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445 – Contributed Oral Poster Presentations: Survey Research Methods Section

The Accuracy of a National Generalized Variance Function for Subnational Estimation

at Bureau of Justice Statistics

Generalized Variance Functions (GVFs) approximate the variance of an estimate as a function of readily available information about that estimate. They can be used to calculate variance estimates for surveys with complex sample designs, and because they do not require users to have knowledge of the complex design they are often easier to use than direct variance estimation techniques such as Taylor Series Linearization (TSL) for basic analyses. However, the validity of GVFs estimates is only known when they are applied to estimate types that were used to build the GVF equations.

This paper explores the accuracy of a national GVF when applied to subnational estimates using data from the National Crime Victimization Survey (NCVS). The NCVS, sponsored by the Bureau of Justice Statistics (BJS) and conducted by the U.S. Census Bureau, is a multi-mode, rotating panel design survey of households that produces nationally-representative criminal victimization estimates for major types of crimes in the United States. For the NCVS, GVFs created by the Census Bureau were designed to produce variance estimates at the national level, but their accuracy at the subnational level has not been evaluated. We assess the accuracy of GVF estimates within subnational areas based on geographic identifiers on the NCVS Public Use Files (i.e. Census region, population size, and urbanicity) by comparing them with TSL estimates. Our analysis found that TSL and GVFs do not provide consistent variance estimates within these subnational areas and thus, the current NCVS GVFs should not be applied below the national level.