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| Analyse factorielle confirmatoire bayésienne (AFCB)× | Analyse factorielle exploratoire (AFE)× | |
|---|---|---|
| Domaine≠ | Psychométrie | Statistique |
| Famille | Latent structure | Latent structure |
| Année d'origine≠ | 2007–2012 | — |
| Auteur d'origine≠ | Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov | — |
| Type≠ | Bayesian latent variable model | Latent variable / dimension reduction |
| Source fondatrice≠ | Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232 | Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. DOI ↗ |
| Alias≠ | BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA | common factor analysis, açımlayıcı faktör analizi, 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. | Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance. |
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