Comparer des méthodes

	Analyse factorielle confirmatoire bayésienne (AFCB)	Théorie de la généralisabilité (Théorie G)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	2007–2012	1963–1972
Auteur d'origine	Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov	Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam
Type	Bayesian latent variable model	Variance-components reliability model
Source fondatrice	Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232	Cronbach, L. J., Gleser, G. C., Nanda, H. & Rajaratnam, N. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles. Wiley.
Alias	BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA	G-theory, G-study / D-study framework, variance components reliability
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.	Generalizability Theory is a psychometric framework that decomposes observed score variance into multiple sources — persons, items, raters, occasions, and their interactions — using analysis of variance. It replaces the single reliability coefficient of classical test theory with a family of coefficients that tell researchers how well scores generalize across different measurement conditions.