Mixture modeling is a latent variable (i.e., a variable that cannot be measured directly) approach to quantitatively represent unobserved subpopulations within an overall population. It includes a range of cross-sectional (such as latent class [LCA] or latent profile analysis) and longitudinal (such as latent transition analysis) analyses and is often referred to as a "person-centered" approach to quantitative data. This research methods paper describes one type of mixture modeling, LCA, and provides examples of how this method can be applied to discipline-based education research in biology and other science, technology, engineering, and math (STEM) disciplines. This paper briefly introduces LCA, explores the affordances LCA provides for equity-focused STEM education research, highlights some of its limitations, and provides suggestions for researchers interested in exploring LCA as a method of analysis. We encourage discipline-based education researchers to consider how statistical analyses may conflict with their equity-minded research agendas while also introducing LCA as a method of leveraging the affordances of quantitative data to pursue research goals aligned with equity, inclusion, access, and justice agendas.