Imputation: 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 analytical purposes. For census years, the complete data set is also needed for sample design purposes.
For nonresponding general purpose governments, imputations for missing units are based on recently reported historical data from either a prior year annual survey or the most recent census, adjusted by a growth rate. If no historical data are available, data from a randomly selected similar unit are adjusted by the ratio of the populations of the nonresponding and randomly selected donor governments.
The imputations for nonresponding special districts are done similarly. If prior year reported data are available, the prior year data for the nonrespondent are adjusted by a growth rate that is determined from reporting units that are similar to the nonrespondent. Special districts are similar if they are of the same function code and similar geography, e.g., police protection in a state or water transport in a region. For nonresponding special districts with no recently reported data available, data are used from a randomly selected donor that is similar to the nonrespondent. In cases where secondary data sources exist, the data from those sources are used.
For individual questionnaire items that are not reported by general purpose governments or dependent and independent school districts, either data from another source, pro-rating of totals, or prior year data are used to give a complete dataset.
Note: Between years 2002 through 2006, individual government imputed data were not released to the public. For 2007, individual unit data are available upon request. Official Census Bureau datafiles carry imputation and edit flags to help the users determine the usability of the data for their purposes.
Editing: Editing is a process that ensures 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 post collection. Edits consist primarily of four types: (1) consistency edits, (2) historical ratio edits of the current year's reported value to the prior year's value, (3) current year ratio edits, and (4) balance checks.
The consistency edits check the logical relationships of data items reported on the form. For example, if interest on debt is reported, then there must be debt.
The historical ratio edits compare data for the current year to data for the prior year or prior census year. If data fall outside of acceptable tolerance levels, the item is flagged for further review. For example, the reported property tax for the current year may be compared against the property tax last year, if the reporting unit was in last year's sample. If it was not in last year's sample, the current year value is compared to the prior census year value.
The current year ratio edits compare one data item on the form against a different data item. If data fall outside of acceptable tolerance levels, the item is flagged for further review. For example, airport expenditure to airport revenue is a current year ratio.
Balance checks are checks of linear relationships that exist in the data. Debt flow is an example of a balance check. The ending debt must equal the beginning debt plus the debt issued minus the debt retired.
After all data are edited and imputed, they are aggregated. A macro-edit, or aggregate-level, review is conducted with current year state aggregates compared to prior year and prior census aggregates. Macro-level ratio edits and tolerance levels were developed using the current year data.
For the ratio edits, consistency edits, balance checks, and macro edits, the edit results are reviewed by analysts and adjusted as 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. The results of the action are tracked with a data edit flag.
Sampling Error: The data for the census year are not subject to sampling and do not contain sampling error. The user should be mindful that the data for years not ending in '2' or '7' are from sample surveys and are subject to sampling error. Discussions of sampling error are available in the survey methodology descriptions for those years.