State and Local Government Employee-Retirement Systems
Fiscal Year 2005
| Main Retirement | 2005 Retirement |
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State and Local Government Employee-Retirement Systems Fiscal Year 2005 |
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Data Processing |
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Editing: |
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Editing is a process that ensures survey data are accurate, complete, and consistent.
Efforts are made at all phases of collection, processing, and tabulation to minimize errors.
Although some edits are built into the Internet data collection instrument and the data entry programs, the majority of the edits are performed after the case has been loaded into the Census Bureau’s database. Edits consist primarily of two types: consistency and a ratio of the current year’s reported value to the prior year’s value.  The consistency edits check the logical relationships of data items reported on the form.
For example, if a value exists for the number of retirees receiving benefits because of age or length of service then there must be a value reported for the amount paid.
The current year/prior year edits compare by item
code the data reported for the current year with data reported for the prior year and vice versa. If data falls out of
acceptable tolerance levels, the item is flagged for review.
For both types of edits, the edit results are reviewed by analysts and adjusted when needed. When the analyst is unable to resolve or accept the edit failure, contact is made with the respondent to verify or correct the reported data. |
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Imputation: |
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Not all respondents answer every item on the questionnaire. There are also questionnaires that are not
returned despite efforts to gain a response. Imputation is the process of filling in missing or invalid
data with reasonable values in order to have a complete data set.
For both State and Local governments the imputations were based on either a prior year annual survey or the most recent Census of Governments. All but four missing variables were imputed using one of the following methods: cell median or donor distribution of Z81, cell mean, or reported prior year data was multiplied by a growth factor. |
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Estimation: |
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Estimation is the process by which sample data are used to indicate the value of an unknown quantity in a
population. A simple unbiased estimate is calculated for each variable and can be obtained from the data
by multiplying the value of each variable by its weight.
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Variance: |
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Data that are derived from the annual sample survey are subject to sampling error. The statistics in this
report that are based wholly or partly on data from the sample are apt to differ from the results of a
Census covering all governments. Estimates based on a sample survey are subject to sampling variability.
The particular sample used is one of a large number of all possible samples of the same size that could
have been selected using the same sample design. Each of the possible samples would yield somewhat
different results.
The standard error is a measure of the variation among the estimates from all possible samples and thus is a measure of the precision with which an estimate from a particular sample approximates the average results of all possible samples. Each viewable table contains a column that gives users the coefficients of variation (or relative standard error) that have been computed for these estimates. The coefficient of variation is the estimated standard error expressed as a percent of the estimated total or proportion. State government retirement data are not subject to sampling error since all State government retirement systems are included in the sample. Consequently, state-local aggregates shown here for individual states are more reliable (on relative standard error basis) than the local government estimates they include. The CV’s presenting the tables can be used to derive the standard error of the estimate. The standard error can then be used to derive interval estimates with prescribed levels of confidence that the interval includes the average results of all samples: a. intervals defined by one standard error above and below the sample estimate will contain the true value about 68 percent of the time; b. intervals defined by 1.6 standard errors above and below the sample estimate will contain the true value about 90 percent of the time; c. intervals defined by two standard errors above and below the sample estimate will contain the true value about 95 percent of the time. The user can calculate the standard error by multiplying the CV presented in the tables by the corresponding estimate. The CVs presented in the tables are in percentage form and must be divided by 100 before being multiplied by the estimate. This standard error estimate can then be used to get a 90 percent interval estimate by multiplying it by 1.6 and adding the result to the estimated total to get the upper bound and subtracting it from the estimated total to get the lower bound. |
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