(s)One parent, no siblingOtherTotalEstimatesTotal201254.812.019.59.83.9100%201658.99.818.68.54.3100%Child SexMale201254.612.119.79.74.0100%201660.79.417.88.04.0100%Female201255.111.919.39.93.7100%201656.910.219.58.94.5100%Zip code classification by community typeCity201249.39.724.112.14.8100%201654.48.722.29.94.8100%Suburb201258.211.918.98.32.8100%201662.39.617.27.53.4100%Town201251.812.519.910.65.3100%201653.311.220.910.34.4100%Rural201257.514.914.69.04.0100%201660.711.414.97.55.6100%Detailed race and ethnicity of childWhite, non-Hispanic201260.513.614.38.92.6100%201664.411.414.37.02.9100%Black, non-Hispanic201229.89.033.617.010.7100%201632.27.333.515.511.5100%Hispanic201257.59.322.47.53.3100%201661.57.621.37.32.3100%Asian or Pacific Islander, non-Hispanic201260.015.414.97.91.8 !100%201672.312.27.94.82.9100%All other races and multiple races, non-Hispanic201247.311.826.511.23.1100%201650.67.920.413.37.8100%Standard Error (BRR)Total20120.670.290.660.290.23 20160.690.300.660.260.37 Child SexMale20120.990.440.900.450.40 20161.020.431.000.390.42 Female20120.960.420.960.440.31 20161.180.471.030.450.54 Zip code classification by community typeCity20121.240.531.380.620.62 20161.410.531.400.620.66 Suburb20121.110.501.000.420.32 20161.260.531.200.400.42 Town20122.191.191.730.991.10 20162.901.342.651.481.13 Rural20121.670.741.470.970.45 20161.810.921.540.900.99 Detailed race and ethnicity of childWhite, non-Hispanic20120.790.370.750.440.24 20160.870.430.790.310.34 Black, non-Hispanic20121.950.832.091.151.41 20162.080.852.641.361.80 Hispanic20121.550.691.590.660.44 20161.810.541.810.720.45 Asian or Pacific Islander, non-Hispanic20122.851.702.201.230.64 20162.491.531.350.830.81 All other races and multiple races, non-Hispanic20123.641.313.191.910.72 20163.551.372.742.281.73 Relative Standard Error (%)Total20121.222.453.373.006.02 20161.173.113.563.058.75 Child SexMale20121.823.654.584.639.93 20161.674.565.644.8310.44 Female20121.743.524.994.448.40 20162.074.595.295.0811.98 Zip code classification by community typeCity20122.525.415.765.1513.03 20162.596.106.296.2813.78 Suburb20121.904.175.325.1011.57 20162.025.546.965.3112.30 Town20124.229.598.709.3820.70 20165.4312.0112.7314.3825.95 Rural20122.914.9410.0910.7811.45 20162.988.0010.3412.0617.82 Detailed race and ethnicity of childWhite, non-Hispanic20121.302.745.254.899.13 20161.363.775.504.4611.69 Black, non-Hispanic20126.569.266.226.7813.21 20166.4611.647.868.7615.62 Hispanic20122.707.447.118.8813.43 20162.937.068.519.9619.27 Asian or Pacific Islander, non-Hispanic20124.7511.0714.7715.5435.65 20163.4412.5517.2117.3228.42 All other races and multiple races, non-Hispanic20127.6911.0312.0317.0723.03 20167.0217.4513.4217.0822.38 Weighted Sample Sizes (n/1,000s)Total201226,350.2 201626,032.5 Child SexMale201213,578.6 201613,538.9 Female201212,771.7 201612,493.6 Zip code classification by community typeCity20127,757.8 20167,881.7 Suburb201210,079.6 201611,788.5 Town20122,391.2 20162,029.1 Rural20126,121.6 20164,333.1 Detailed race and ethnicity of childWhite, non-Hispanic201214,072.4 201613,482.9 Black, non-Hispanic20123,738.1 20163,656.6 Hispanic20125,932.7 20166,074.7 Asian or Pacific Islander, non-Hispanic20121,346.3 20161,440.5 All other races and multiple races, non-Hispanic20121,260.8 20161,377.8 Family type, including same sex partners by Child Sex, Zip code classification by community type and Detailed race and ethnicity of child, for [Current grade or year of school (Sixth grade,Seventh grade,Eighth grade,Ninth grade,Tenth grade,Eleventh grade,Twelfth grade)] for years 2012 and 2016 Family type, including same sex partnersTwo parents and sibling(s)Two parents, no siblingOne parent and sibling(s)One parent, no siblingOtherTotalPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CI EstimatesTotal201254.8[53.49-56.16]12.0[11.43-12.60]19.5[18.22-20.83]9.8[9.23-10.40]3.9[3.44-4.37]100%201658.9[57.53-60.28]9.8[9.19-10.41]18.6[17.32-19.96]8.5[7.96-8.98]4.3[3.57-5.06]100%Child SexMale201254.6[52.59-56.54]12.1[11.21-12.96]19.7[17.97-21.56]9.7[8.81-10.59]4.0[3.29-4.88]100%201660.7[58.69-62.74]9.4[8.62-10.34]17.8[15.85-19.84]8.0[7.30-8.85]4.0[3.26-4.94]100%Female201255.1[53.19-57.00]11.9[11.14-12.81]19.3[17.43-21.26]9.9[9.10-10.86]3.7[3.16-4.41]100%201656.9[54.58-59.25]10.2[9.26-11.12]19.5[17.54-21.65]8.9[8.04-9.84]4.5[3.55-5.71]100%Zip code classification by community typeCity201249.3[46.83-51.78]9.7[8.74-10.84]24.1[21.40-26.91]12.1[10.93-13.41]4.8[3.69-6.19]100%201654.4[51.58-57.17]8.7[7.71-9.83]22.2[19.52-25.08]9.9[8.76-11.25]4.8[3.63-6.28]100%Suburb201258.2[55.93-60.34]11.9[10.94-12.92]18.9[16.94-20.94]8.3[7.51-9.20]2.8[2.20-3.49]100%201662.3[59.72-64.72]9.6[8.64-10.77]17.2[14.95-19.72]7.5[6.74-8.33]3.4[2.66-4.34]100%Town201251.8[47.42-56.10]12.5[10.26-15.03]19.9[16.67-23.56]10.6[8.75-12.70]5.3[3.51-7.99]100%201653.3[47.52-59.00]11.2[8.77-14.14]20.9[16.06-26.63]10.3[7.71-13.65]4.4[2.58-7.25]100%Rural201257.5[54.19-60.83]14.9[13.47-16.40]14.6[11.91-17.79]9.0[7.25-11.13]4.0[3.15-4.97]100%201660.7[57.02-64.20]11.4[9.74-13.39]14.9[12.05-18.17]7.5[5.87-9.49]5.6[3.89-7.89]100%Detailed race and ethnicity of childWhite, non-Hispanic201260.5[58.93-62.07]13.6[12.90-14.38]14.3[12.90-15.90]8.9[8.10-9.84]2.6[2.16-3.11]100%201664.4[62.65-66.13]11.4[10.56-12.27]14.3[12.80-15.94]7.0[6.37-7.61]2.9[2.33-3.71]100%Black, non-Hispanic201229.8[26.06-33.83]9.0[7.48-10.81]33.6[29.54-37.83]17.0[14.80-19.39]10.7[8.17-13.80]100%201632.2[28.17-36.41]7.3[5.75-9.14]33.5[28.51-38.96]15.5[12.99-18.40]11.5[8.41-15.64]100%Hispanic201257.5[54.43-60.60]9.3[8.03-10.79]22.4[19.40-25.74]7.5[6.25-8.90]3.3[2.50-4.26]100%201661.5[57.86-65.03]7.6[6.60-8.74]21.3[17.91-25.12]7.3[5.96-8.86]2.3[1.58-3.40]100%Asian or Pacific Islander, non-Hispanic201260.0[54.20-65.50]15.4[12.28-19.07]14.9[11.04-19.84]7.9[5.80-10.76]1.8 ![0.88-3.63]100%201672.3[67.10-76.97]12.2[9.46-15.59]7.9[5.56-11.01]4.8[3.38-6.72]2.9[1.61-4.99]100%All other races and multiple races, non-Hispanic201247.3[40.20-54.60]11.8[9.48-14.70]26.5[20.67-33.32]11.2[7.90-15.57]3.1[1.97-4.93]100%201650.6[43.58-57.63]7.9[5.54-11.09]20.4[15.51-26.42]13.3[9.41-18.53]7.8[4.93-11.99]100%20122016 Family type, including same sex partnersFamily type, including same sex partners Two parents and sibling(s)Two parents, no siblingOne parent and sibling(s)One parent, no siblingOtherTwo parents and sibling(s)Two parents, no siblingOne parent and sibling(s)One parent, no siblingOtherEstimatesTotal54.812.019.59.83.958.99.818.68.54.3Child SexMale54.612.119.79.74.060.79.417.88.04.0Female55.111.919.39.93.756.910.219.58.94.5Zip code classification by community typeCity49.39.724.112.14.854.48.722.29.94.8Suburb58.211.918.98.32.862.39.617.27.53.4Town51.812.519.910.65.353.311.220.910.34.4Rural57.514.914.69.04.060.711.414.97.55.6Detailed race and ethnicity of childWhite, non-Hispanic60.513.614.38.92.664.411.414.37.02.9Black, non-Hispanic29.89.033.617.010.732.27.333.515.511.5Hispanic57.59.322.47.53.361.57.621.37.32.3Asian or Pacific Islander, non-Hispanic60.015.414.97.91.872.312.27.94.82.9All other races and multiple races, non-Hispanic47.311.826.511.23.150.67.920.413.37.820122016 Family type, including same sex partnersFamily type, including same sex partners Two parents and sibling(s)Two parents, no siblingOne parent and sibling(s)One parent, no siblingOtherTwo parents and sibling(s)Two parents, no siblingOne parent and sibling(s)One parent, no siblingOtherEstimatesTotal54.812.019.59.83.958.99.818.68.54.3Child SexMale54.612.119.79.74.060.79.417.88.04.0Female55.111.919.39.93.756.910.219.58.94.5Zip code classification by community typeCity49.39.724.112.14.854.48.722.29.94.8Suburb58.211.918.98.32.862.39.617.27.53.4Town51.812.519.910.65.353.311.220.910.34.4Rural57.514.914.69.04.060.711.414.97.55.6Detailed race and ethnicity of childWhite, non-Hispanic60.513.614.38.92.664.411.414.37.02.9Black, non-Hispanic29.89.033.617.010.732.27.333.515.511.5Hispanic57.59.322.47.53.361.57.621.37.32.3Asian or Pacific Islander, non-Hispanic60.015.414.97.91.872.312.27.94.82.9All other races and multiple races, non-Hispanic47.311.826.511.23.150.67.920.413.37.8Standard Error (BRR)Total0.670.290.660.290.230.690.300.660.260.37Child SexMale0.990.440.900.450.401.020.431.000.390.42Female0.960.420.960.440.311.180.471.030.450.54Zip code classification by community typeCity1.240.531.380.620.621.410.531.400.620.66Suburb1.110.501.000.420.321.260.531.200.400.42Town2.191.191.730.991.102.901.342.651.481.13Rural1.670.741.470.970.451.810.921.540.900.99Detailed race and ethnicity of childWhite, non-Hispanic0.790.370.750.440.240.870.430.790.310.34Black, non-Hispanic1.950.832.091.151.412.080.852.641.361.80Hispanic1.550.691.590.660.441.810.541.810.720.45Asian or Pacific Islander, non-Hispanic2.851.702.201.230.642.491.531.350.830.81All other races and multiple races, non-Hispanic3.641.313.191.910.723.551.372.742.281.73Relative Standard Error (%)Total1.222.453.373.006.021.173.113.563.058.75Child SexMale1.823.654.584.639.931.674.565.644.8310.44Female1.743.524.994.448.402.074.595.295.0811.98Zip code classification by community typeCity2.525.415.765.1513.032.596.106.296.2813.78Suburb1.904.175.325.1011.572.025.546.965.3112.30Town4.229.598.709.3820.705.4312.0112.7314.3825.95Rural2.914.9410.0910.7811.452.988.0010.3412.0617.82Detailed race and ethnicity of childWhite, non-Hispanic1.302.745.254.899.131.363.775.504.4611.69Black, non-Hispanic6.569.266.226.7813.216.4611.647.868.7615.62Hispanic2.707.447.118.8813.432.937.068.519.9619.27Asian or Pacific Islander, non-Hispanic4.7511.0714.7715.5435.653.4412.5517.2117.3228.42All other races and multiple races, non-Hispanic7.6911.0312.0317.0723.037.0217.4513.4217.0822.38Weighted Sample Sizes (n/1,000s)Total26,350.2 26,032.5 Child SexMale13,578.6 13,538.9 Female12,771.7 12,493.6 Zip code classification by community typeCity7,757.8 7,881.7 Suburb10,079.6 11,788.5 Town2,391.2 2,029.1 Rural6,121.6 4,333.1 Detailed race and ethnicity of childWhite, non-Hispanic14,072.4 13,482.9 Black, non-Hispanic3,738.1 3,656.6 Hispanic5,932.7 6,074.7 Asian or Pacific Islander, non-Hispanic1,346.3 1,440.5 All other races and multiple races, non-Hispanic1,260.8 1,377.8 20122016 Family type, including same sex partnersFamily type, including same sex partners Two parents and sibling(s)Two parents, no siblingOne parent and sibling(s)One parent, no siblingOtherTwo parents and sibling(s)Two parents, no siblingOne parent and sibling(s)One parent, no siblingOther Pct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIPct.95% CIEstimatesTotal54.8[53.49-56.16]12.0[11.43-12.60]19.5[18.22-20.83]9.8[9.23-10.40]3.9[3.44-4.37]58.9[57.53-60.28]9.8[9.19-10.41]18.6[17.32-19.96]8.5[7.96-8.98]4.3[3.57-5.06]Child SexMale54.6[52.59-56.54]12.1[11.21-12.96]19.7[17.97-21.56]9.7[8.81-10.59]4.0[3.29-4.88]60.7[58.69-62.74]9.4[8.62-10.34]17.8[15.85-19.84]8.0[7.30-8.85]4.0[3.26-4.94]Female55.1[53.19-57.00]11.9[11.14-12.81]19.3[17.43-21.26]9.9[9.10-10.86]3.7[3.16-4.41]56.9[54.58-59.25]10.2[9.26-11.12]19.5[17.54-21.65]8.9[8.04-9.84]4.5[3.55-5.71]Zip code classification by community typeCity49.3[46.83-51.78]9.7[8.74-10.84]24.1[21.40-26.91]12.1[10.93-13.41]4.8[3.69-6.19]54.4[51.58-57.17]8.7[7.71-9.83]22.2[19.52-25.08]9.9[8.76-11.25]4.8[3.63-6.28]Suburb58.2[55.93-60.34]11.9[10.94-12.92]18.9[16.94-20.94]8.3[7.51-9.20]2.8[2.20-3.49]62.3[59.72-64.72]9.6[8.64-10.77]17.2[14.95-19.72]7.5[6.74-8.33]3.4[2.66-4.34]Town51.8[47.42-56.10]12.5[10.26-15.03]19.9[16.67-23.56]10.6[8.75-12.70]5.3[3.51-7.99]53.3[47.52-59.00]11.2[8.77-14.14]20.9[16.06-26.63]10.3[7.71-13.65]4.4[2.58-7.25]Rural57.5[54.19-60.83]14.9[13.47-16.40]14.6[11.91-17.79]9.0[7.25-11.13]4.0[3.15-4.97]60.7[57.02-64.20]11.4[9.74-13.39]14.9[12.05-18.17]7.5[5.87-9.49]5.6[3.89-7.89]Detailed race and ethnicity of childWhite, non-Hispanic60.5[58.93-62.07]13.6[12.90-14.38]14.3[12.90-15.90]8.9[8.10-9.84]2.6[2.16-3.11]64.4[62.65-66.13]11.4[10.56-12.27]14.3[12.80-15.94]7.0[6.37-7.61]2.9[2.33-3.71]Black, non-Hispanic29.8[26.06-33.83]9.0[7.48-10.81]33.6[29.54-37.83]17.0[14.80-19.39]10.7[8.17-13.80]32.2[28.17-36.41]7.3[5.75-9.14]33.5[28.51-38.96]15.5[12.99-18.40]11.5[8.41-15.64]Hispanic57.5[54.43-60.60]9.3[8.03-10.79]22.4[19.40-25.74]7.5[6.25-8.90]3.3[2.50-4.26]61.5[57.86-65.03]7.6[6.60-8.74]21.3[17.91-25.12]7.3[5.96-8.86]2.3[1.58-3.40]Asian or Pacific Islander, non-Hispanic60.0[54.20-65.50]15.4[12.28-19.07]14.9[11.04-19.84]7.9[5.80-10.76]1.8 ![0.88-3.63]72.3[67.10-76.97]12.2[9.46-15.59]7.9[5.56-11.01]4.8[3.38-6.72]2.9[1.61-4.99]All other races and multiple races, non-Hispanic47.3[40.20-54.60]11.8[9.48-14.70]26.5[20.67-33.32]11.2[7.90-15.57]3.1[1.97-4.93]50.6[43.58-57.63]7.9[5.54-11.09]20.4[15.51-26.42]13.3[9.41-18.53]7.8[4.93-11.99]! