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Multifaceted Gender Identity Measurement As An Alternative to Forced-Choice Assessments

Abstract Number:

3371 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Thomas Belin (1), Hilary Aralis (1), Zichen Liu (1), Andrew Chuang (1), Sung-Jae Lee (1), Donatello Telesca (1)

Institutions:

(1) University of California, Los Angeles, Los Angeles, California, USA

Co-Author(s):

Hilary Aralis  
University of California, Los Angeles
Zichen Liu  
University of California, Los Angeles
Andrew Chuang  
University of California, Los Angeles
Sung-Jae Lee  
University of California, Los Angeles
Donatello Telesca  
University of California, Los Angeles

First Author:

Thomas Belin  
University of California, Los Angeles

Presenting Author:

Thomas Belin  
University of California-Los Angeles

Abstract Text:

While suggesting specific question wording for surveys collecting data on gender identity and sexual orientation, a 2022 National Academies of Sciences, Engineering, and Medicine report on "Measuring Sex, Gender Identity, and Sexual Orientation" recognized limitations of "forced-choice measurement" using multiple-choice items and recommended further research into representing nonbinary gender identity and gender fluidity. Here we describe a framework for "Multifaceted Gender Identity Measurement" (M-GIM) asking respondents about the extent to which they agree or disagree with a series of gender-identity and sexual-orientation prompts, anticipating that identifiable clusters will emerge from patterns in ordinal responses without requiring individuals to self-classify into one of a limited number of categories. After highlighting the appeal of keeping such queries free of the implicit constraints and negative associations built into mutually-exclusive response options, the presentation will discuss a conceptual framework for investigating disparities in quality-of-life outcomes across population subgroups characterized by similar gender-identity or sexual-orientation profiles.

Keywords:

gender identity|sexual orientation|ordinal data|cluster analysis|nonbinary|gender fluidity

Sponsors:

Social Statistics Section

Tracks:

Statistical Measurement of Social Issues and Trends

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