Abstract
Persistent gender inequalities in STEM organizations suggest that inclusion is not experienced equally, despite extensive diversity initiatives. Although prior research links gender to workplace inclusion, the robustness of these associations across alternative conceptualizations and analytic decisions remains unclear. Using a large organizational dataset from a STEM organization (leaders n = 182; followers n = 812 nested in teams), we examine how gender relates to employee inclusion. Adopting a multiverse framework, we systematically vary gender operationalizations (categorical, dissimilarity-based, and queer-inclusive continuous deviation measures), inclusion outcomes, and team-level specifications. Across 648 defensible models, we find no single robust gender effect. Instead, the presence, direction, and magnitude of associations depend on how gender and inclusion are operationalized and on team gender composition. Continuous deviation-based measures yield more frequent and stronger associations than categorical approaches. These findings underscore the importance of transparent measurement and analytic choices when studying inclusion in gendered organizational contexts.