Comparer des méthodes

	Analyse d'items robuste	Analyse factorielle exploratoire (AFE)
Domaine	Psychométrie	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1980s–2000s	—
Auteur d'origine	Robust methods tradition (Huber, Hampel, Tukey); applied to item analysis by Wilcox and colleagues	—
Type	Diagnostic / item-level evaluation	Latent variable / dimension reduction
Source fondatrice	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838	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 item statistics, outlier-resistant item analysis, robust classical item analysis	common factor analysis, açımlayıcı faktör analizi, factor analysis
Apparentées	5	4
Résumé	Robust item analysis applies outlier-resistant statistical methods to the evaluation of individual test or scale items. Instead of classical means and Pearson correlations — both sensitive to extreme scores — it uses trimmed means, Winsorized correlations, or M-estimators to obtain item difficulty and item-total discrimination indices that remain stable when respondent distributions are skewed or contaminated by outliers.	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.