Gender: "culturally specific characteristics associated with masculinity and femininity . . . a wide range of . . . social roles assigned to men and women historically and cross-culturally" (Hawkesworth 2013, 36, emphasis added)Intersectionality: "that race, class, sexual orientation, nationality, and gender are not discrete markers of difference but rather intersection social structures of inequality experienced by individuals in specific social locations" (Ewig and Ferree 2013, 442, emphasis added)As illustrated by these definitions from the Oxford Handbook of Gender and Politics, context dependency is a defining characteristic of gender, since gender and intersectional inequalities differ spatially and temporarily in how they are organized. It is on this issue of context dependency where gender studies scholars often collide with (the application and interpretation) of regression-based statistical analysis in debates about quantitative methods (see Spierings 2012). In this reflection, I will briefly address where this tension comes from, focusing particularly on how (statistical) relationships vary in occurrence and strength between contexts. Next, I discuss three rather common archetypical responses to this tension that do little to resolve the issue. Subsequently, I focus on more profound solutions, including multilevel regression models, which challenge gender scholars to specify and develop their theoretical arguments on how and why relationships are context dependent. The different, bad, and better ways in which to deal with possible context dependency are illustrated by an assessment of their