Comparer des méthodes

	Analyse factorielle confirmatoire robuste	Analyse Factorielle Confirmatoire (AFC)
Domaine	Statistique	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	1984–1994	1969
Auteur d'origine	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)	Karl Gustav Jöreskog
Type	Confirmatory latent variable model with robust estimation	Hypothesis-testing latent variable model
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.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
Alias	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA	CFA, confirmatory FA, measurement model, restricted 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.	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.