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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.