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.STDERR-SOURCE-END! Interpret data with caution. Estimate is unstable because the standard error represents more than 30 percent of the estimate.CONFINT-SOURCE-ENDNOTE: The following variables have varying value categories and labels across dataset years: GRADE.View the Descriptive Statistics for these variables in the workspace to learn more about the variable categories.For TrendStats the names of the variables used in this table are: FAMILYX, CSEX, ZIPLOCL, RACEETH2 and GRADE. The variable names are unique identifiers. To locate these variables in TrendStats, enter the variable name in the search box.For PowerStats the names of the variables used in this table are: FAMILY12X (PFI:2012), CSEX (PFI:2012, PFI:2016), ZIPLOCL (PFI:2012, PFI:2016), RACEETH2 (PFI:2012, PFI:2016), GRADEBT (PFI:2012), FAMILY16X (PFI:2016) and GRADE (PFI:2016).The weight variable used in this table is WTA000.Dollars adjusted for .Source: U.S. Department of Education, National Center for Education Statistics, National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2012 and National Household Education Surveys Program, Parent and Family Involvement in Education (PFI), 2016.Computation by NCES TrendStats on 11/29/2018.cmnbkpeb8cmnbkpeb8 1481PEELS1689ELS1699ELS17210HSLS113021ECPP112921ECPP113223PFI113123PFI113525HSB113626NELS:882752PSS2623SASS2633SASS2643SASS2903SASS2913SASS2923SASS2873SASS2883SASS2893SASS2933SASS2943SASS2953SASS2654SASS2664SASS2674SASS21024SASS21034SASS21044SASS2994SASS21004SASS21014SASS2964SASS2974SASS2984SASS2595SASS2605SASS2615SASS21175SASS21185SASS21195SASS21145SASS21155SASS21165SASS21115SASS21125SASS21135SASS2586SASS21106SASS21096SASS21086SASS2577SASS21077SASS21067SASS21057SASS21288SSOCS2708SSOCS2748SSOCS2738SSOCS21388SSOCS21398SSOCS212619NTPS212720NTPS3689ELS3699ELS37210HSLS313411B&B35411B&B33111B&B32011B&B35612B&B:GR37113BPS35313BPS3113BPS33213BPS312114NPSAS:UG38214NPSAS:UG35114NPSAS:UG32414NPSAS:UG33514NPSAS:UG33614NPSAS:UG32214NPSAS:UG31414NPSAS:UG31714NPSAS:UG312215NPSAS:GR38315NPSAS:GR35215NPSAS:GR31215NPSAS:GR33715NPSAS:GR33815NPSAS:GR31815NPSAS:GR31615NPSAS:GR31315NPSAS:GR313525HSB313626NELS:8842816NSOPF42616NSOPF42516NSOPF42316NSOPF42917NSOPF513411B&B55411B&B53111B&B52011B&B55612B&B:GR513322ATES Baccalaureate and BeyondBeginning Postsecondary StudentsEducation Longitudinal StudyHigh School and BeyondHigh School Longitudinal StudyNational Education Longitudinal Study of 1988National Household Education SurveysNational Postsecondary Student Aid Study, UndergraduateNational Postsecondary Student Aid Study, GraduateNational Study of Postsecondary FacultyNational Study of Postsecondary Faculty, InstitutionsNational Teacher and Principal Survey, Public SchoolsNational Teacher and Principal Survey, Public School PrincipalsPre-Elementary Education Longitudinal StudyPrivate School Universe SurveySchools and Staffing Survey, SchoolsSchools and Staffing Survey, TeachersSchools and Staffing Survey, PrincipalsSchools and Staffing Survey, DistrictsSchools and Staffing Survey, Library Media CentersSchool Survey on Crime and SafetyBaccalaureate and BeyondBeginning Postsecondary StudentsEducation Longitudinal StudyHigh School and BeyondHigh School Longitudinal StudyNational Education Longitudinal Study of 1988National Household Education SurveysNational Postsecondary Student Aid Study, UndergraduateNational Postsecondary Student Aid Study, GraduateNational Study of Postsecondary FacultyNational Study of Postsecondary Faculty, InstitutionsNational Teacher and Principal Survey, Public SchoolsNational Teacher and Principal Survey, Public School PrincipalsPre-Elementary Education Longitudinal StudyPrivate School Universe SurveySchools and Staffing Survey, SchoolsSchools and Staffing Survey, TeachersSchools and Staffing Survey, PrincipalsSchools and Staffing Survey, DistrictsSchools and Staffing Survey, Library Media CentersSchool Survey on Crime and SafetyBaccalaureate and Beyond: 2016/2017Baccalaureate and Beyond: 2008/2012Baccalaureate and Beyond: 1993/2003Baccalaureate and Beyond: 1993/2003 Graduate studentsBaccalaureate and Beyond: 2000/2001Beginning Postsecondary Students: 2012/2017Beginning Postsecondary Students: 2004/2009Beginning Postsecondary Students: 1996/2001Beginning Postsecondary Students: 1990/1994Education Longitudinal Study of 2002High School and BeyondHigh School Longitudinal Study of 2009National Education Longitudinal Study of 1988Adult Training and Education Survey: 2016Early Childhood Program Participation: 2016Early Childhood Program Participation: 2012Parent and Family Involvement in Education: 2016Parent and Family Involvement in Education: 2012National Postsecondary Student Aid Study: 2016 UndergraduatesNational Postsecondary Student Aid Study: 2012 UndergraduatesNational Postsecondary Student Aid Study: 2008 UndergraduatesNational Postsecondary Student Aid Study: 2004 UndergraduatesNational Postsecondary Student Aid Study: 2000 UndergraduatesNational Postsecondary Student Aid Study: 1996 UndergraduatesNational Postsecondary Student Aid Study: 1993 UndergraduatesNational Postsecondary Student Aid Study: 1990 UndergraduatesNational Postsecondary Student Aid Study: 1987 UndergraduatesNational Postsecondary Student Aid Study: 2016 Graduate StudentsNational Postsecondary Student Aid Study: 2012 Graduate StudentsNational Postsecondary Student Aid Study: 2008 Graduate StudentsNational Postsecondary Student Aid Study: 2004 Graduate StudentsNational Postsecondary Student Aid Study: 2000 Graduate StudentsNational Postsecondary Student Aid Study: 1996 Graduate StudentsNational Postsecondary Student Aid Study: 1993 Graduate StudentsNational Postsecondary Student Aid Study: 1990 Graduate StudentsNational Postsecondary Student Aid Study: 1987 Graduate StudentsNational Study of Postsecondary Faculty: 2004 FacultyNational Study of Postsecondary Faculty: 1999 FacultyNational Study of Postsecondary Faculty: 1993 FacultyNational Study of Postsecondary Faculty: 1988 FacultyNational Study of Postsecondary Faculty: 2004 InstitutionNational Teacher and Principal Survey, 2015-16 Public SchoolsNational Teacher and Principal Survey, 2015-16 Public School PrincipalsPre-Elementary Education Longitudinal Study, Waves 1-5Private School Universe Survey: 2011-12Schools and Staffing Survey, Public and Private Schools: 2011-12Schools and Staffing Survey, Public and Private Schools: 2007-08Schools and Staffing Survey, Public and Private Schools: 2003-04Schools and Staffing Survey, Public and Private Schools: 1999-00Schools and Staffing Survey, Public and Private Teachers: 2011-12Schools and Staffing Survey, Public and Private Teachers: 2007-08Schools and Staffing Survey, Public and Private Teachers: 2003-04Schools and Staffing Survey, Public and Private Teachers: 1999-00Schools and Staffing Survey, Public and Private School Principals: 2011-12Schools and Staffing Survey, Public and Private School Principals: 2007-08Schools and Staffing Survey, Public and Private School Principals: 2003-04Schools and Staffing Survey, Public and Private School Principals: 1999-00Schools and Staffing Survey, Districts: 2011-12Schools and Staffing Survey, Districts: 2007-08Schools and Staffing Survey, Districts: 2003-04Schools and Staffing Survey, Districts: 1999-00Schools and Staffing Survey, Library Media Centers: 2011-12Schools and Staffing Survey, Library Media Centers: 2007-08Schools and Staffing Survey, Library Media Centers: 2003-04Schools and Staffing Survey, Library Media Centers: 1999-00School Survey on Crime and Safety: 2015-16School Survey on Crime and Safety: 2009-10School Survey on Crime and Safety: 2007-08School Survey on Crime and Safety: 2005-06School Survey on Crime and Safety: 2003-04School Survey on Crime and Safety: 1999-2000Baccalaureate and Beyond: 2016/2017Baccalaureate and Beyond: 2008/2012Baccalaureate and Beyond: 1993/2003Beginning Postsecondary Students: 2012/2017Beginning Postsecondary Students: 2004/2009Beginning Postsecondary Students: 1996/2001Education Longitudinal Study of 2002High School and BeyondHigh School Longitudinal Study of 2009National Education Longitudinal Study of 1988Early Childhood Program Participation: 2016Adult Training and Education Survey: 2016Early Childhood Program Participation: 2012Parent and Family Involvement in Education: 2016Parent and Family Involvement in Education: 2012National Postsecondary Student Aid Study: 2016 UndergraduatesNational Postsecondary Student Aid Study: 2012 UndergraduatesNational Postsecondary Student Aid Study: 2008 UndergraduatesNational Postsecondary Student Aid Study: 2004 UndergraduatesNational Postsecondary Student Aid Study: 2016 Graduate StudentsNational Postsecondary Student Aid Study: 2012 Graduate StudentsNational Postsecondary Student Aid Study: 2008 Graduate StudentsNational Postsecondary Student Aid Study: 2004 Graduate StudentsNational Study of Postsecondary Faculty: 2004 FacultyNational Study of Postsecondary Faculty: 2004 InstitutionNational Teacher and Principal Survey, 2015-16 Public SchoolsNational Teacher and Principal Survey, 2015-16 Public School PrincipalsPre-Elementary Education Longitudinal Study, Waves 1-5Private School Universe Survey: 2011-12Schools and Staffing Survey, Public and Private Schools: 2011-12Schools and Staffing Survey, Public and Private Schools: 2007-08Schools and Staffing Survey, Public and Private Schools: 2003-04Schools and Staffing Survey, Public and Private Teachers: 2011-12Schools and Staffing Survey, Public and Private Teachers: 2007-08Schools and Staffing Survey, Public and Private Teachers: 2003-04Schools and Staffing Survey, Public and Private School Principals: 2011-12Schools and Staffing Survey, Public and Private School Principals: 2007-08Schools and Staffing Survey, Public and Private School Principals: 2003-04Schools and Staffing Survey, Districts: 2011-12Schools and Staffing Survey, Districts: 2007-08Schools and Staffing Survey, Districts: 2003-04Schools and Staffing Survey, Library Media Centers: 2011-12Schools and Staffing Survey, Library Media Centers: 2007-08Schools and Staffing Survey, Library Media Centers: 2003-04School Survey on Crime and Safety: 2015-16School Survey on Crime and Safety: 2009-10School Survey on Crime and Safety: 2007-08School Survey on Crime and Safety: 2005-06School Survey on Crime and Safety: 2003-04School Survey on Crime and Safety: 1999-2000Sophomores (approximately 16,000 respondents)Seniors (approximately 14,000 respondents)Public SchoolsPrivate SchoolsCombined (public and private schools)Public SchoolsPrivate SchoolsCombined (public and private schools)Public SchoolsPrivate SchoolsCombined (public and private schools)Public SchoolsPrivate SchoolsCombined (public and private schools)Public School TeachersPrivate School TeachersCombined (public and private school teachers)Public School TeachersPrivate School TeachersCombined (public and private school teachers)Public School TeachersPrivate School TeachersCombined (public and private school teachers)Public School TeachersPrivate School TeachersCombined (public and private school teachers)Public School PrincipalsPrivate School PrincipalsCombined (public and private school principals)Public School PrincipalsPrivate School PrincipalsCombined (public and private school principals)Public School PrincipalsPrivate School PrincipalsCombined (public and private school principals)Public School PrincipalsPrivate School PrincipalsCombined (public and private school principals)Sophomores (approximately 16,000 respondents)Seniors (approximately 14,000 respondents)Public SchoolsPrivate SchoolsCombined (public and private schools)Public SchoolsPrivate SchoolsCombined (public and private schools)Public SchoolsPrivate SchoolsCombined (public and private schools)Public School TeachersPrivate School TeachersCombined (public and private school teachers)Public School TeachersPrivate School TeachersCombined (public and private school teachers)Public School TeachersPrivate School TeachersCombined (public and private school teachers)Public School PrincipalsPrivate School PrincipalsCombined (public and private school principals)Public School PrincipalsPrivate School PrincipalsCombined (public and private school principals)Public School PrincipalsPrivate School PrincipalsCombined (public and private school principals) Print Page NCES DATA USAGE AGREEMENT Under law, public use data collected and distributed by the National Center for Education Statistics (NCES) may be used only for statistical purposes. 