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| Analyse de modération (interaction)× | Régression logistique× | |
|---|---|---|
| Domaine≠ | Inférence causale | Statistiques de recherche |
| Famille≠ | Regression model | Process / pipeline |
| Année d'origine≠ | 2018 | 1958 |
| Auteur d'origine≠ | Aiken & West (1991); Hayes (PROCESS, 2018) | David Roxbee Cox |
| Type≠ | Linear regression with interaction term | Method |
| Source fondatrice≠ | Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis (2nd ed.). Guilford Press. ISBN: 978-1462534654 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias≠ | interaction analysis, moderated regression, simple moderation, Düzenleyici Değişken Analizi (Moderation / İnteraksiyon) | logit model, binomial logistic regression, LR |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | Moderation analysis tests whether the effect of a predictor X on an outcome Y changes with the level of a third variable W, the moderator. It is estimated within a regression framework through an interaction term X×W, popularised by Aiken & West (1991) and Hayes's PROCESS macro (2018). | 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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