Complex survey sampling techniques (e.g., clustering, stratification, oversampling) allow for cost efficient estimation for large, dispersed populations. As such, they are frequently used in demographic, health, and public opinion survey research settings. In recent years, Bayesian statistical methods, due to their flexibility and intuitive interpretation relative to frequentist methods, have become increasingly popular for analyzing complex survey sample data; however, complex survey sampling introduces certain features (e.g., unequal selection probabilities, dependencies between observations) that violate traditional statistical assumptions and can bias survey estimates.