Comparer des méthodes

	Analyse bayésienne des items	Analyse factorielle confirmatoire bayésienne (AFCB)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	1990s–2000s	2007–2012
Auteur d'origine	Originated in Bayesian psychometrics literature, developed extensively by Jean-Paul Fox and colleagues	Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov
Type	Bayesian inference / item-level diagnostics	Bayesian latent variable model
Source fondatrice	Fox, J.-P. (2010). Bayesian Item Response Modeling: Theory and Applications. Springer.	Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232
Alias	BIA, Bayesian classical item analysis, Bayesian item statistics, Bayesian item-level diagnostics	BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA
Apparentées	4	4
Résumé	Bayesian item analysis applies Bayesian inference to estimate item-level statistics — difficulty, discrimination, and distractor effectiveness — by combining observed response data with prior knowledge. It produces full posterior distributions over item parameters rather than single point estimates, providing richer uncertainty information especially with small samples.	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.