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Loading... Create account User e-mail: Password: NCES DATA USAGE AGREEMENT Under law, public use data collected and distributed by the National Center for Education Statistics (NCES) may be used only for statistical purposes. Any effort to determine the identity of any reported case by public-use data users is prohibited by law. Violations are subject to Class E felony charges of a fine up to $250,000 and/or a prison term up to 5 years. NCES does all it can to assure that the identity of data subjects cannot be disclosed. All direct identifiers, as well as any characteristics that might lead to identification, are omitted or modified in the dataset to protect the true characteristics of individual cases. Any intentional identification or disclosure of a person or institution violates the assurances of confidentiality given to the providers of the information. Therefore, users shall: Use the data in any dataset for statistical purposes only. 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ImputationImputation was conducted for selected items on the teacher questionnaire and parent interview data. In general, the item missing rate was low. The risk of imputation-related bias was judged to be minimal. The variance inflation due to imputation was also low due to the low imputation rate of 10 percent. Imputation for the supplemental sample increased the amount of data usable for analysis, offsetting the potential risk of bias.The methods of imputation included: hot-deck imputation, regression, external data source, and a derivation method, based on the internal consistency of inter-related variables.
Imputation
Imputation was conducted for selected items on the teacher questionnaire and parent interview data. In general, the item missing rate was low. The risk of imputation-related bias was judged to be minimal. The variance inflation due to imputation was also low due to the low imputation rate of 10 percent. Imputation for the supplemental sample increased the amount of data usable for analysis, offsetting the potential risk of bias.
The methods of imputation included: hot-deck imputation, regression, external data source, and a derivation method, based on the internal consistency of inter-related variables.
WeightingThe final weights are needed to have the estimates reflect the population of private schools when analyzing the data. The data from the area frame component were weighted to reflect the sampling rates (probability of selection) of the PSUs. Survey data from both the list and area frame components were adjusted for school nonresponse. The final weight for PSS data items is the product of the Base Weight and the Nonresponse Adjustment Factor, where:Base Weight is the inverse of the probability of selection of the school. The base weight is equal to one for all list-frame schools. For area-frame schools, the base weight is equal to the inverse of the probability of selecting the PSU in which the school resides.Nonresponse Adjustment Factor is an adjustment that accounts for school nonresponse. It is the weighted (base weight) ratio of the total eligible in-scope schools (interviewed schools plus noninterviewed schools) to the total responding in-scope schools (interviewed schools) within cells. Noninterviewed and out-of-scope cases are assigned a nonresponse adjustment factor of zero.Because we have more information for list-frame schools, the cells used to compute the nonresponse adjustment were defined differently for list-frame and area-frame schools. For schools in the list frame, the cells were defined by affiliation (17 categories), locale type (4 categories), grade level (4 categories), Census region (4 categories), and enrollment (3 categories). The nonresponse adjustment cells for area frame schools were defined by three-level typology (3 categories) and grade level (4 categories). If the number of schools in a cell was fewer than 15 or the nonresponse adjustment factor was greater than 1.5, then that cell was collapsed into a similar cell. The variables used to collapse the cells and the collapse order varied according to whether the school was from the list or area frame and whether a school was a traditional or k-terminal school. The cells for traditional schools from the list frame were collapsed within enrollment category, locale type, grade level, and Census region. Cells for k-terminal schools from the list frame were collapsed within enrollment category, locale type, Census region, and affiliation. Cells for traditional schools from the area frame were collapsed within grade level and then within three-level typology. Cells for k-terminal schools from the area frame were collapsed within three level typology.ImputationAfter the data edit processing was complete, there were missing values within some records classified as interviews. These were cases where the respondent had not answered some applicable questionnaire items (and data for these items were not added in the pre-edit, consistency, or logic edit) or the response had been deleted during editing. Values were imputed to the missing data during imputation. Two types of imputation were employed: donor and analyst imputation.Donor ImputationIn donor imputation, values were created by extracting data from the record for a sample case (donor) with similar characteristics, using a procedure known as the “sequential nearest neighbor hot deck” (Kalton and Kasprzyk 1982, 1986; Kalton 1983; Little and Rubin 1987; Madow, Olkin, and Rubin 1983). In order to match incomplete records to those with complete data, “imputation” variables that identify certain characteristics of the school that were deemed to be important to the reporting of the data in each item (e.g., religious affiliation, enrollment, school level of instruction) were used. Items were grouped according to the perceived relevance of the imputation variables to the data collected by the item. For example, school level of instruction was used for matching incomplete records and donors to fill item 16 (length of school year) but was not used for item 7 (students by race).Analyst ImputationAfter the donor imputation was completed, there were records that still had missing values for 64 items. These were cases where the imputation failed to create a value because there was no suitable record to use as a donor, or the value imputed was deleted because it was outside the acceptable range for the item or was inconsistent with other data on the same record, or the religious orientation or purpose, or the religious orientation or affiliation, was not reported (items 14a and 14c) and no previous PSS information was available.For these cases, values were imputed by analysts to the items with missing data. That is, staff reviewed the data record, sample file record, and the questionnaire and identified a value consistent with the information from these sources for imputation.
Weighting
Because we have more information for list-frame schools, the cells used to compute the nonresponse adjustment were defined differently for list-frame and area-frame schools. For schools in the list frame, the cells were defined by affiliation (17 categories), locale type (4 categories), grade level (4 categories), Census region (4 categories), and enrollment (3 categories). The nonresponse adjustment cells for area frame schools were defined by three-level typology (3 categories) and grade level (4 categories). If the number of schools in a cell was fewer than 15 or the nonresponse adjustment factor was greater than 1.5, then that cell was collapsed into a similar cell. The variables used to collapse the cells and the collapse order varied according to whether the school was from the list or area frame and whether a school was a traditional or k-terminal school. The cells for traditional schools from the list frame were collapsed within enrollment category, locale type, grade level, and Census region. Cells for k-terminal schools from the list frame were collapsed within enrollment category, locale type, Census region, and affiliation. Cells for traditional schools from the area frame were collapsed within grade level and then within three-level typology. Cells for k-terminal schools from the area frame were collapsed within three level typology.
After the data edit processing was complete, there were missing values within some records classified as interviews. These were cases where the respondent had not answered some applicable questionnaire items (and data for these items were not added in the pre-edit, consistency, or logic edit) or the response had been deleted during editing. Values were imputed to the missing data during imputation. Two types of imputation were employed: donor and analyst imputation.
Donor Imputation
In donor imputation, values were created by extracting data from the record for a sample case (donor) with similar characteristics, using a procedure known as the “sequential nearest neighbor hot deck” (Kalton and Kasprzyk 1982, 1986; Kalton 1983; Little and Rubin 1987; Madow, Olkin, and Rubin 1983). In order to match incomplete records to those with complete data, “imputation” variables that identify certain characteristics of the school that were deemed to be important to the reporting of the data in each item (e.g., religious affiliation, enrollment, school level of instruction) were used. Items were grouped according to the perceived relevance of the imputation variables to the data collected by the item. For example, school level of instruction was used for matching incomplete records and donors to fill item 16 (length of school year) but was not used for item 7 (students by race).
Analyst Imputation
After the donor imputation was completed, there were records that still had missing values for 64 items. These were cases where the imputation failed to create a value because there was no suitable record to use as a donor, or the value imputed was deleted because it was outside the acceptable range for the item or was inconsistent with other data on the same record, or the religious orientation or purpose, or the religious orientation or affiliation, was not reported (items 14a and 14c) and no previous PSS information was available.
