MAIHDA
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.
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出典
- 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. DOI: 10.1016/j.socscimed.2017.11.011 ↗
- Merlo, J. (2018). Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) within an intersectional framework. Social Science & Medicine, 203, 74–80. DOI: 10.1016/j.socscimed.2017.12.026 ↗
- Evans, C. R., Leckie, G., Subramanian, S. V., Bell, A., & Merlo, J. (2024). A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). SSM - Population Health, 26, 101664. DOI: 10.1016/j.ssmph.2024.101664 ↗
このページの引用方法
ScholarGate. (2026, June 22). Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). ScholarGate. https://scholargate.app/ja/gender-studies/maihda-intersectional-analysis
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- Gender Gap DecompositionGender Studies↔ 比較
- Intersectionality AnalysisGender Studies↔ 比較
- ロジスティック回帰研究統計↔ 比較