Publications

Intersectionality is widely recognized as one of the largest contributions to the study of race, gender, and class across the academy. However, the quantitative operationalization of intersectionality within political science is often unsatisfactory. I provide evidence that the Bayesian Multilevel Model is an accessible and flexible tool for understanding intersectional dynamics in political behavior. Using both a synthetic simulation and a real-world example with the American National Election Survey (ANES), I show how Bayesian Multilevel Models increase our inferential understanding of group-based heterogeneity in public opinion and political behavior. Conventional techniques, such as interaction terms, frequently yield estimates that are obscured by considerable noise, making it challenging to discern meaningful patterns. In contrast, the Bayesian Multilevel Model excels at revealing underlying patterns in small sample-size environments.

This chapter reviews the major advances in accounting for intersectionality empirically and embracing methodological pluralism within Political Science and related Social Sciences. Intersectionality, or approaching identity categories rooted in structural power such as race, gender, and class as inseparable, remains a site of intellectual promise particularly because of its utility for explaining the big questions in American politics. I focus on intersectional quantitative methods as a site for new innovations as it is the natural step after demonstrating the current literature's advances of frameworks to operationalize intersectionality.

Class is a known determinant of political attitudes and behaviors, yet it is often overlooked in quantitative intersectional research due to challenges in operationalization. This oversight stems from two main issues: inconsistent definitions of class in survey instruments and sparse data. In this paper, we propose defining class as a context-dependent latent variable, estimated through mixture models. Traditional methods typically isolate a single socioeconomic status (SES) or subjective social status (SSS) measure as an independent variable, but mixture models integrate multiple facets of SES and SSS, identifying the component of class most pertinent to the political outcome being studied. Coupled with intersectional approaches like Bayesian Multilevel Models, this framework allows for a more comprehensive representation of relevant identities in data sparse environments. We demonstrate our method with two empirical examples using 2020 American National Election Studies data, showing that the significance of SES or SSS elements varies depending on the outcome. Our results also indicate that not accounting for class in intersectional modeling leads to biased estimates.