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Updated: Jan 23, 2026

Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Identifying Basic Classes of Sexual Orientation with Latent Profile Analysis: Developing the Multivariate Sexual
Nicole Legate1, Ronald D Rogge2
1Department of Psychology, Illinois Institute of Technology, 3424 S. State St., Chicago, IL, 60616, USA. nlegate@iit.edu.
Abstract:
Despite considerable progress, research on sexual minorities has been hindered by a lack of clarity and consistency in defining sexual minority groups. Further, despite recent recommendations to assess the three main dimensions of sexual orientation-identity, behavior, and attraction-it remains unclear how best to integrate such multivariate information to define discrete sexual orientation groups, particularly when identity and behavior fail to match. The current study used a data-driven approach to identify a parsimonious set of sexual orientation classes. Latent profile analysis (LPA) was run within a large (N = 3182) and sexually diverse sample, using dimensions of sexual identity, behavior, and attraction as predictors. LPAs supported four fundamental sexual orientation classes not only in the overall sample, but also when conducted separately in men (n = 980) and women (n = 2175): heterosexual, homosexual, bisexual, and heteroflexible (a class representing individuals who self-identify as heterosexual or mostly heterosexual but report moderate same-sex sexual behavior and attraction). Heterosexuals reported the highest levels of psychological functioning and lowest risk behaviors. Homosexuals showed similarly high levels of psychological functioning to heterosexuals, but higher levels of risk behaviors. Bisexuals and heteroflexibles showed similarly low levels of psychological functioning and high risk taking. To facilitate applications of this classification approach, the study developed the Multivariate Sexual Orientation Classification System, reproducing the four LPA groups with 97% accuracy (kappa = .95) using just two items. Implications of this classification approach are discussed.
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