For these cases, values were imputed by analysts to the items with missing data. That is, staff reviewed the data record, sample file record, and the questionnaire and identified a value consistent with the information from these sources for imputation.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables. 2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
Perturbation
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible. After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation.
Skips and Missing Values
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information.
1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.
2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
PerturbationTo protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.ImputationThree types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation. Skips and Missing ValuesFollowing data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Please consult the survey methodology for more information. 1Donors were selected based on the type of data the donor would supply to the record undergoing imputation. Matching variables were selected based on their close relationship to the item requiring imputation, and a pool of donors was selected based on their answers to these matching variables.2Goldring, R., Taie, S., Rizzo, L., Colby, D., and Fraser, A. (2013). User’s Manual for the 2011–12 Schools and Staffing Survey, Volume 1: Overview (NCES 2013-330). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
Three types of edits were performed on the SASS data: blanking, consistency, and logic edits. Blanking edits delete extraneous entries that result from respondents failing to follow skip patterns correctly and assign “missing” codes to items that respondents should have answered and didn’t. Consistency edits ensured that responses to related items were consistent and did not contradict other survey data. Finally, logic edits were performed, using information collected from the same questionnaire, associated questionnaires in the same school or district, or information from the sampling frame to fill missing items, where possible.After blanking, consistency, and logic edits were completed, any missing items that remained were filled using imputation. Data were imputed from items found on questionnaires of the same type that had certain characteristics in common or from the aggregated answers of similar questionnaires. These records are called “donor records1,” and the method of imputation that involves imputing data from donor records is called “hot-deck2” imputation.
ImputationCompleted SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.SSOCS:2004 and Beyond: In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical. WeightingData are weighted to compensate for differential probabilities of selection and to adjust for the effects of nonresponse.Sample weights allow inferences to be made about the population from which the sample units are drawn. Because of the complex nature of the SSOCS sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error. An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Due to nonresponse, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias due to nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The same predictor variables from the SSOCS:2004 CHAID analysis were used for SSOCS:2006: instructional level, region, enrollment size, percent minority, student-to-FTE teaching staff ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time equivalent (FTE) teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted so that the weighted distribution of the responding schools resembled the initial distribution of the total sample. The nonresponse-adjusted weights were then poststratified to calibrate the sample to known population totals. Two dimension margins were set up for the poststratification—(1) instructional level and school enrollment size; and (2) instructional level and locale—and an iterative process known as the raking ratio adjustment brought the weights into agreement with known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. All three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2004). Miller, A.K. (2004). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.
Completed SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.SSOCS:2004 and Beyond: In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical.
Data are weighted to compensate for differential probabilities of selection and to adjust for the effects of nonresponse.Sample weights allow inferences to be made about the population from which the sample units are drawn. Because of the complex nature of the SSOCS sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error. An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Due to nonresponse, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias due to nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The same predictor variables from the SSOCS:2004 CHAID analysis were used for SSOCS:2006: instructional level, region, enrollment size, percent minority, student-to-FTE teaching staff ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time equivalent (FTE) teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted so that the weighted distribution of the responding schools resembled the initial distribution of the total sample. The nonresponse-adjusted weights were then poststratified to calibrate the sample to known population totals. Two dimension margins were set up for the poststratification—(1) instructional level and school enrollment size; and (2) instructional level and locale—and an iterative process known as the raking ratio adjustment brought the weights into agreement with known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. All three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2004). Miller, A.K. (2004). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.
ImputationCompleted SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.SSOCS:2004 and Beyond. In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical.WeightingSample weights allow inferences to be made about the population from which the sample units were drawn. Because of the complex nature of the SSOCS:2004 sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error. The procedures used to create the SSOCS sampling weights are described below.An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Because some schools refused to participate, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias from nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (i.e., chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The predictor variables for the analysis were instructional level, region, enrollment size, percent minority, student-to-teacher ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time-equivalent teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted by dividing the base weight by the response rate in each class, so that the weighted distribution of the responding schools resembled the initial distribution of the total sample.The non-response-adjusted weights were then poststratified to calibrate the sample to known population totals. For SSOCS:2004, two dimension margins were set up for the poststratification: (1) instructional level and school enrollment size, and (2) instructional level and locale. An iterative process known as the raking ratio adjustment brought the weights into agreement with the known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. Similar to SSOCS:2000, all three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2003).Miller, A.K. (2003). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.
Completed SSOCS surveys contain some level of item nonresponse after the conclusion of the data collection phase. Imputation procedures were used to impute missing values of key items in SSOCS:2000 and missing values of all items in each subsequent SSOCS. All imputed values are flagged as such.SSOCS:2004 and Beyond. In subsequent collections, imputation procedures were used to create values for all questionnaire items with missing data. This procedural change from SSOCS:2000 was implemented because the analysis of incomplete datasets may cause different users to arrive at different conclusions, depending on how the missing data are treated. The imputation methods used in SSOCS:2004 and later surveys were tailored to the nature of each survey item. Four methods were used: aggregate proportions, logical, best match, and clerical.
Sample weights allow inferences to be made about the population from which the sample units were drawn. Because of the complex nature of the SSOCS:2004 sample design, these weights are necessary to obtain population-based estimates, to minimize bias arising from differences between responding and nonresponding schools, and to calibrate the data to known population characteristics in a way that reduces sampling error. The procedures used to create the SSOCS sampling weights are described below.An initial (base) weight was first determined within each stratum by calculating the ratio of the number of schools available in the sampling frame to the number of schools selected. Because some schools refused to participate, the responding schools did not necessarily constitute a random sample from the schools in the stratum. In order to reduce the potential of bias from nonresponse, weighting classes were determined by using a statistical algorithm similar to CHAID (i.e., chi-square automatic interaction detector) to partition the sample such that schools within a weighting class were homogenous with respect to their probability of responding. The predictor variables for the analysis were instructional level, region, enrollment size, percent minority, student-to-teacher ratio, percentage of students eligible for free or reduced-price lunch, and number of full-time-equivalent teachers. When the number of responding schools in a class was sufficiently small, the weighting class was combined with another to avoid the possibility of large weights. After combining the necessary classes, the base weights were adjusted by dividing the base weight by the response rate in each class, so that the weighted distribution of the responding schools resembled the initial distribution of the total sample.The non-response-adjusted weights were then poststratified to calibrate the sample to known population totals. For SSOCS:2004, two dimension margins were set up for the poststratification: (1) instructional level and school enrollment size, and (2) instructional level and locale. An iterative process known as the raking ratio adjustment brought the weights into agreement with the known control totals. Poststratification works well when the population not covered by the survey is similar to the covered population within each poststratum. Thus, to be effective, the variables that define the poststrata must be correlated with the variables of interest, they must be well measured in the survey, and control totals must be available for the population as a whole. Similar to SSOCS:2000, all three requirements were satisfied by the aforementioned poststratification margins. Instructional level, school enrollment, and locale have been shown to be correlated with crime (Miller 2003).Miller, A.K. (2003). Violence in U.S. Public Schools: 2000 School Survey on Crime and Safety (NCES 2004-314R). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.
ImputationAll key data items with missing values were imputed using well-known procedures. Depending on the type of data to be imputed and the extent of missing values, logical imputation, poststratum means, or "hot-deck" imputation methods were employed. For three data items, imputation was done using information from the 1998-99 CCD file. Logical imputation is the assignment of data values based on other information in the data record. In the poststratum means method, a record with missing data was assigned the mean value of those cases in the same "poststratum" for which information on the item was available. The poststrata or "imputation classes" were defined on the basis of variables that were correlated with the item being imputed. Preliminary exploratory analyses (e.g., using chi-square tests of association, correlation analysis, and regression analysis) were carried out to identify the relevant classification variables. The strength of association of the variables in combination with subjective assessment was used to prioritize the importance of the variables in forming the imputation classes. In the "hot-deck" technique, cases with missing items were assigned the corresponding value of a "similar" respondent in the same "poststratum". Similar to the poststratum means approach, preliminary exploratory analyses were carried out to identify the relevant classification variables to be used to define the poststrata. The classification variables were separated into two groups -- "hard" and "soft" boundary variables. The hard boundary variables were considered to be so important that the imputation classes were always formed within those boundaries. The boundaries formed by the soft boundary variables were crossed, if necessary, to form the imputation class.WeightingA stratified random sample design was used to select schools for the SSOCS:2000. Over 3,000 schools were selected at rates that varied by sampling stratum; i.e., the classes formed by crossing instructional level (elementary, middle, secondary, combined), type of locale (city, urban fringe, town, rural), and enrollment size class (less than 300, 300-499, 500-999, 1,000+). Since the schools were selected with unequal probabilities, sampling weights are required for analysis to inflate the survey responses to population levels. Weighting is also used to reduce the potential bias resulting from nonresponse and possible undercoverage of the sampling frame.One method of computing sampling errors to reflect various aspects of the sample design and estimation procedures is the replication method. Under replication methods, a specified number of subsamples of the full sample (called "replicates") are created. The survey estimates can then be computed for each of the replicates by creating replicate weights that mimic the actual sample design and estimation procedures used in the full sample. The variability of the estimates computed from the replicate weights is then used to estimate the sampling errors of the estimates from the full sample. An important advantage of the replication methods is that they preclude the need to specify cumbersome variance formulas that are typically needed for complex sample designs (McCarthy, 1966).1 Another advantage is that they can readily be adapted to reflect the variance resulting from nonresponse (and other weight) adjustment procedures. The two most prevalent replication methods are balanced repeated replication (BRR) and jackknife replication. The two methods differ in the manner in which the replicates are constructed. For the SSOCS:2000, a variant of jackknife replication was used to develop replicate weights for variance estimation because the jackknife method is believed to perform somewhat better than BRR for estimates of moderately rare events (e.g., number of schools in which a serious crime was committed). Under the jackknife method, the replicates are formed by deleting specified subsets of units from the full sample. The jackknife method provides a relatively simple way of creating the replicates for variance estimation and has been used extensively in NCES surveys.1. McCarthy, P. (1966). Replication: An Approach to the Analysis of Data from Complex Surveys. Vital and Health Statistics, Series 2, No. 14. Washington, DC: U.S. Department of Health, Education and Welfare.
All key data items with missing values were imputed using well-known procedures. Depending on the type of data to be imputed and the extent of missing values, logical imputation, poststratum means, or "hot-deck" imputation methods were employed. For three data items, imputation was done using information from the 1998-99 CCD file. Logical imputation is the assignment of data values based on other information in the data record. In the poststratum means method, a record with missing data was assigned the mean value of those cases in the same "poststratum" for which information on the item was available. The poststrata or "imputation classes" were defined on the basis of variables that were correlated with the item being imputed. Preliminary exploratory analyses (e.g., using chi-square tests of association, correlation analysis, and regression analysis) were carried out to identify the relevant classification variables. The strength of association of the variables in combination with subjective assessment was used to prioritize the importance of the variables in forming the imputation classes. In the "hot-deck" technique, cases with missing items were assigned the corresponding value of a "similar" respondent in the same "poststratum". Similar to the poststratum means approach, preliminary exploratory analyses were carried out to identify the relevant classification variables to be used to define the poststrata. The classification variables were separated into two groups -- "hard" and "soft" boundary variables. The hard boundary variables were considered to be so important that the imputation classes were always formed within those boundaries. The boundaries formed by the soft boundary variables were crossed, if necessary, to form the imputation class.
A stratified random sample design was used to select schools for the SSOCS:2000. Over 3,000 schools were selected at rates that varied by sampling stratum; i.e., the classes formed by crossing instructional level (elementary, middle, secondary, combined), type of locale (city, urban fringe, town, rural), and enrollment size class (less than 300, 300-499, 500-999, 1,000+). Since the schools were selected with unequal probabilities, sampling weights are required for analysis to inflate the survey responses to population levels. Weighting is also used to reduce the potential bias resulting from nonresponse and possible undercoverage of the sampling frame.One method of computing sampling errors to reflect various aspects of the sample design and estimation procedures is the replication method. Under replication methods, a specified number of subsamples of the full sample (called "replicates") are created. The survey estimates can then be computed for each of the replicates by creating replicate weights that mimic the actual sample design and estimation procedures used in the full sample. The variability of the estimates computed from the replicate weights is then used to estimate the sampling errors of the estimates from the full sample. An important advantage of the replication methods is that they preclude the need to specify cumbersome variance formulas that are typically needed for complex sample designs (McCarthy, 1966).1 Another advantage is that they can readily be adapted to reflect the variance resulting from nonresponse (and other weight) adjustment procedures. The two most prevalent replication methods are balanced repeated replication (BRR) and jackknife replication. The two methods differ in the manner in which the replicates are constructed. For the SSOCS:2000, a variant of jackknife replication was used to develop replicate weights for variance estimation because the jackknife method is believed to perform somewhat better than BRR for estimates of moderately rare events (e.g., number of schools in which a serious crime was committed). Under the jackknife method, the replicates are formed by deleting specified subsets of units from the full sample. The jackknife method provides a relatively simple way of creating the replicates for variance estimation and has been used extensively in NCES surveys.
