Bayesian Multilevel Modeling for the Intersections of Race and Gender

Abstract

Intersectionality is widely recognized as one of the largest contributions to the study of race and gender across the academy. However, the quantitative operationalization of intersectionality within Political Science is often unsatisfactory. I offer a method to account for the multidimensionality of identity which highlights the modifying nature of living with both different combinations of oppression, and privilege. I identify the Bayesian Multilevel Model as a superior tool to understanding intersectional dynamics in political behavior than conventional methods. By applying this method to two major published studies, I show how Bayesian Multilevel Models increase our inferential understanding of group-based heterogeneity in public opinion and political behavior. In doing so, the model better captures the interwoven nature of race and gender that often go unnoticed in Political Science research.