Comparer des méthodes

	Modélisation multiniveau	Analyse de la variance (ANOVA)	Régression logistique
Domaine	Statistiques de recherche	Statistiques de recherche	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	1992	1925	1958
Auteur d'origine	Anthony Bryk and Stephen Raudenbush	Ronald A. Fisher	David Roxbee Cox
Type	Method	Method	Method
Source fondatrice	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	HLM, mixed-effects models, random effects models, MLM	ANOVA, F-test	logit model, binomial logistic regression, LR
Apparentées	3	4	3
Résumé	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.	ANOVA is a parametric statistical method developed by Ronald A. Fisher in 1925 that tests whether means differ significantly across three or more independent groups. By partitioning total variance into between-group and within-group components, ANOVA determines whether observed differences are likely due to treatment effects or random variation, making it fundamental to comparative research across medicine, psychology, agriculture, and engineering.	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.