1. McCarthy, P. (1966). Replication: An Approach to the Analysis of Data from Complex Surveys. Vital and Health Statistics, Series 2, No. 14. Washington, DC: U.S. Department of Health, Education and Welfare.
ImputationStochastic methods were used to impute the missing values for the ELS:2002 third follow-up data. Specifically, a weighted sequential hot-deck (WSHD) statistical imputation procedure (Cox 1980; Iannacchione 1982) using the final analysis weight (F3QWT) was applied to the missing values for the variables in table 12 in the order in which they are listed. The WSHD procedure replaces missing data with valid data from a donor record within an imputation class. In general, variables with lower item nonresponse rates were imputed earlier in the process.
Stochastic methods were used to impute the missing values for the ELS:2002 third follow-up data. Specifically, a weighted sequential hot-deck (WSHD) statistical imputation procedure (Cox 1980; Iannacchione 1982) using the final analysis weight (F3QWT) was applied to the missing values for the variables in table 12 in the order in which they are listed. The WSHD procedure replaces missing data with valid data from a donor record within an imputation class. In general, variables with lower item nonresponse rates were imputed earlier in the process.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, HSLS:09 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies. ImputationStochastic methods were used to impute the missing values. Specifically, a weighted sequential hot-deck (WSHD; statistical) imputation procedure (Cox 1980; Iannacchione 1982) using the final student analysis weight (W2STUDENT) was applied to the missing values for variables. The WSHD procedure replaces missing data with valid data from a donor record (i.e., first follow-up student [item] respondent) within an imputation class. In general, variables with lower item nonresponse rates were imputed earlier in the process. Skips and Missing Values The HSLS:09 data were edited using procedures developed and implemented for previous studies sponsored by NCES Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in HSLS:09. Please consult the methodology report (coming soon) for more information. Description of missing data codes
To protect the confidentiality of NCES data that contain information about specific individuals, HSLS:09 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies.
Stochastic methods were used to impute the missing values. Specifically, a weighted sequential hot-deck (WSHD; statistical) imputation procedure (Cox 1980; Iannacchione 1982) using the final student analysis weight (W2STUDENT) was applied to the missing values for variables. The WSHD procedure replaces missing data with valid data from a donor record (i.e., first follow-up student [item] respondent) within an imputation class. In general, variables with lower item nonresponse rates were imputed earlier in the process.
The HSLS:09 data were edited using procedures developed and implemented for previous studies sponsored by NCES Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.
The table below shows codes for missing values used in HSLS:09. Please consult the methodology report (coming soon) for more information.
Description of missing data codes
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values During and following data collection, the data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a number of explanations for missing data; for example, the item may not have been applicable to certain respondents or a respondent may not have known the answer to the question. With the exception of the not applicable codes, missing data were stochastically imputed. Moreover, for hierarchical analyses and developing survey estimates for faculty members corresponding to sample institutions that provided faculty lists and responded to the institution survey, contextual weights were produced for such subsets of the responding faculty members. The table below shows codes for missing values used. Please consult the methodology report for more information. Description of missing data codes
All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.
During and following data collection, the data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a number of explanations for missing data; for example, the item may not have been applicable to certain respondents or a respondent may not have known the answer to the question. With the exception of the not applicable codes, missing data were stochastically imputed. Moreover, for hierarchical analyses and developing survey estimates for faculty members corresponding to sample institutions that provided faculty lists and responded to the institution survey, contextual weights were produced for such subsets of the responding faculty members.
The table below shows codes for missing values used. Please consult the methodology report for more information.
1Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions.
2Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable imputed and observed will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, B&B:08/12 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. B&B:08/12 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies. Imputation Variables with missing data were imputed for graduates who were respondents in a study wave . The imputation procedures employed a two-step process. The first step is a logical imputation . If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation. This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values The B&B: 08/12 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:08. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in B&B:08/12. Please consult the First Look for more information. Description of missing value codes
To protect the confidentiality of NCES data that contain information about specific individuals, B&B:08/12 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. B&B:08/12 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies.
Variables with missing data were imputed for graduates who were respondents in a study wave . The imputation procedures employed a two-step process. The first step is a logical imputation . If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation. This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.
The B&B: 08/12 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:08. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.
The table below shows codes for missing values used in B&B:08/12. Please consult the First Look for more information.
Description of missing value codes
1In other words, if a graduate was a respondent in B&B:09, he or she will have no missing data for variables created as part of the B&B:09 wave. Similarly, if a graduate was a respondent in B&B:12, he or she will have no missing data for variables created as part of the B&B:12 wave, but may have missing data for variables created as part of the B&B:09 wave if he or she was not a respondent in B&B:09.
2Logical imputation is a process that aims to infer or deduce the missing values from answers to other questions.
3Sequential hot deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
Imputation Variables used in cross-sectional estimates in the Baccalaureate and Beyond descriptive reports were imputed. The variables identified for imputation were used in the two B&B:93/03 descriptive reports (Bradburn, Nevill, and Forrest Cataldi 2006; Alt and Henke 2007). The imputations were performed in three steps. First, the interview variables were imputed using the sequential hot deck imputation method.1 This imputation procedure involves identifying a relatively homogenous group of observations, and within the group selecting a random donor’s value to impute a value for the recipient. Second, using the interview variables, including the newly imputed variable values, derived variables were constructed. Skips and Missing Values Both during and upon completion of data collection, edit checks were performed on the B&B:93/03 data file to confirm that the intended skip patterns were implemented during the interview. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons. The Table below lists each missing value code and its associated meaning in the B&B:93/03 interview. For more information, see the Baccalaureate and Beyond Longitudinal Study (B&B:93/03) methodology report. Description of missing data codes
Variables used in cross-sectional estimates in the Baccalaureate and Beyond descriptive reports were imputed. The variables identified for imputation were used in the two B&B:93/03 descriptive reports (Bradburn, Nevill, and Forrest Cataldi 2006; Alt and Henke 2007). The imputations were performed in three steps. First, the interview variables were imputed using the sequential hot deck imputation method.1 This imputation procedure involves identifying a relatively homogenous group of observations, and within the group selecting a random donor’s value to impute a value for the recipient. Second, using the interview variables, including the newly imputed variable values, derived variables were constructed.
Both during and upon completion of data collection, edit checks were performed on the B&B:93/03 data file to confirm that the intended skip patterns were implemented during the interview. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons.
The Table below lists each missing value code and its associated meaning in the B&B:93/03 interview. For more information, see the Baccalaureate and Beyond Longitudinal Study (B&B:93/03) methodology report.
1Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. Under this methodology, while each respondent record has a chance to be selected for use as a hot-deck donor, the number of times a respondent record can be used for imputation will be controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictor) for each item being imputed were defined. Imputation classes were developed by using a Chi-squared Automatic Interaction.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, B&B:01 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values Both during and upon completion of data collection, edit checks were performed on the B&B:00/01 data file to confirm that the intended skip patterns were implemented during the interview. Following data collection, the information collected in CATI was subjected to various checks and examinations. These checks were intended to confirm that the database reflected appropriate skip-pattern relationships and different types of missing data by inserting special codes. The Table below lists each missing value code and its associated meaning in the B&B:00/01 interview. For more information, see the Baccalaureate and Beyond Longitudinal Study (B&B:00/01) methodology report . Description of missing data codes
To protect the confidentiality of NCES data that contain information about specific individuals, B&B:01 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors.
Both during and upon completion of data collection, edit checks were performed on the B&B:00/01 data file to confirm that the intended skip patterns were implemented during the interview. Following data collection, the information collected in CATI was subjected to various checks and examinations. These checks were intended to confirm that the database reflected appropriate skip-pattern relationships and different types of missing data by inserting special codes.
The Table below lists each missing value code and its associated meaning in the B&B:00/01 interview. For more information, see the Baccalaureate and Beyond Longitudinal Study (B&B:00/01) methodology report .
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, BPS:12/17 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:12/17 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values The BPS:12/17 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:12. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in BPS:12/17. Please consult the methodology report (coming soon) for more information. Description of missing data codes
To protect the confidentiality of NCES data that contain information about specific individuals, BPS:12/17 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:12/17 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies.
The BPS:12/17 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:12. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.
The table below shows codes for missing values used in BPS:12/17. Please consult the methodology report (coming soon) for more information.
2Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, BPS:04/09 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:04/09 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values The BPS:04/09 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:04. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in BPS:04/09. Please consult the methodology report (coming soon) for more information. Description of missing data codes
To protect the confidentiality of NCES data that contain information about specific individuals, BPS:04/09 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:04/09 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies.
The BPS:04/09 data were edited using procedures developed and implemented for previous studies sponsored by NCES, including the base-year study, NPSAS:04. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data.
The table below shows codes for missing values used in BPS:04/09. Please consult the methodology report (coming soon) for more information.
Imputation Logical imputations were performed where items were missing but their values could be implicitly determined. Skips and Missing Values During and following data collection, the CATI/CAPI data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a variety of explanations for missing data within individual data elements. The table below shows codes for missing values used in BPS:01. Please consult the methodology report for more information. Description of missing data codes
Logical imputations were performed where items were missing but their values could be implicitly determined.
During and following data collection, the CATI/CAPI data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a variety of explanations for missing data within individual data elements.
The table below shows codes for missing values used in BPS:01. Please consult the methodology report for more information.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, BPS:94 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:94 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values The BPS:94 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data.A variety of explanations are possible for missing data.The table below shows codes for missing values used in BPS:94. Please consult the methodology report for more information. Description of missing data codes
To protect the confidentiality of NCES data that contain information about specific individuals, BPS:94 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. BPS:94 has multiple sources of data for some variables (CPS, NLSDS, student interview, etc.), and reporting differences can occur in each. Data swapping and other forms of perturbation, implemented to protect respondent confidentiality, can also lead to inconsistencies.
The BPS:94 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data.
The table below shows codes for missing values used in BPS:94. Please consult the methodology report for more information.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. The table below shows the set of reserve codes for missing values used in NPSAS 2016. Please consult the data file documentation report for more information. Description of missing data codes
Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely.
The table below shows the set of reserve codes for missing values used in NPSAS 2016. Please consult the data file documentation report for more information.
2Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variableimputed and observedwill resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Missing Values and Imputation Following data collection, the data are subjected to various consistency and quality control checks before release. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. Except for data that were missing for cases to which they did not apply (e.g., whether a spouse is enrolled in college for unmarried students) and in a small number of items describing institutional characteristics, missing data were imputed using a two-step process. The first step is a logical imputation.1 If a value could be calculated from the logical relationships with other variables, then that information was used to impute the value for the observation with a missing value. The second step is weighted hot deck imputation.2 This procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor's value to impute a value for the observation with a missing value. The table below shows the set of missing value codes for missing values that were not imputed in NPSAS:12. More information is available from the NPSAS:12 Data File Documentation (http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2014182). Description of missing value codes
To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Data swapping and other forms of perturbation can lead to inconsistencies.
Missing Values and Imputation
Following data collection, the data are subjected to various consistency and quality control checks before release. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely.
Except for data that were missing for cases to which they did not apply (e.g., whether a spouse is enrolled in college for unmarried students) and in a small number of items describing institutional characteristics, missing data were imputed using a two-step process. The first step is a logical imputation.1 If a value could be calculated from the logical relationships with other variables, then that information was used to impute the value for the observation with a missing value. The second step is weighted hot deck imputation.2 This procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor's value to impute a value for the observation with a missing value.
The table below shows the set of missing value codes for missing values that were not imputed in NPSAS:12. More information is available from the NPSAS:12 Data File Documentation (http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2014182).
1Logical imputation is a process that aims to infer or deduce the missing values from values for other items.
