Comparer des méthodes

	Analyse Factorielle Exploratoire Bayésienne (AFEB)	Théorie de la réponse aux items (TRI)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	2004 (Bayesian formulation); factor analysis roots: 1904	1952–1968
Auteur d'origine	Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904)	Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models)
Type	Probabilistic latent variable model	Probabilistic measurement model
Source fondatrice	Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67.	Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley.
Alias	Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis	IRT, latent trait theory, item characteristic curve theory, modern test theory
Apparentées	4	5
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.	Item response theory models the probability that a respondent answers an item correctly (or endorses it) as a function of the respondent's latent trait level and the item's own statistical properties — difficulty, discrimination, and guessing. Unlike classical test theory, IRT places persons and items on the same scale, yielding measurement that is sample-independent for items and test-independent for persons.