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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- 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
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