2Sequential hot deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent's answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using the chi-square automatic interaction detection algorithm.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. The table below shows the set of reserve codes for missing values used in NPSAS 2008. Please consult the methodology report for more information. Description of missing data codes
The table below shows the set of reserve codes for missing values used in NPSAS 2008. Please consult the methodology report for more information.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation The imputation procedures employed a two-step process. In the first step, the matching criteria and imputation classes that were used to stratify the dataset were identified such that all imputation was processed independently within each class. In the second step, the weighted sequential hot deck process1 was implemented, whereby missing data were replaced with valid data from donor records that match the recipients with respect to the matching criteria. Variables requiring imputation were not imputed simultaneously. However, some variables that were related substantively were grouped together into blocks, and the variables within a block were imputed simultaneously. Basic demographic variables were imputed first using variables with full information to determine the matching criteria. The order in which variables were imputed was also determined to some extent by the substantive nature of the variables. For example, basic demographics (such as age) were imputed first and these were used to process education variables (such as student level and enrollment intensity) which in turn were used to impute the financial aid variables (such as aid receipt and loan amounts). Skips and Missing Values Edit checks were performed on the NPSAS:04 student interview data and CADE data, both during and upon completion of data collection, to confirm that the intended skip patterns were implemented in both instruments. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons. The table below shows the set of reserve codes for missing values used in NPSAS 2004. Please consult the methodology report for more information. Description of missing data codes
The imputation procedures employed a two-step process. In the first step, the matching criteria and imputation classes that were used to stratify the dataset were identified such that all imputation was processed independently within each class. In the second step, the weighted sequential hot deck process1 was implemented, whereby missing data were replaced with valid data from donor records that match the recipients with respect to the matching criteria. Variables requiring imputation were not imputed simultaneously. However, some variables that were related substantively were grouped together into blocks, and the variables within a block were imputed simultaneously. Basic demographic variables were imputed first using variables with full information to determine the matching criteria. The order in which variables were imputed was also determined to some extent by the substantive nature of the variables. For example, basic demographics (such as age) were imputed first and these were used to process education variables (such as student level and enrollment intensity) which in turn were used to impute the financial aid variables (such as aid receipt and loan amounts).
Edit checks were performed on the NPSAS:04 student interview data and CADE data, both during and upon completion of data collection, to confirm that the intended skip patterns were implemented in both instruments. At the conclusion of data collection, special codes were added as needed to indicate the reason for missing data. Missing data within individual data elements can occur for a variety of reasons.
The table below shows the set of reserve codes for missing values used in NPSAS 2004. Please consult the methodology report for more information.
1Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. While each respondent record may be selected for use as a hot-deck donor, the number of times a respondent record is used for imputation is controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictors) for each item being imputed are defined. Imputation classes are developed by using a Chi-squared Automatic Interaction.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, NPSAS:00 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing ValuesThe NPSAS:00 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in NPSAS:00 Please consult the methodology report for more information. Description of missing data codes
To protect the confidentiality of NCES data that contain information about specific individuals, NPSAS:00 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors.
The table below shows codes for missing values used in NPSAS:00 Please consult the methodology report for more information.
Imputation Values for 22 analysis variables were imputed. The variables were imputed using a weighted hot deck procedure, with the exception of estimated family contribution (EFC), which was imputed through a multiple regression approach.The weighed hot deck imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing ValuesThe NPSAS:96 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in NPSAS:96 Please consult the methodology report for more information. Description of missing data codes
Values for 22 analysis variables were imputed. The variables were imputed using a weighted hot deck procedure, with the exception of estimated family contribution (EFC), which was imputed through a multiple regression approach.The weighed hot deck imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.
The table below shows codes for missing values used in NPSAS:96 Please consult the methodology report for more information.
Derived Variables and Imputed Values Approximately 800 variables have been constructed based on data collected in the NPSAS:93. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values. Skips and Missing Values Both the student and parent CATI programs were designed to accommodate responses of "refusal" and "don't know" to any single question. Typically, refusal responses are given for items considered too sensitive by the respondent. "Don't know" responses may be given for any one of several reasons: (1) the respondent misunderstands the question wording, and is not offered subsequent explanation by the interviewer; (2) the respondent is hesitant to provide "best guess" responses, with insufficient prompting from the interviewer; (3) the respondent truly does not know the answer; or (4) the respondent chooses to respond with "don't know" as an implicit refusal to answer the question. Whenever they occur, indeterminate responses in the data set must be resolved by imputation or otherwise dealt with during analysis. The table below shows the set of reserve codes for missing values used in NPSAS 1993. Please consult the data file documentation report for more information. Description of missing data codes
Derived Variables and Imputed Values
Approximately 800 variables have been constructed based on data collected in the NPSAS:93. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values.
Both the student and parent CATI programs were designed to accommodate responses of "refusal" and "don't know" to any single question. Typically, refusal responses are given for items considered too sensitive by the respondent. "Don't know" responses may be given for any one of several reasons: (1) the respondent misunderstands the question wording, and is not offered subsequent explanation by the interviewer; (2) the respondent is hesitant to provide "best guess" responses, with insufficient prompting from the interviewer; (3) the respondent truly does not know the answer; or (4) the respondent chooses to respond with "don't know" as an implicit refusal to answer the question. Whenever they occur, indeterminate responses in the data set must be resolved by imputation or otherwise dealt with during analysis.
The table below shows the set of reserve codes for missing values used in NPSAS 1993. Please consult the data file documentation report for more information.
Imputation Variables with more than 5 percent missing cases were imputed. After using information from all appropriate secondary sources, there remained eight variables which required some statistical imputation. Two methods of statistical imputation were used, regression-based or hot deck. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. The table below shows the set of reserve codes for missing values used in NPSAS 1990. Please consult the data file documentation report for more information. Description of missing data codes
Variables with more than 5 percent missing cases were imputed. After using information from all appropriate secondary sources, there remained eight variables which required some statistical imputation. Two methods of statistical imputation were used, regression-based or hot deck.
The table below shows the set of reserve codes for missing values used in NPSAS 1990. Please consult the data file documentation report for more information.
Derived Variables and Imputed Values Approximately 800 variables have been constructed based on data collected in the NPSAS:87. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values. Skips and Missing Values Both the student and parent CATI programs were designed to accommodate responses of "refusal" and "don't know" to any single question. Typically, refusal responses are given for items considered too sensitive by the respondent. "Don't know" responses may be given for any one of several reasons: (1) the respondent misunderstands the question wording, and is not offered subsequent explanation by the interviewer; (2) the respondent is hesitant to provide "best guess" responses, with insufficient prompting from the interviewer; (3) the respondent truly does not know the answer; or (4) the respondent chooses to respond with "don't know" as an implicit refusal to answer the question. Whenever they occur, indeterminate responses in the data set must be resolved by imputation or otherwise dealt with during analysis. The table below shows the set of reserve codes for missing values used in NPSAS 1993. Please consult the data file documentation report for more information. Description of missing data codes
Approximately 800 variables have been constructed based on data collected in the NPSAS:87. As a general rule, the constructions of derive variables that concern financial aid and other financial descriptors depend first on record abstract data from the CADE system. These data are supplemented in many cases with information collected in the telephone interviews with parents and students. As between parent and student data, precedence was generally given to parent data for variables concerning family income and assets. Imputations were performed on seven variables that contained missing values.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in non-sampling errors. Data swapping and other forms of perturbation can lead to inconsistencies. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation.1 If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values Following data collection, the data are subjected to various consistency and quality control checks before release for use by analysts. One important check is examining all variables with missing data and substituting specific values to indicate the reason for the missing data. For example, an item may not have been applicable to some groups of respondents, a respondent may not have known the answer to a question, or a respondent may have skipped the item entirely. The table below shows the set of reserve codes for missing values used in NPSAS 2008. Please consult the methodology report for more information. Description of missing data codes
All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation.1 If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient.
Perturbation To protect the confidentiality of NCES data that contain information about specific individuals, NPSAS:00 data were subject to perturbation procedures to minimize disclosure risk. Perturbation procedures, which have been approved by the NCES Disclosure Review Board, preserve the central tendency estimates but may result in slight increases in nonsampling errors. Imputation All variables with missing data were imputed. The imputation procedures employed a two-step process. The first step is a logical imputation1. If the imputed value could be deduced from the logical relationships with other variables, then that information was used to impute the value for the recipient. The second step is weighted hot-deck imputation.2 This imputation procedure involves identifying a relatively homogenous group of observations, and, from within the group, selecting a random donor’s value to impute a value for the recipient. Skips and Missing Values The NPSAS:00 data were edited using procedures developed and implemented for previous studies sponsored by NCES. Following data collection, the information collected in the student instrument was subjected to various quality control checks and examinations. These checks were to confirm that the collected data reflected appropriate skip patterns. Another evaluation examined all variables with missing data and substituted specific values to indicate the reason for the missing data. A variety of explanations are possible for missing data. The table below shows codes for missing values used in NPSAS:00 Please consult the methodology report for more information. Description of missing data codes
Perturbation A restricted faculty-level data file was created for release to individuals who apply for and meet standards for such data releases. While this file does not include personally identifying information (i.e., name and Social Security number), other data (i.e., institution, Integrated Postsecondary Education Data System [IPEDS] ID, demographic information, and salary data) may be manipulated in such a way to seem to identify data records corresponding to a particular faculty member. To protect further against such situations, some of the variable values were swapped between faculty respondents. This procedure perturbed and added additional uncertainty to the data. Thus, associations made among variable values to identify a faculty respondent may be based on the original or edited, imputed and/or swapped data. For the same reasons, the data from the institution questionnaire were also swapped to avoid data disclosure. Imputation Item imputation for the faculty questionnaire was performed in several steps. In the first step, the missing values of gender, race, and ethnicity were filled—using cold-deck imputation1— based on the sampling frame information or institution record data. These three key demographic variables were imputed prior to any other variables since they were used as key predictors for all other variables on the data file. After all logical2 and cold-deck imputation procedures were performed, the remaining variables were imputed using the weighted sequential hot-deck method.3 Initially, variables were separated into two groups: unconditional and conditional variables. The first group (unconditional) consisted of variables that applied to all respondents, while the second group (conditional) consisted of variables that applied to only a subset of the respondents. That is, conditional variables were subject to “gate” questions. After this initial grouping, these groups were divided into finer subgroups. After all variables were imputed, consistency checks were applied to the entire faculty data file to ensure that the imputed values did not conflict with other questionnaire items, observed or imputed. This process involved reviewing all of the logical imputation and editing rules as well. Skips and Missing Values During and following data collection, the data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a number of explanations for missing data; for example, the item may not have been applicable to certain respondents or a respondent may not have known the answer to the question. With the exception of the not applicable codes, missing data were stochastically imputed. Moreover, for hierarchical analyses and developing survey estimates for faculty members corresponding to sample institutions that provided faculty lists and responded to the institution survey, contextual weights were produced for such subsets of the responding faculty members. The table below shows codes for missing values used in NSOPF:04. Please consult the methodology report for more information. Description of missing data codes
A restricted faculty-level data file was created for release to individuals who apply for and meet standards for such data releases. While this file does not include personally identifying information (i.e., name and Social Security number), other data (i.e., institution, Integrated Postsecondary Education Data System [IPEDS] ID, demographic information, and salary data) may be manipulated in such a way to seem to identify data records corresponding to a particular faculty member. To protect further against such situations, some of the variable values were swapped between faculty respondents. This procedure perturbed and added additional uncertainty to the data. Thus, associations made among variable values to identify a faculty respondent may be based on the original or edited, imputed and/or swapped data. For the same reasons, the data from the institution questionnaire were also swapped to avoid data disclosure.
Item imputation for the faculty questionnaire was performed in several steps. In the first step, the missing values of gender, race, and ethnicity were filled—using cold-deck imputation1— based on the sampling frame information or institution record data. These three key demographic variables were imputed prior to any other variables since they were used as key predictors for all other variables on the data file. After all logical2 and cold-deck imputation procedures were performed, the remaining variables were imputed using the weighted sequential hot-deck method.3 Initially, variables were separated into two groups: unconditional and conditional variables. The first group (unconditional) consisted of variables that applied to all respondents, while the second group (conditional) consisted of variables that applied to only a subset of the respondents. That is, conditional variables were subject to “gate” questions. After this initial grouping, these groups were divided into finer subgroups. After all variables were imputed, consistency checks were applied to the entire faculty data file to ensure that the imputed values did not conflict with other questionnaire items, observed or imputed. This process involved reviewing all of the logical imputation and editing rules as well.
The table below shows codes for missing values used in NSOPF:04. Please consult the methodology report for more information.
1Cold-deck imputation involves replacing the missing values with data from sources such as data used for sampling frame construction. While resource intensive, these methods often obtain the actual value that is missing. Stochastic imputation methods, such as sequential hot-deck imputation, rely on the observed data to provide replacing values (donors) for records with missing values.
