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Innovations in gender measurement challenge biological essentialism and the gender binary. However, in isolation, new measurements do not do enough, and new analytic approaches are necessary. Methodological innovation is particularly important in the analysis of gradational scales of masculinity and femininity because of the theoretical multiplicity and complexity of these constructs. Leveraging the new gradational measures in the 2024 General Social Survey, I demonstrate that latent class analysis can be used as an inductive data-driven approach to gain insight into gendered inequalities. This analysis is grounded in the theoretical complexity of masculinities and femininities. Through this feminist methodological approach, response patterns in the data determine masculinity and femininity---not the researcher---directly challenging the normalness of gender and how it gets researched.