Comparer des méthodes

	Analyse factorielle confirmatoire multiniveau (MCFA)	Modélisation multiniveau
Domaine	Psychométrie	Statistiques de recherche
Famille	Latent structure	Process / pipeline
Année d'origine	1994	1992
Auteur d'origine	Bengt O. Muthen	Anthony Bryk and Stephen Raudenbush
Type	Latent variable model / measurement model	Method
Source fondatrice	Muthen, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	MCFA, multilevel measurement model, two-level CFA, hierarchical CFA	HLM, mixed-effects models, random effects models, MLM
Apparentées	6	3
Résumé	Multilevel confirmatory factor analysis tests a pre-specified factor structure while simultaneously accounting for the non-independence of observations caused by clustered data. It decomposes item variance into within-group and between-group components, fitting a separate measurement model at each level, making it the standard tool for validating psychometric scales administered within natural groups such as classrooms, clinics, or organisations.	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.