Imputation and Estimation  

Imputation: When non-response occurs, when respondents do not completely answer the questionnaire, or when reported data are considered incorrect during the error detection steps, imputation is used to fill in the missing information and modify the incorrect information. Many methods of imputation may be used to complete a questionnaire, including manual changes made by an analyst. PRICE uses statistical techniques to impute the missing data include: deterministic imputation, replacement using historical data (with a trend calculated, when appropriate), replacement using auxiliary information available from other sources, replacement based on known data relationships for the sample unit, and replacement using data from a similar unit in the sample. Usually, key variables are imputed first and are used as anchors in subsequent steps to impute other, related, variables. Imputation generates a complete and coherent micro data file that covers all survey variables.

Estimation: The sample will be used for estimation comes from a single-wave of sampling process. An initial sampling weight (the design weight) will be calculated for each unit of the survey and is simply the inverse of the probability of selection that is conditional on the realized sample size. The weight calculated for each sampling unit indicates how many other units it represents. The final weights are usually either one or greater than one. Sampling units which are "Take-all" (also called "must-take") have sampling weights of one and only represent themselves.

Estimation of totals will be done by simple aggregation of the weighted values of all estimation units that are found in the domain of estimation. Estimates will be computed for several domains of estimation such as economic clusters and major states, based on the efficacy of sample observations and the survey reference period.