Comparer des méthodes

	Analyse factorielle exploratoire robuste	Analyse factorielle confirmatoire robuste
Domaine	Psychométrie	Statistique
Famille	Latent structure	Latent structure
Année d'origine	2000–2003	1984–1994
Auteur d'origine	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)
Type	Latent variable / dimension reduction (robust)	Confirmatory latent variable model with robust estimation
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.	Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.
Alias	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA
Apparentées	4	6
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.	Robust confirmatory factor analysis fits a pre-specified factor structure to observed data while correcting standard errors and goodness-of-fit statistics for violations of multivariate normality. It is the preferred variant of CFA whenever Likert-type, skewed, or kurtotic indicators make the classical normal-theory estimator unreliable.