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| MAIHDA× | 逻辑回归× | |
|---|---|---|
| 领域≠ | Gender Studies | 研究统计学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 2018 | 1958 |
| 提出者≠ | Clare Evans & S. V. Subramanian (building on Juan Merlo) | David Roxbee Cox |
| 类型≠ | Cross-classified random-effects multilevel model | Method |
| 开创性文献≠ | 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 ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 别名 | Intersectional MAIHDA, Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy, Intersectional Multilevel Analysis | logit model, binomial logistic regression, LR |
| 相关 | 3 | 3 |
| 摘要≠ | 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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