<p>This study seeks to gain a better understanding of gender diversity and its implications for educational inequality research by exploring how different methods of gender categorization shape analytical outcomes. Using large-scale, nationally representative data from Mexico’s National Survey on Sexual and Gender Diversity (ENDISEG), the study compares three gender categorization strategies: existing classifications of gender trajectories—cisgender, transgender, or something else—a binary/non-binary framework, and a multidimensional model that considers both gender trajectory and binary identification simultaneously. These two axes reflect key dimensions of gender normativity: alignment with sex assigned at birth and conformity to binary gender categories. Linear models are employed to assess disparities in post-secondary education completion. Results show that trans individuals face significant educational disadvantages, while non-binary identification alone appears unrelated to educational attainment. However, a more nuanced picture emerges when accounting for gender trajectory: trans non-binary individuals face the steepest penalties. These findings underline the complexity of gender categorization and the analytical value of a multidimensional approach when measuring gender diversity and understanding inequalities across gender categories.</p>