3Sequential hot-deck imputation involves defining imputation classes, which generally consist of a cross-classification of covariates, and then replacing missing values sequentially from a single pass through the survey data within the imputation classes. When this form of imputation is performed using the sampling weights, the procedure is called weighted sequential hot-deck imputation. This procedure takes into account the unequal probabilities of selection in the original sample to specify the expected number of times a particular respondent’s answer will be used as a donor. These expected selection frequencies are specified so that, over repeated applications of the algorithm, the weighted distribution of all values for that variable—imputed and observed—will resemble that of the target universe. Under this methodology, while each respondent record has a chance to be selected for use as a hot-deck donor, the number of times a respondent record can be used for imputation will be controlled. To implement the weighted sequential hot-deck procedure, imputation classes and sorting variables that are relevant (strong predictor) for each item being imputed were defined. Imputation classes were developed by using a Chi-squared Automatic Interaction.
Both the faculty and institution questionnaire data were edited using seven principles designed to improve data quality and consistency.Menu items. For many questions there were several sub-items listed where the respondent was asked to give a response for each sub-item. These questions were cleaned with several procedures. First if the main question had an “NA” (Not Applicable) check box and that box was checked, all of the sub-items were set to a value of “no” or “zero” depending on the wording of the question. Second, if the respondent had filled out one or more of the sub-items with a “yes” response or a positive number but had left other sub-items blank, the missing sub-items were set to “no,” “zero,” or “don’t know” depending on the question wording. If all sub-items were missing and there was no “NA” box, or the “NA” box was not checked, the case was flagged and the data values were imputed for that question. Examples of these types of questions are Question 21 in the institution questionnaire and Question 29 in the faculty questionnaire.Inter-item consistency checks. Many types of inter-item consistency checks were performed on the data. One procedure was to check groups of related items for internal consistency and to make adjustments to make them consistent. For example, in questions that asked about a spouse in the faculty questionnaire (Questions 66i, Q76i, and 77a) if respondents indicated that they did not have a spouse in one or more of the questions, the other questions were checked for consistency and corrected as necessary. Another procedure checked “NA” boxes. If the respondent had checked the “NA” box for a question but had filled in any of the sub-items for that question the “NA” box was set to blank. For example, this procedure was used with Question 21 in the institution questionnaire and Question 16 in the faculty questionnaire. A third procedure was to check filter items for which more detail was sought in a follow-up open-ended or closed-ended question. If detail was provided, then the filter question was checked to make sure the appropriate response was recorded. For example, this procedure was used with Question 11 in the institution questionnaire and Question 12E in the faculty questionnaire.Percent items. All items where respondents were asked to give a percentage were checked to make sure they summed to 100 percent. The editing program also looked for any numbers between 0 and 1 to make sure that respondents did not fill in the question with a decimal rather than a percentage. All fractions of a percent were rounded to the nearest whole percent. An example of this type of item is Question 31 in the faculty questionnaire.Data imputation for the faculty questionnaire was performed in four steps. The imputation method for each variable is specified in the labels for the imputation flags in the faculty dataset.Logical imputation. The logical imputation was conducted during the data cleaning steps as explained in the immediately preceding section. Cold deck. Missing responses were filled in with data from the sample frame whenever the relevant data were available. Examples include gender, race, and employment status.Hot deck. This procedure selected non-missing values from “sequential nearest neighbors” within the imputation class. All questions that were categorical and had more than 16 categories were imputed with this method. An example is Question Q14 – principal field of teaching. The imputation class for this question was created using faculty stratum and instructional duty status (Q1). Regression type. This procedure employed SAS PROC IMPUTE21. All items that were still missing after the logical, cold deck, and hot deck imputation procedures were imputed with this method. Project staff selected the independent variables by first looking through the questionnaire for logically related items and then by conducting a correlation analysis of the questions against each other to find the top correlates for each item.
Menu items. For many questions there were several sub-items listed where the respondent was asked to give a response for each sub-item. These questions were cleaned with several procedures. First if the main question had an “NA” (Not Applicable) check box and that box was checked, all of the sub-items were set to a value of “no” or “zero” depending on the wording of the question. Second, if the respondent had filled out one or more of the sub-items with a “yes” response or a positive number but had left other sub-items blank, the missing sub-items were set to “no,” “zero,” or “don’t know” depending on the question wording. If all sub-items were missing and there was no “NA” box, or the “NA” box was not checked, the case was flagged and the data values were imputed for that question. Examples of these types of questions are Question 21 in the institution questionnaire and Question 29 in the faculty questionnaire.
Inter-item consistency checks. Many types of inter-item consistency checks were performed on the data. One procedure was to check groups of related items for internal consistency and to make adjustments to make them consistent. For example, in questions that asked about a spouse in the faculty questionnaire (Questions 66i, Q76i, and 77a) if respondents indicated that they did not have a spouse in one or more of the questions, the other questions were checked for consistency and corrected as necessary. Another procedure checked “NA” boxes. If the respondent had checked the “NA” box for a question but had filled in any of the sub-items for that question the “NA” box was set to blank. For example, this procedure was used with Question 21 in the institution questionnaire and Question 16 in the faculty questionnaire. A third procedure was to check filter items for which more detail was sought in a follow-up open-ended or closed-ended question. If detail was provided, then the filter question was checked to make sure the appropriate response was recorded. For example, this procedure was used with Question 11 in the institution questionnaire and Question 12E in the faculty questionnaire.
Percent items. All items where respondents were asked to give a percentage were checked to make sure they summed to 100 percent. The editing program also looked for any numbers between 0 and 1 to make sure that respondents did not fill in the question with a decimal rather than a percentage. All fractions of a percent were rounded to the nearest whole percent. An example of this type of item is Question 31 in the faculty questionnaire.
Logical imputation. The logical imputation was conducted during the data cleaning steps as explained in the immediately preceding section.
Cold deck. Missing responses were filled in with data from the sample frame whenever the relevant data were available. Examples include gender, race, and employment status.
Hot deck. This procedure selected non-missing values from “sequential nearest neighbors” within the imputation class. All questions that were categorical and had more than 16 categories were imputed with this method. An example is Question Q14 – principal field of teaching. The imputation class for this question was created using faculty stratum and instructional duty status (Q1).
Regression type. This procedure employed SAS PROC IMPUTE21. All items that were still missing after the logical, cold deck, and hot deck imputation procedures were imputed with this method. Project staff selected the independent variables by first looking through the questionnaire for logically related items and then by conducting a correlation analysis of the questions against each other to find the top correlates for each item.
Depending on the scale of the variable being imputed, one of two methods were used:1) Regression imputation was used for continuous and dichotomous variables; and2) Hotdeck imputation was used for unordered polytomous variables.The regression method incorporated in NCES’s PROC IMPUTE was used to impute missing values for approximately 90 percent of the 395 items on the faculty questionnaire.Of the total of 395 items, 353 were imputed using the regression-based imputation procedures only.
Depending on the scale of the variable being imputed, one of two methods were used:1) Regression imputation was used for continuous and dichotomous variables; and2) Hotdeck imputation was used for unordered polytomous variables.The regression method incorporated in NCES’s PROC IMPUTE was used to impute missing values for approximately 90 percent of the 395 items on the faculty questionnaire.
Of the total of 395 items, 353 were imputed using the regression-based imputation procedures only.
NSOPF:88 was conducted with a sample of 480 institutions (including 2-year, 4-year, doctoral-granting, and other colleges and universities), some 11,010 faculty, and more than 3,000 department chairpersons. Institutions were sampled from the 1987 IPEDS universe and were stratified by modified Carnegie Classifications and size (faculty counts). These strata were (1) public, research; (2) private, research; (3) public, other Ph.D. institution (not defined in any other stratum); (4) private, other Ph.D. institution (not defined in any other stratum); (5) public, comprehensive; (6) private, comprehensive; (7) liberal arts; (8) public, 2-year; (9) private, 2-year; (10) religious; (11) medical; and (12) “other” schools (not defined in any other stratum). Within each stratum, institutions were randomly selected. Of the 480 institutions selected, 450 (94 percent) agreed to participate and provided lists of their faculty and department chairpersons. Within 4-year institutions, faculty and department chairpersons were stratified by program area and randomly sampled within each stratum; within 2-year institutions, simple random samples of faculty and department chairpersons were selected; and within specialized institutions (religious, medical, etc.), faculty samples were randomly selected (department chairpersons were not sampled). At all institutions, faculty were also stratified on the basis of employment status—full-time and part-time. Note that teaching assistants and teaching fellows were excluded in NSOPF:88.Although NSOPF:88 consisted of three questionnaires, imputations were only performed for faculty item nonresponse. The within-cell random imputation method was used to fill in most Faculty Questionnaire items that had missing data.
NSOPF:88 was conducted with a sample of 480 institutions (including 2-year, 4-year, doctoral-granting, and other colleges and universities), some 11,010 faculty, and more than 3,000 department chairpersons. Institutions were sampled from the 1987 IPEDS universe and were stratified by modified Carnegie Classifications and size (faculty counts). These strata were (1) public, research; (2) private, research; (3) public, other Ph.D. institution (not defined in any other stratum); (4) private, other Ph.D. institution (not defined in any other stratum); (5) public, comprehensive; (6) private, comprehensive; (7) liberal arts; (8) public, 2-year; (9) private, 2-year; (10) religious; (11) medical; and (12) “other” schools (not defined in any other stratum). Within each stratum, institutions were randomly selected. Of the 480 institutions selected, 450 (94 percent) agreed to participate and provided lists of their faculty and department chairpersons. Within 4-year institutions, faculty and department chairpersons were stratified by program area and randomly sampled within each stratum; within 2-year institutions, simple random samples of faculty and department chairpersons were selected; and within specialized institutions (religious, medical, etc.), faculty samples were randomly selected (department chairpersons were not sampled). At all institutions, faculty were also stratified on the basis of employment status—full-time and part-time. Note that teaching assistants and teaching fellows were excluded in NSOPF:88.
Although NSOPF:88 consisted of three questionnaires, imputations were only performed for faculty item nonresponse. The within-cell random imputation method was used to fill in most Faculty Questionnaire items that had missing data.
Imputation The imputation process for the missing data from the institution questionnaire involved similar steps to those used for imputation of the faculty data. The missing data for variables were imputed using the weighted sequential hot-deck method.1 Analogous to the imputation process for the faculty data, the variables were partitioned into conditional and unconditional groups. The unconditional variables were sorted by percent missing and then imputed in the order from the lowest percent missing to the highest. The conditional group was partitioned into three subgroups based on the level of conditionality for each variable, and then imputed in that order. The imputation class for both unconditional and conditional variables consisted of the institution sampling stratum, and the sorting variables included the number of full-time and part-time faculty members. Skips and Missing Values During and following data collection, the data were reviewed to confirm that the data collected reflected the intended skip-pattern relationships. At the conclusion of data collection, special codes were inserted in the database to reflect the different types of missing data. There are a number of explanations for missing data; for example, the item may not have been applicable to certain respondents or a respondent may not have known the answer to the question. With the exception of the not applicable codes, missing data were stochastically imputed. Moreover, for hierarchical analyses and developing survey estimates for faculty members corresponding to sample institutions that provided faculty lists and responded to the institution survey, contextual weights were produced for such subsets of the responding faculty members. The table below shows codes for missing values used in NSOPF:04. Please consult the methodology report for more information. Description of missing data codes
The imputation process for the missing data from the institution questionnaire involved similar steps to those used for imputation of the faculty data. The missing data for variables were imputed using the weighted sequential hot-deck method.1 Analogous to the imputation process for the faculty data, the variables were partitioned into conditional and unconditional groups. The unconditional variables were sorted by percent missing and then imputed in the order from the lowest percent missing to the highest. The conditional group was partitioned into three subgroups based on the level of conditionality for each variable, and then imputed in that order. The imputation class for both unconditional and conditional variables consisted of the institution sampling stratum, and the sorting variables included the number of full-time and part-time faculty members.
ImputationThe NTPS used two main approaches to impute data. First, donor respondent methods, such as hot-deck imputation, were used. Second, if no suitable donor case could be matched, the few remaining items were imputed using mean or mode from groups of similar cases to impute a value to the item with missing data. Finally, in rare cases for which imputed values were inconsistent with existing questionnaire data or out of the range of acceptable values, Census Bureau analysts looked at the items and tried to determine an appropriate value.WeightingWeighting of the sample units was carried out to produce national estimates for public schools, principals, and teachers. The weighting procedures used in NTPS had three purposes: to take into account the school's selection probability; to reduce biases that may result from unit nonresponse; and to make use of available information from external sources to improve the precision of sample estimates.
The NTPS used two main approaches to impute data. First, donor respondent methods, such as hot-deck imputation, were used. Second, if no suitable donor case could be matched, the few remaining items were imputed using mean or mode from groups of similar cases to impute a value to the item with missing data. Finally, in rare cases for which imputed values were inconsistent with existing questionnaire data or out of the range of acceptable values, Census Bureau analysts looked at the items and tried to determine an appropriate value.
