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

Abstract

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.

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

“…As a framework for incorporating social power, heterogeneity, and community relevance into health research, intersectionality has gained prominence as an analytic approach to qualitative and more recently quantitative methods [1].…”

Can intersectionality help with understanding and tackling health inequalities? Perspectives of professional stakeholders

“…Hankivsky et al [28] offer several examples from the Canadian context where the application of intersectionality has led to equity-informed policy actions focused on diverse populations, such as in relation to HIV testing and prevention, aboriginal health and palliative care. Bowleg [29] suggests that intersectionality can encourage us to examine the substantial heterogeneity within taken-for-granted categories, such as "women", and the interplay of micro-level with macro-level factors producing disparate health outcomes (2012:1268). She summarizes five main benefits of intersectionality for public health: it provides a unifying encourage an approach that is sensitive to subgroup inequalities and the processes that generate them.…”

Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake

“…Since women of colour in the U.S. are often confronted with poverty, childcare responsibilities and the lack of formal vocational training, the lived experiences of this population group were likely to be different when compared to other female population groups (Crenshaw 1989). Meanwhile, the concept of intersectionality is gaining popularity across different disciplines (Bauer 2021) including public health (Bowleg 2012). Most notably, Hancock (2007), McCall (2005) and Bauer (2021) have advanced the translation of the principles of intersectionality into quantitative research.…”

“…From a statistical point of view, classification tree analysis is a quantitative, non-parametric, exploratory method suitable for analysis of interactions from an intersectionality-informed perspective, which can support the detection of subgroups with higher or lower prevalence of certain diseases or related risk factors [11,12]. In contrast to other parametric procedures, classification tree analysis makes no distributional assumptions, neither on the outcome nor on the predictor variables, and is not affected by collinearities, outliers, heteroscedasticity, or distributional error structures [13].…”