Comparer des méthodes

	Analyse de corrélation canonique bayésienne (Bayesian CCA)	Analyse Factorielle Confirmatoire (AFC)
Domaine	Statistique	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	2005-2013	1969
Auteur d'origine	Francis Bach & Michael Jordan (probabilistic formulation, 2005); Klami, Virtanen & Kaski (fully Bayesian treatment, 2013)	Karl Gustav Jöreskog
Type	Latent variable model / dimensionality reduction	Hypothesis-testing latent variable model
Source fondatrice	Bach, F. R. & Jordan, M. I. (2005). A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of California, Berkeley.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
Alias	Bayesian CCA, probabilistic CCA, BCCA	CFA, confirmatory FA, measurement model, restricted factor analysis
Apparentées	5	4
Résumé	Bayesian canonical correlation analysis is a probabilistic generative model that identifies shared latent structure between two or more sets of observed variables. It extends classical CCA by placing priors on model parameters, enabling principled uncertainty quantification, automatic determination of the number of shared dimensions, and robustness when sample sizes are small relative to dimensionality.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.