Comparer des méthodes

	Analyse factorielle exploratoire robuste	Théorie de la réponse aux items (TRI)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	2000–2003	1952–1968
Auteur d'origine	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)	Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models)
Type	Latent variable / dimension reduction (robust)	Probabilistic measurement 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.	Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley.
Alias	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation	IRT, latent trait theory, item characteristic curve theory, modern test theory
Apparentées	4	5
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.	Item response theory models the probability that a respondent answers an item correctly (or endorses it) as a function of the respondent's latent trait level and the item's own statistical properties — difficulty, discrimination, and guessing. Unlike classical test theory, IRT places persons and items on the same scale, yielding measurement that is sample-independent for items and test-independent for persons.