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| Modèle Rasch robuste× | Fonctionnement différentiel des items (FDI)× | |
|---|---|---|
| Domaine | Psychométrie | Psychométrie |
| Famille | Latent structure | Latent structure |
| Année d'origine≠ | 1982 | 1970s–1993 |
| Auteur d'origine≠ | Mislevy & Bock (robust ability estimation); broader robust IRT formalized through 1980s–2000s | William H. Angoff and colleagues (ETS); systematized by Holland & Wainer |
| Type≠ | Robust item calibration model | Item-level bias detection |
| Source fondatrice≠ | Strobl, C., Wickelmaier, F., & Zeileis, A. (2011). Accounting for individual differences in Bradley-Terry models by means of recursive partitioning. Journal of Educational and Behavioral Statistics, 36(2), 135–153. DOI ↗ | Holland, P. W. & Wainer, H. (Eds.) (1993). Differential Item Functioning. Lawrence Erlbaum Associates. ISBN: 978-0805809589 |
| Alias | robust IRT Rasch, robust dichotomous Rasch, outlier-resistant Rasch model, robust item calibration | DIF, item bias analysis, measurement non-equivalence, item-level measurement bias |
| Apparentées | 5 | 5 |
| Résumé≠ | The robust Rasch model applies the standard one-parameter logistic Rasch framework with estimation procedures designed to limit the influence of outlying item responses, aberrant respondents, or mild model violations, producing stable item and person parameter estimates that are less sensitive to data contamination than ordinary maximum likelihood or conditional maximum likelihood Rasch estimation. | 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. |
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