期刊
SSM-POPULATION HEALTH
卷 16, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ssmph.2021.100977
关键词
Epidemiology; Intersectionality; Statistics; Research methods; Systematic review
资金
- National Institutes of Health [NIMH R01MH112420, NIA T32AG049663, NIAID K01 AI145572]
- National Institutes of Health, University of California, San Francisco, Center for AIDS Prevention Studies [P30MH062246]
Intersectionality theory investigates how systems of power and oppression influence marginalized groups, but there is a lack of consistent quantitative methods. This review identified eight classes of quantitative methods used in intersectionality research, with regression being the most common approach. Researchers should pay attention to maintaining the core tenets of intersectionality and avoid using methods that contradict its principles.
Intersectionality is a theoretical framework that investigates how interlocking systems of power and oppression at the societal level influence the lived experiences of historically and socially marginalized groups. Currently, there are no consistent or widely adopted quantitative methods to investigate research questions informed by intersectionality theory. The objective of this systematic review is to describe the current landscape of quantitative methods used to assess intersectionality and to provide recommendations on analytic best practices for future research. We searched PubMed, EMBASE, and the Web of Science in December 2019 to identify studies using analytic quantitative intersectionality approaches published up to December 2019 (PROSPERO CRD42020162686). To be included in the study, articles had to: (1) be empirical research, (2) use a quantitative statistical method, (3) be published in English, and (4) incorporate intersectionality. Our initial search yielded 1889 articles. After screening by title/abstract, methods, and full text review, our final analytic sample included 153 papers. Eight unique classes of quantitative methods were identified, with the majority of studies employing regression with an interaction term. We additionally identified several methods which appear to be at odds with the key tenets of intersectionality. As quantitative intersectionality continues to expand, careful attention is needed to avoid the dilution of the core tenets. Specifically, emphasis on social power is needed as methods continue to be adopted and developed. Additionally, clear explanation of the selection of statistical approaches is needed and, when using regression with interaction terms, researchers should opt for use of the additive scale. Finally, use of methods that are potentially at odds with the tenets of intersectionality should be avoided.
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