Comparer des méthodes

	Analyse factorielle confirmatoire multi-groupes (AFC-MG)	Analyse factorielle exploratoire (AFE)
Domaine	Psychométrie	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1971	—
Auteur d'origine	Karl Jöreskog	—
Type	Measurement model / invariance test	Latent variable / dimension reduction
Source fondatrice	Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.	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	MG-CFA, multi-group CFA, measurement invariance testing, multi-sample CFA	common factor analysis, açımlayıcı faktör analizi, factor analysis
Apparentées	6	4
Résumé	Multi-group confirmatory factor analysis tests whether a measurement model holds equivalently across two or more groups — such as cultures, genders, or time points. By imposing increasingly stringent equality constraints and comparing model fit, it determines whether comparisons of latent mean scores are justified.	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.