State and Local Government Finance
Survey Methodology
Fiscal Year 2006
| 2006 Census Technical Documentation | Back to Survey Methodology |
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State and Local Government Finance Survey Methodology Fiscal Year 2006 |
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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: logical edits and ratio edits. The logical edits are designed to correct unreported or misreported debt, incorrect interest calculations, check logical relationships of data items reported, and to make adjustments for missing or underreported salaries and wages by utilizing the Annual Survey of Government Employment and Payroll. 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. If analysts were unable to obtain corrected data from original sources, they attempted to obtain data from secondary sources. |
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Imputation: |
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Imputation is the process of filling in missing or invalid data with reasonable values in order to have a complete data set. Not all respondents answer every item on the questionnaire. There are also questionnaires that are not
returned despite efforts to gain a response.
For general purpose governments and for schools, the imputations were based on recent historical data from either a prior year annual survey or the most recent Census of Governments, if it was available. These data were adjusted by a growth rate that was determined by the growth of units that were similar (in size, geography, and type of government) to the non-respondent, If there were no recent historical data available, the imputations were based on the data from a randomly selected donor that was similar to the non-respondent. For special districts, if prior year data were available, the data were brought forward with a national level growth rate applied. Otherwise, the data were imputed to be zero, since most cases lacking prior year data were births with no activity. In cases where good secondary data sources exist, the data from those sources were used. Beginning with the 2002 Census, individual unit imputed data were no longer available on the data files released to the public. |
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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. In the Census year publications for finance statistics, totals, ratios, and year-to-year changes are published. For most cases, a regression estimate is used. In cases where sample size is smaller than 20 units, the simple unbiased estimate is used The simple unbiased estimate is always used for debt and capital outlay variables.
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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 financial statistics result from a complete canvass of all state government agencies. Consequently, there is no associated measure of sampling error, such as the relative standard error. However, these statistics are subject to non-sampling error. Such error includes inaccuracies in classification, coverage, and processing. Even though efforts were made at all phases of collection, processing, and tabulation to minimize errors, the data were still subject to errors from imputing for missing data, errors from miscoding, and errors in coverage. Every effort was made to keep such errors to a minimum through examining, editing, and tabulating the data. The CVs (coefficient of variation) presented in 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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