Reaching Racial and Ethnic Minorities in an Address-based Sample: How Much Efficiency Can We Gain?

Thomas M. Menino Convention & Exhibition Center 

Most national surveys aim to produce accurate overall population estimates while supporting comparisons across key sociodemographic groups, including racial and ethnic minorities. This paper evaluates the efficiency and tradeoffs of oversampling racial and ethnic minority groups using the Bayesian Surname–Geography method in the address-based sampling context, in comparison with two alternative approaches based on vendor-appended demographic indicators and proprietary big-data classifiers. Beyond assessing data quality measures such as precision and recall, the paper presents an evaluation framework that explicitly links oversampling choices to design effects arising from unequal selection probabilities. This framework is applied to the empirical data from two national surveys to assess effective sample size gains for both target and non-target populations. The results provide practical guidance for designing oversampling strategies that improve precision for multiple subgroups while avoiding substantial variance inflation in overall estimates.

Oversampling

racial/ethnic minority population

design effect

Bayesian Surname and Geocoding (BSG) 

Survey Research Methods Section