Comparer des méthodes

	Analyse factorielle confirmatoire robuste	Analyse factorielle exploratoire (AFE)
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1984–1994	—
Auteur d'origine	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)	—
Type	Confirmatory latent variable model with robust estimation	Latent variable / dimension reduction
Source fondatrice	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.	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
Alias	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA	common factor analysis, açımlayıcı faktör analizi, factor analysis
Apparentées	6	4
Résumé	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.	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.