Latent variable and clustering methods in intersectionality research: systematic review of methods applications.

PURPOSE: An intersectionality framework has been increasingly incorporated into quantitative study of health inequity, to incorporate social power in meaningful ways. Researchers have identified "person-centered" methods that cluster within-individual characteristics as appropriate to intersectionality. We aimed to review their use and match with theory. METHODS: We conducted a multidisciplinary systematic review of English-language quantitative studies wherein authors explicitly stated an intersectional approach, and used clustering methods. We extracted study characteristics and applications of intersectionality. RESULTS: 782 studies with quantitative applications of intersectionality were identified, of which 16 were eligible: eight using latent class analysis, two latent profile analysis, and six clustering methods. Papers used cross-sectional data (100.0%) primarily had U.S. lead authors (68.8%) and were published within psychology, social sciences, and health journals. While 87.5% of papers defined intersectionality and 93.8% cited foundational authors, engagement with intersectionality method literature was more limited. Clustering variables were based on social identities/positions (e.g., gender), dimensions of identity (e.g., race centrality), or processes (e.g., stigma). Results most commonly included four classes/clusters (60.0%), which were frequently used in additional analyses. These described sociodemographic differences across classes/clusters, or used classes/clusters as an exposure variable to predict outcomes in regression analysis, structural equation modeling, mediation, or survival analysis. Author rationales for method choice included both theoretical/intersectional and statistical arguments. CONCLUSION: Latent variable and clustering methods were used in varied ways in intersectional approaches, and reflected differing matches between theory and methods. We highlight situations in which these methods may be advantageous, and missed opportunities for additional uses.

Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods.

BACKGROUND: Intersectionality is a theoretical framework rooted in the premise that human experience is jointly shaped by multiple social positions (e.g. race, gender), and cannot be adequately understood by considering social positions independently. Used widely in qualitative studies, its uptake in quantitative research has been more recent. OBJECTIVES: To characterize quantitative research applications of intersectionality from 1989 to mid-2020, to evaluate basic integration of theoretical frameworks, and to identify innovative methods that could be applied to health research. METHODS: Adhering to PRISMA guidelines, we conducted a systematic review of peer-reviewed articles indexed within Scopus, Medline, ProQuest Political Science and Public Administration, and PsycINFO. Original English-language quantitative or mixed-methods research or methods papers that explicitly applied intersectionality theoretical frameworks were included. Experimental studies on perception/stereotyping and measures development or validation studies were excluded. We extracted data related to publication, study design, quantitative methods, and application of intersectionality. RESULTS: 707 articles (671 applied studies, 25 methods-only papers, 11 methods plus application) met inclusion criteria. Articles were published in journals across a range of disciplines, most commonly psychology, sociology, and medical/life sciences; 40.8% studied a health-related outcome. Results supported concerns among intersectionality scholars that core theoretical tenets are often lost or misinterpreted in quantitative research; about one in four applied articles (26.9%) failed to define intersectionality, while one in six (17.5%) included intersectional position components not reflective of social power. Quantitative methods were simplistic (most often regression with interactions, cross-classified variables, or stratification) and were often misapplied or misinterpreted. Several novel methods were identified. CONCLUSIONS: Intersectionality is frequently misunderstood when bridging theory into quantitative methodology. Further work is required to (1) ensure researchers understand key features that define quantitative intersectionality analyses, (2) improve reporting practices for intersectional analyses, and (3) develop and adapt quantitative methods.