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| Alpha de Cronbach bayésien× | Analyse factorielle confirmatoire bayésienne (AFCB)× | |
|---|---|---|
| Domaine | Psychométrie | Psychométrie |
| Famille | Latent structure | Latent structure |
| Année d'origine≠ | 2011 (Bayesian form); 1951 (classical alpha) | 2007–2012 |
| Auteur d'origine≠ | Padilla & Zhang (Bayesian adaptation); Cronbach (classical alpha, 1951) | Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov |
| Type≠ | Bayesian reliability estimation | Bayesian latent variable model |
| Source fondatrice≠ | Padilla, M. A., & Zhang, G. (2011). Estimating internal consistency using Bayesian methods. Journal of Modern Applied Statistical Methods, 10(1), 277–286. DOI ↗ | Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232 |
| Alias | Bayesian alpha, Bayesian internal consistency, Bayes-alpha, posterior alpha | BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA |
| Apparentées≠ | 2 | 4 |
| Résumé≠ | Bayesian Cronbach's alpha applies Bayesian inference to estimate the classical internal-consistency coefficient, yielding a full posterior distribution over alpha rather than a single point estimate. This allows researchers to quantify uncertainty with credible intervals and incorporate prior knowledge, making reliability assessment more informative — especially with small or skewed 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. |
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