Comparer des méthodes

	Analyse factorielle confirmatoire bayésienne (AFCB)	Analyse Factorielle Exploratoire Bayésienne (AFEB)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	2007–2012	2004 (Bayesian formulation); factor analysis roots: 1904
Auteur d'origine	Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov	Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904)
Type	Bayesian latent variable model	Probabilistic latent variable model
Source fondatrice	Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232	Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67.
Alias	BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA	Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis
Apparentées	4	4
Résumé	Bayesian confirmatory factor analysis tests a pre-specified factor structure using Bayesian inference. Instead of point estimates with p-values, it produces full posterior distributions for loadings, factor correlations, and residual variances, allowing the researcher to incorporate prior knowledge and propagate parameter uncertainty naturally.	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.