Comparer des méthodes

	Cross-Classified Multilevel Models in Education	Modélisation multiniveau
Domaine	Education	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	1993	1992
Auteur d'origine	Multilevel modeling community (Raudenbush; Goldstein; Rasbash & Browne)	Anthony Bryk and Stephen Raudenbush
Type	Multilevel model with units cross-classified by two or more non-nested groupings	Method
Source fondatrice	Goldstein, H. (2011). Multilevel Statistical Models (4th ed.). Wiley. ISBN: 9780470748657	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	Cross-Classified Random Effects Models, CCREM, Cross-Classified Multilevel Modeling, Multiple Membership Cross-Classified Models	HLM, mixed-effects models, random effects models, MLM
Apparentées	4	3
Résumé	Cross-classified multilevel models extend hierarchical linear modeling to situations where units belong to two or more groupings that do not nest neatly inside one another. In education, students are often classified by both school and neighborhood, or by primary and secondary school across time — classifications that cut across each other rather than form a clean hierarchy. These models assign a random effect to each classification simultaneously, partitioning variance among them and yielding correct inferences where a purely nested model would be misspecified.	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.