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Analyse Factorielle Exploratoire Bayésienne (AFEB)×Analyse factorielle confirmatoire bayésienne (AFCB)×
DomainePsychométriePsychométrie
FamilleLatent structureLatent structure
Année d'origine2004 (Bayesian formulation); factor analysis roots: 19042007–2012
Auteur d'origineLopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904)Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov
TypeProbabilistic latent variable modelBayesian latent variable model
Source fondatriceLopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67. link ↗Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232
AliasBayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysisBCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA
Apparentées44
RésuméBayesian 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.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.
ScholarGateJeu de données
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  1. v1
  2. 2 Sources
  3. PUBLISHED

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ScholarGateComparer des méthodes: Bayesian EFA · Bayesian Confirmatory Factor Analysis. Consulté le 2026-06-15 sur https://scholargate.app/fr/compare