Comparer des méthodes

	Analyse Factorielle Confirmatoire (AFC)	Modélisation multiniveau
Domaine	Psychométrie	Statistiques de recherche
Famille	Latent structure	Process / pipeline
Année d'origine	1969	1992
Auteur d'origine	Karl Gustav Jöreskog	Anthony Bryk and Stephen Raudenbush
Type	Hypothesis-testing latent variable model	Method
Source fondatrice	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Alias	CFA, confirmatory FA, measurement model, restricted factor analysis	HLM, mixed-effects models, random effects models, MLM
Apparentées	4	3
Résumé	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.	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.