Disentangling the Heterogeneity in Minority Stress: A Data Mining Approach

Area of Science:

• Social Psychology
• Public Health
• Sociology

Background:

• Minority stress theory links discrimination to distress in minoritized groups, including sexual minorities.
• Intersectionality is crucial for understanding distress and discrimination among sexual minorities.
• Existing research often treats discrimination and distress as uniform, potentially masking intersectional effects.

Purpose of the Study:

• To identify sociodemographic correlates of specific discrimination and distress patterns.
• To examine how intersections of sociodemographic factors influence minority stress in sexual minority populations.
• To uncover novel insights into population subgroups experiencing unique forms of discrimination and distress.

Main Methods:

• Utilized a multivariate conditional inference tree, a novel data mining technique.
• Analyzed a nationally representative sample of 1518 US adults identifying as sexual minorities (2016-2018).
• Investigated sociodemographic factors including age, race, gender identity, sexual identity, education, urbanicity, and poverty.

Main Results:

• Identified three subgroups with elevated minority stress: general adults, adults in poverty, and bisexual/other sexual minority adults.
• Demonstrated that minority stress among sexual minority adults in the US is heterogeneous.
• Revealed specific patterns of distress and discrimination linked to intersections of sociodemographic factors.

Conclusions:

• Minority stress is a complex and varied experience within the sexual minority population.
• Conditional inference tree modeling is a valuable tool for intersectionality research.
• Findings can inform targeted policy and intervention development for diverse sexual minority subgroups.