Comparer des méthodes

	Analyse factorielle exploratoire robuste	Analyse Factorielle Confirmatoire (AFC)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	2000–2003	1969
Auteur d'origine	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)	Karl Gustav Jöreskog
Type	Latent variable / dimension reduction (robust)	Hypothesis-testing latent variable model
Source fondatrice	Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
Alias	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation	CFA, confirmatory FA, measurement model, restricted factor analysis
Apparentées	4	4
Résumé	Robust exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard EFA but replaces classical covariance estimation with robust counterparts — such as minimum covariance determinant or iteratively reweighted least squares — so that a small fraction of atypical cases cannot distort the recovered factor loadings.	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.