Comparer des méthodes

	Analyse d'items robuste	Fonctionnement différentiel des items (FDI)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	1980s–2000s	1970s–1993
Auteur d'origine	Robust methods tradition (Huber, Hampel, Tukey); applied to item analysis by Wilcox and colleagues	William H. Angoff and colleagues (ETS); systematized by Holland & Wainer
Type	Diagnostic / item-level evaluation	Item-level bias detection
Source fondatrice	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838	Holland, P. W. & Wainer, H. (Eds.) (1993). Differential Item Functioning. Lawrence Erlbaum Associates. ISBN: 978-0805809589
Alias	robust item statistics, outlier-resistant item analysis, robust classical item analysis	DIF, item bias analysis, measurement non-equivalence, item-level measurement bias
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.	Differential item functioning identifies test or survey items that behave differently for examinees from different groups — such as gender, ethnicity, or language background — after controlling for the underlying ability or trait being measured. DIF analysis is essential for fairness evaluation in educational testing and psychological scale development.