Comparer des méthodes

	Analyse d'items robuste	Théorie de la réponse aux items (TRI)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	1980s–2000s	1952–1968
Auteur d'origine	Robust methods tradition (Huber, Hampel, Tukey); applied to item analysis by Wilcox and colleagues	Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models)
Type	Diagnostic / item-level evaluation	Probabilistic measurement model
Source fondatrice	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838	Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley.
Alias	robust item statistics, outlier-resistant item analysis, robust classical item analysis	IRT, latent trait theory, item characteristic curve theory, modern test theory
Apparentées	5	5
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.	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.