NHANES data are often used to provide national estimates on important public health issues. This module introduces how to generate the descriptive statistics for NHANES data that are most often used to obtain these estimates. Topics covered in this module include checking frequency distribution and normality, generating percentiles, generating means, and generating proportions.
It is highly recommended that you examine the frequency distribution and normality of the data before starting any analysis. These descriptive statistics are useful in determining whether parametric or non-parametric methods are appropriate to use, and whether you need to recode or transform data to account for extreme values and outliers.
Percentiles are used to indicate the relative position of an individual within a given dataset. Frequency distribution and percentiles also can be used to describe the characteristics of a distribution and to check for outliers.
Although SAS and Stata have commands for calculating estimates of weighted percentiles, they do not have commands to directly produce standard errors for the percentiles. So this tutorial will not provide sample programs in SAS and Stata for percentiles and their standard errors. Please refer to SUDAAN program for reference. |
Means are used to estimate averages of a particular variable of interest, e.g., the average total cholesterol levels or the average systolic blood pressure levels in a given population.
Proportions are used for prevalence estimates of an event or trait, e.g., the prevalence of persons with high blood pressure (HBP) in the U.S.
National Center for Health
Statistics
3311 Toledo Road
Hyattsville, MD 20782
Phone: 1-866-441-NCHS (6247)
For data inquiries, use
nchsquery@cdc.gov
Problems or comments about the Tutorial?
Email the Tutorial Team:
NHANESWebTutorial@cdc.gov
Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA 30333, U.S.A
Tel: (404) 639-3311 / Public Inquiries: (404) 639-3534 / (800) 311-3435