🤖 AI Summary
This study addresses the challenge of sampling sparse, geographically dispersed minority populations whose individual identities are not directly observable. The authors propose an innovative approach that integrates individual-level group membership probabilities generated by Bayesian Improved Surname Geocoding (BISG) into a stratified Poisson probability sampling framework. This design represents the first effective synthesis of BISG-derived prior information with formal probability sampling, substantially enhancing estimation accuracy and cost efficiency for sparse populations. Empirical results demonstrate that the method successfully replicates key findings from large-scale Pew Research Center surveys—such as religious denomination composition and participation rates—at a fraction of the cost of conventional survey methodologies.
📝 Abstract
Sampling geographically dispersed minority populations poses substantial challenges when individual group membership cannot be directly observed. Although stratified sampling can offer efficiency gains, these gains are typically modest unless the minority population is highly concentrated within a small number of strata. In this paper, we propose using Bayesian Improved Surname Geocoding (BISG) to enhance the efficiency of minority population sampling. BISG generates individual-level probabilities of minority group membership based on names and residential addresses. We incorporate these probabilities into a stratified Poisson probability sampling design. Applying the proposed approach to a national survey of Jewish Americans, we find that our estimates closely align with those from a large-scale Pew Research Center survey of the same population, which relied on a substantially more expensive sampling strategy involving geographic stratification and screening. At a fraction of the cost, our survey reproduces nearly identical patterns observed by Pew, including estimates of religious denominations and participation in specific religious activities.