Assessing the Validity of Data Synthesis Methods to Estimate Religious Populations

Abstract

The present study tests the validity of a data synthesis approach to population estimates of religiously defined groups. This is particularly important in places like the United States, where there is no definitive source of official data on its population's religious composition, and researchers must rely on costly, large-scale surveys, or congregational membership studies. Each approach has limitations, especially for estimation of small religious groups and for estimation within small geographic areas. Without official statistics, the degree of bias in estimates is unknown. Data synthesis, specifically Bayesian multilevel estimation with poststratification, offers a useful alternative that maximizes the utility of data across all sources to estimate multiple groups from the same sources of data. This method also facilitates comparison of groups. This study provides evidence of the validity of the approach by synthesizing data from Canada, a country that includes questions about religious identification in its national census.