So sánh phương pháp

	MAIHDA	Intersectionality Analysis
Lĩnh vực	Gender Studies	Gender Studies
Họ	Regression model	Process / pipeline
Năm ra đời	2018	1989
Người khởi xướng	Clare Evans & S. V. Subramanian (building on Juan Merlo)	Kimberlé Crenshaw
Loại	Cross-classified random-effects multilevel model	Critical qualitative analytic framework
Công trình gốc	Evans, C. R., Williams, D. R., Onnela, J.-P., & Subramanian, S. V. (2018). A multilevel approach to modeling health inequalities at the intersection of multiple social identities. Social Science & Medicine, 203, 64–73.	Crenshaw, K. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43(6), 1241–1299.
Tên gọi khác	Intersectional MAIHDA, Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy, Intersectional Multilevel Analysis	Intersectional Analysis, Intersectionality Framework, Intersectional Qualitative Analysis
Liên quan	3	4
Tóm tắt	MAIHDA — Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy — is a quantitative method for studying intersectional inequalities. Introduced for intersectionality by Clare Evans and S. V. Subramanian in 2018, building on Juan Merlo's discriminatory-accuracy framework, it treats the many strata formed by crossing social categories (for example gender × race/ethnicity × education) as level-2 units in a multilevel model, then partitions outcome variation between and within those strata to assess how much intersectional position predicts the outcome.	Intersectionality analysis is a critical qualitative framework that examines how multiple social categories — such as race, gender, class, sexuality, and disability — intersect and operate together to shape lived experience, advantage, and disadvantage. Coined by legal scholar Kimberlé Crenshaw in 1989 and 1991, it rejects single-axis analysis that treats categories one at a time, insisting instead that overlapping systems of power produce qualitatively distinct positions that cannot be understood by adding the categories separately.