Comparer des méthodes

	Analyse Factorielle Exploratoire Bayésienne (AFEB)	Analyse Factorielle Confirmatoire (AFC)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	2004 (Bayesian formulation); factor analysis roots: 1904	1969
Auteur d'origine	Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904)	Karl Gustav Jöreskog
Type	Probabilistic latent variable model	Hypothesis-testing latent variable model
Source fondatrice	Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
Alias	Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis	CFA, confirmatory FA, measurement model, restricted factor analysis
Apparentées	4	4
Résumé	Bayesian exploratory factor analysis applies a full probabilistic framework to the common factor model. By placing prior distributions over factor loadings and unique variances, it yields posterior distributions rather than point estimates, quantifies uncertainty around every loading, and can treat the number of factors as an unknown to be inferred from data.	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.