60: Novel Methods for Incorporating Complex Sample Designs into Bayesian Analyses

Music City Center 

Bayesian statistical methods have become increasingly popular in health and social science research for their intuitive framework together with their ability to accommodate hierarchical data structures and missing data. However, accounting for complex sample design elements such as weights, stratification, and clustering is not straightforward. We propose a novel extension of the finite population Bayesian bootstrap (FPBB) where synthetic populations are generated and posterior draws obtained assuming a simple random sample design are re-weighted using importance sampling. We evaluate our approach through a simulation study of a stratified sample in a misspecified linear modeling setting and compare results to an existing method. Results demonstrate adequate coverage, with only mildly inflated empirical variances. Compared to the other existing method, our approach is computationally faster and produces comparably unbiased estimates and coverage. This simple and generalizable approach will have significant implications for survey data analysts by allowing for implementation of complex Bayesian models while accounting for sampling designs.

Bayesian statistics

complex survey design

finite population

Bayesian bootstrap 

Survey Research Methods Section