Weighting of the sample units was carried out to produce national estimates for public schools, principals, and teachers. The weighting procedures used in NTPS had three purposes: to take into account the school's selection probability; to reduce biases that may result from unit nonresponse; and to make use of available information from external sources to improve the precision of sample estimates.
ImputationFour approaches to imputation were used in the NHES:2016: logic-based imputation, which was used whenever possible; unweighted sequential hot deck imputation, which was used for the majority of the missing data (i.e., for all variables that were not boundary and sort variables—described below); weighted random imputation, which was used for a small number of variables including boundary and sort variables; and manual imputation, which was used in a very small number of cases for a small number of variables.For more information about these approaches, please see the NHES: 2016 Data File User's Manual.
Four approaches to imputation were used in the NHES:2016: logic-based imputation, which was used whenever possible; unweighted sequential hot deck imputation, which was used for the majority of the missing data (i.e., for all variables that were not boundary and sort variables—described below); weighted random imputation, which was used for a small number of variables including boundary and sort variables; and manual imputation, which was used in a very small number of cases for a small number of variables.For more information about these approaches, please see the NHES: 2016 Data File User's Manual.
ImputationThree approaches to imputation were used in the NHES:2012: unweighted sequential hot deck imputation, which was used for the majority of the missing data, that is, for all variables that were not required for Interview Status Recode (ISR) classification, as described in chapter 4; weighted random imputation, which was used for a small number of variables; and manual imputation, which was used in a very small number of cases for most variables.For more information about these approaches, please see the NHES: 2012 Data File User's Manual.
Three approaches to imputation were used in the NHES:2012: unweighted sequential hot deck imputation, which was used for the majority of the missing data, that is, for all variables that were not required for Interview Status Recode (ISR) classification, as described in chapter 4; weighted random imputation, which was used for a small number of variables; and manual imputation, which was used in a very small number of cases for most variables.For more information about these approaches, please see the NHES: 2012 Data File User's Manual.
Nonresponse Nonresponse inevitably introduces some degree of error into survey results. In examining the impact of nonresponse, it is useful to think of the survey population as including two strata--a respondent stratum that consists of all units that would have provided data had they been selected for the survey, and a nonrespondent stratum that consists of all units that would not have provided data had they been selected. The actual sample of respondents necessarily consists entirely of units from the respondent stratum. Thus, sample statistics can serve as unbiased estimates only for the respondent stratum; as estimates for the entire population, the sample statistics will be biased to the extent that the characteristics of the respondents differ from those of the entire population.In the High School and Beyond study, there were two stages of sample selection and therefore two stages of nonresponse. During the base year survey, sample schools were asked to permit the selection of individual sophomores and seniors from school rosters and to designate "survey days" for the collection of student questionnaire and test data. Schools that refused to cooperate in either of these activities were dropped from the sample. Individual students at cooperating schools could also fail to take part in the base year survey. Unlike "refusal" schools, nonparticipating students were not dropped from the sample; they remained eligible for selection into the follow-up samples.Estimates based on student data from the base year surveys include two components of nonresponse bias: bias introduced by nonresponse at the school level, and bias introduced by nonresponse on the part of students attending cooperating schools. Each component of the overall bias depends on two factors--the level of nonresponse and the difference between respondents and nonrespondents: Bias = P1(Y1R - Y1NR) + P2(Y2R - Y2NR)in which P1 = the proportion of the population of students attending schools that would have been nonrespondents,YlNR = the parameter describing the population of students attending nonrespondent schools, P2 = the proportion of students attending respondent schools who would have been nonrespondents, and Y2NR = the parameter describing this group of students.Nonresponse bias will be small if the nonrespondent strata constitute only a small portion of the survey population or if the differences between respondents and nonrespondents are small. The proportions P1 and P2 can generally be estimated from survey data using appropriately weighted nonresponse rates. The implications of the equation can be easily seen in terms of a particular base year estimate. On the average, sophomores got 10.9 items right on a standardized vocabulary test. This figure is an estimate of Y2R, the population mean for all participating students at cooperating schools. Now, suppose that sophomores at cooperating schools average two more correct than sophomores attending refusal schools (Y1R - Y1NR = 2), and suppose further that among sophomores attending cooperating schools, student respondents average one more correct answer than student nonrespondents (Y2R - Y2NR = 1). Noting that the base year school nonresponse rate was about .30 and the student nonresponse rate for sophomores was about .12, we can use these figures as estimates of P1 and P2 and we can use this equation to calculate the bias as: Bias = .30(2) + .12(1) = .72 That is, the sample estimate is biased by about .7 of a test score point.This example assumes knowledge of the relevant population means; in practice, of course, they are not known and, although Pl and P2 can generally be estimated from the nonresponse rates, the lack of survey data for nonrespondents prevents the estimation of the nonresponse bias. The High School and Beyond study is an exception to this general rule: during the first follow-up, school questionnaire data were obtained from most of the base year refusal schools, and student data were obtained from most of the base year student nonrespondents selected for the first follow-up sample. These data provide a basis for assessing the magnitude of nonresponse bias in base year estimates.The bias introduced by base year school-level refusals is of particular concern since it carries over into successive rounds of the survey. Students attending refusal schools were not sampled during the base year and have no chance for selection into subsequent rounds of observation. To the extent that these students differ from students from cooperating schools during later waves of the study, the bias introduced by base year school nonresponse will persist. Student nonresponse is not carried over in this way since student nonrespondents remain eligible for sampling in later waves of the study.The results of three types of analyses concerning nonresponse are described in an earlier report. Based on school questionnaire data, schools that participated during the base year were compared with all eligible schools. Based on the first follow-up student data, base year student respondents were compared with nonrespondents. Finally, student nonresponse during the first follow-up survey was analyzed. Taken together, these earlier analyses indicated that nonresponse had little effect on base year and first follow-up estimates. The results presented there suggest that the school-level component of the bias affected base year estimates by 2 percent or less and that the student-level component had even less impact.
Nonresponse
Nonresponse inevitably introduces some degree of error into survey results. In examining the impact of nonresponse, it is useful to think of the survey population as including two strata--a respondent stratum that consists of all units that would have provided data had they been selected for the survey, and a nonrespondent stratum that consists of all units that would not have provided data had they been selected. The actual sample of respondents necessarily consists entirely of units from the respondent stratum. Thus, sample statistics can serve as unbiased estimates only for the respondent stratum; as estimates for the entire population, the sample statistics will be biased to the extent that the characteristics of the respondents differ from those of the entire population.
In the High School and Beyond study, there were two stages of sample selection and therefore two stages of nonresponse. During the base year survey, sample schools were asked to permit the selection of individual sophomores and seniors from school rosters and to designate "survey days" for the collection of student questionnaire and test data. Schools that refused to cooperate in either of these activities were dropped from the sample. Individual students at cooperating schools could also fail to take part in the base year survey. Unlike "refusal" schools, nonparticipating students were not dropped from the sample; they remained eligible for selection into the follow-up samples.
Estimates based on student data from the base year surveys include two components of nonresponse bias: bias introduced by nonresponse at the school level, and bias introduced by nonresponse on the part of students attending cooperating schools. Each component of the overall bias depends on two factors--the level of nonresponse and the difference between respondents and nonrespondents:
Bias = P1(Y1R - Y1NR) + P2(Y2R - Y2NR)
in which
P1 = the proportion of the population of students attending schools that would have been nonrespondents,
YlNR = the parameter describing the population of students attending nonrespondent schools,
P2 = the proportion of students attending respondent schools who would have been nonrespondents, and
Y2NR = the parameter describing this group of students.
Nonresponse bias will be small if the nonrespondent strata constitute only a small portion of the survey population or if the differences between respondents and nonrespondents are small. The proportions P1 and P2 can generally be estimated from survey data using appropriately weighted nonresponse rates.
The implications of the equation can be easily seen in terms of a particular base year estimate. On the average, sophomores got 10.9 items right on a standardized vocabulary test. This figure is an estimate of Y2R, the population mean for all participating students at cooperating schools. Now, suppose that sophomores at cooperating schools average two more correct than sophomores attending refusal schools (Y1R - Y1NR = 2), and suppose further that among sophomores attending cooperating schools, student respondents average one more correct answer than student nonrespondents (Y2R - Y2NR = 1). Noting that the base year school nonresponse rate was about .30 and the student nonresponse rate for sophomores was about .12, we can use these figures as estimates of P1 and P2 and we can use this equation to calculate the bias as:
Bias = .30(2) + .12(1) = .72
That is, the sample estimate is biased by about .7 of a test score point.
This example assumes knowledge of the relevant population means; in practice, of course, they are not known and, although Pl and P2 can generally be estimated from the nonresponse rates, the lack of survey data for nonrespondents prevents the estimation of the nonresponse bias. The High School and Beyond study is an exception to this general rule: during the first follow-up, school questionnaire data were obtained from most of the base year refusal schools, and student data were obtained from most of the base year student nonrespondents selected for the first follow-up sample. These data provide a basis for assessing the magnitude of nonresponse bias in base year estimates.
The bias introduced by base year school-level refusals is of particular concern since it carries over into successive rounds of the survey. Students attending refusal schools were not sampled during the base year and have no chance for selection into subsequent rounds of observation. To the extent that these students differ from students from cooperating schools during later waves of the study, the bias introduced by base year school nonresponse will persist. Student nonresponse is not carried over in this way since student nonrespondents remain eligible for sampling in later waves of the study.
The results of three types of analyses concerning nonresponse are described in an earlier report. Based on school questionnaire data, schools that participated during the base year were compared with all eligible schools. Based on the first follow-up student data, base year student respondents were compared with nonrespondents. Finally, student nonresponse during the first follow-up survey was analyzed. Taken together, these earlier analyses indicated that nonresponse had little effect on base year and first follow-up estimates. The results presented there suggest that the school-level component of the bias affected base year estimates by 2 percent or less and that the student-level component had even less impact.
NonresponseSchool-level nonresponse is a serious concern because it carries over into successive rounds of NELS:88. Students attending schools that did not cooperate in the base year were not sampled and had little or no chance of selection into the follow-up samples. To the extent that students at noncooperating schools differ from students at cooperating schools, the student-level bias introduced by base-year school noncooperation persists during subsequent waves. Nonresponse adjustments to weights are an attempt to compensate for bias in the estimate for a particular subgroup; they do not adjust for nonresponse bias within subgroups.In the base year, nonresponding schools were asked to supply information about key school questionnaire variables, and virtually all did so. Based on these data, analysis of school-level nonresponse suggests that, to the extent that schools can be characterized by size, control, organizational structure, student composition, and other characteristics, the impact of nonresponding schools on school level estimates is small.25 Readers interested in more information about the analyses of school nonresponse rates and bias for the NELS:88 base year should refer to the NELS:88 Base-Year Sample Design Report (Spencer et al. 1990). School nonresponse was not assessed in the first or second follow-ups for two reasons. First, there was practically no school-level nonresponse; institutional cooperation levels approached 99 percent in both rounds. Second, the first and second follow-up samples were student-driven, unlike the two-stage initial sample design in the base year. Hence, even if a school refused in either the first or second follow-ups, the individual student was pursued outside of school.25. The use of school questionnaire variables to assess bias in estimates concerning characteristics of the student population is not entirely straightforward. Still, to the extent that school characteristics are closely related to the characteristics of the students attending them, estimates based on school questionnaire data can serve as reasonable proxies for more direct estimates of student-level unit nonresponse bias.
School-level nonresponse is a serious concern because it carries over into successive rounds of NELS:88. Students attending schools that did not cooperate in the base year were not sampled and had little or no chance of selection into the follow-up samples. To the extent that students at noncooperating schools differ from students at cooperating schools, the student-level bias introduced by base-year school noncooperation persists during subsequent waves. Nonresponse adjustments to weights are an attempt to compensate for bias in the estimate for a particular subgroup; they do not adjust for nonresponse bias within subgroups.
In the base year, nonresponding schools were asked to supply information about key school questionnaire variables, and virtually all did so. Based on these data, analysis of school-level nonresponse suggests that, to the extent that schools can be characterized by size, control, organizational structure, student composition, and other characteristics, the impact of nonresponding schools on school level estimates is small.25 Readers interested in more information about the analyses of school nonresponse rates and bias for the NELS:88 base year should refer to the NELS:88 Base-Year Sample Design Report (Spencer et al. 1990). School nonresponse was not assessed in the first or second follow-ups for two reasons. First, there was practically no school-level nonresponse; institutional cooperation levels approached 99 percent in both rounds. Second, the first and second follow-up samples were student-driven, unlike the two-stage initial sample design in the base year. Hence, even if a school refused in either the first or second follow-ups, the individual student was pursued outside of school.
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