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An Automated Algorithm for Classifying Expansive Responses for Gender Identity.

Alexis Ceja1,2, Sawye Raygani3, Bradley T Conner4

  • 1Department of Community Health Systems, School of Nursing, University of California, San Francisco, San Francisco, CA, USA.

Psychology of Sexual Orientation and Gender Diversity
|November 21, 2025
PubMed
Summary
This summary is machine-generated.

An automated algorithm was developed to categorize diverse gender identities for sexual and gender minority (SGM) individuals. This method accurately classifies nearly half of SGM participants, preventing data loss and improving statistical power in research.

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Area of Science:

  • Social Sciences
  • Psychology
  • Sociology

Background:

  • Current gender identity measures lack methods for analyzing expanded responses from sexual and gender minority (SGM) individuals.
  • SGM individuals desire opportunities to provide detailed and multiple gender identity responses.
  • Without proper categorization, a significant portion of SGM participants may be excluded from analyses or misclassified.

Purpose of the Study:

  • To develop an automated algorithm for generating analyzable gender identity categories from expanded responses.
  • To increase statistical power in research involving SGM populations by accurately classifying diverse gender identities.
  • To create five distinct gender categories: cisgender men, cisgender women, gender expansive individuals, transgender men, and transgender women.

Main Methods:

  • An automated algorithm was created using participants' expanded gender identity responses and sex assigned at birth.
  • Data were collected from a cohort of 6,312 SGM individuals between June 2020 and June 2021.
  • Chi-square tests were employed to assess the association between algorithm-generated categories and participant selections, and to identify demographic differences.

Main Results:

  • Forty-six percent of the sample utilized expanded response options (e.g., multiple selections, write-in), which would otherwise be unanalyzable.
  • The algorithm successfully classified participants into five categories, demonstrating high concordance (97-99%) with single-category selections, except for gender expansive individuals (74.3%).
  • The algorithm showed a significant relationship with participant-selected categories (χ2 [20] = 19,000, p < .001).

Conclusions:

  • The developed algorithm effectively incorporates expanded gender identity responses, preventing data loss and improving inclusivity in SGM research.
  • This automated approach enhances statistical power by creating meaningful, analyzable categories from complex gender identity data.
  • The algorithm provides a robust method for classifying gender identity among diverse SGM populations.