Gender plays a significant role in an individual's experiences and behaviors, as well as their expectations of others. Researchers have long operationalized gender using limited, mutually exclusive categories that fail to capture the rich variability within a gender-diverse population. While open-ended responses or multi-item scales may be a socially progressive approach and necessary for some gender-based research (e.g., Bauer et al., 2017), it may be unsuitable and statistically unfeasible for quantitative researchers in other areas. We analyzed responses from over 700 gender-diverse participants in the U.S. on a series of unipolar scales (i.e., gender identity, expression, and perception by others) that granted participants the flexibility of selecting a comprehensive self-definition while still enabling quantitative analysis of group differences as well as capturing maximum within-group variability. Using a cluster analysis, we found that participants' responses were best represented by five categories: Archetypical Men (n = 169), Archetypical Women (n = 168), Intertypical Men (n = 158), Intertypical Women (n = 126), and Nonconforming (n = 85). We explore the variability of characteristics and beliefs (e.g., gender norms, sexist beliefs) within and between traditional sex and these new gender categories. In this paper, we discuss theoretical considerations for future research and how using this comprehensive operationalization of gender can expand our understanding of "gender differences'' beyond the current scientific assumptions and barriers.
Keywords: cluster analysis; gender differences; gender identity; gender spectrum; measurement.