Comparer des méthodes

	Développement d'échelles bayésiennes	Analyse factorielle confirmatoire bayésienne (AFCB)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	1990s–2000s	2007–2012
Auteur d'origine	Harold Jeffreys, expanded into psychometrics by Mislevy and colleagues	Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov
Type	Bayesian probabilistic scale construction	Bayesian latent variable model
Source fondatrice	De Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. Guilford Press. ISBN: 978-1593858698	Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232
Alias	Bayesian psychometric scale construction, Bayesian measurement modeling, Bayesian item development, BSD	BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA
Apparentées	5	4
Résumé	Bayesian scale development applies Bayesian statistical inference to the construction and evaluation of psychometric scales. Rather than relying on single point estimates of item and person parameters, it produces full posterior distributions that quantify uncertainty, incorporate prior knowledge, and support principled decisions about item retention, reliability, and validity in small or complex 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.