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

Many large national surveys aim to produce accurate overall population estimates while supporting comparisons across key sociodemographic groups, including racial and ethnic minorities. Address-based sampling enables more targeted and efficient designs to achieve these goals. This paper evaluates the efficiency and tradeoffs of oversampling racial and ethnic minority groups using the Bayesian Surname–Geography method, in comparison with an alternative approach based on vendor-appended demographic indicators,. We also consider an approach using proprietary big-data classifiers. The evaluation framework explicitly links oversampling choices to design effects arising from unequal selection probabilities. Using empirical data from two national surveys, the framework is applied 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.