Identifying Basic Classes of Sexual Orientation with Latent Profile Analysis: Developing the Multivariate Sexual Orientation Classification System

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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Notes

  1. 1.

    Given its clarity of meaning and widespread use throughout the literature, we have chosen to use the term “opposite sex” when we are referring to individuals with a different gender identity. Per a reviewer’s thoughtful suggestion, we considered using the term “different sex” to be more inclusive of non-binary gender identities. Unfortunately, we found that term to be confusing in a number of places (e.g., when referring to “different sex partners” in the last year), and so we decided to continue using the most common term for this construct.

  2. 2.

    A common concern with survey research is inattentive responding. Thankfully, rates of excessively inattentive responding for online surveys (similar in content, format, and length to the current study) tend to be fairly low (roughly 3–5%; see Maniaci & Rogge, 2014a). Despite such low rates, removing inattentive participants can lead to slight increases in power for correlational analyses (Maniaci & Rogge, 2014a). Identifying incomplete participants and participants completing the survey too rapidly are accepted methods of identifying such individuals (see Maniaci & Rogge, 2014b for a review as well as Maniaci & Rogge, 2014a for a review and supporting results). The cutscores used in the current article were developed by the second author across over 30 online surveys collecting data from over 40,000 participants. On average, participants answer an average of 11 questions per minute on surveys like the one presented in this manuscript. Thus, we used a cutscore of 26 questions per minute or more to identify participants rushing through the survey too quickly (at a pace more than double the average, strongly suggesting that they cannot be reading each item carefully). We also used a cutscore of completing at least 70% of the baseline survey as that helps to quickly separate out individuals who drop out of the survey after the first couple pages and to retain those with truly usable multivariate data.

  3. 3.

    The ANCOVAs controlled for the six demographic variables showing significant differences across the latent groups–thereby helping to defray the impact of those demographic differences when examining group means on the remaining variables.

  4. 4.

    Notably, this discussion is limited to a self-report survey method as it represents the most commonly used method of assessing sexual orientation; other methods (interview, psychophysiological, behavioral) are beyond the scope of this work.

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Correspondence to Nicole Legate.

Appendix

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See Table 5.

Table 5 ANOVA, χ2, and ANCOVA analyses examining the M-SOCS classes of sexuality

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Legate, N., Rogge, R.D. Identifying Basic Classes of Sexual Orientation with Latent Profile Analysis: Developing the Multivariate Sexual Orientation Classification System. Arch Sex Behav 48, 1403–1422 (2019). https://doi.org/10.1007/s10508-018-1336-y

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Keywords

  • Sexual orientation
  • Gay
  • Lesbian
  • Bisexual
  • Mostly heterosexual
  • Health disparities