RIM: A random item mixture model to detect differential item functioning.

[en] In this article we present a new methodology for detecting Di erential Item Functioning (DIF). We introduce a DIF model, called the Random Item Mixture (RIM), that is based on a Rasch model with random item difficulties (besides the common random person abilities). In addition, a mixture model is assumed for the item difficulties such that the items may belong to one of two classes: a DIF or a non-DIF class. The crucial di fference between the DIF class and the non-DIF class is that the item difficulties in the DIF class may di ffer according to the observed person groups while they are equal across the person groups for the items from the non-DIF class. Statistical inference for the RIM is carried out in a Bayesian framework. The performance of the RIM is evaluated using a simulation study in which it is compared with traditional procedures, like the Likelihood Ratio test, the Mantel-Haenszel procedure and the standardized p-DIF procedure. In this comparison, the RIM performs better than the other methods. Finally, the usefulness of the model is also demonstrated on a real life dataset.

Disciplines :

Mathematics

Author, co-author :

Frederickx, Sofie

Tuerlinckx, Francis

De Boeck, Paul

Magis, David ; Université de Liège - ULiège > Département de mathématique > Statistique mathématique

Language :

English

Title :

RIM: A random item mixture model to detect differential item functioning.

Alternative titles :

[fr] RIM: un modèle de mixture à items aléatoires pour détecter le fonctionnement différentiel d'items

Publication date :

2010

Journal title :

Journal of Educational Measurement

ISSN :

0022-0655

Publisher :

Blackwell Publishing

Volume :

Issue :

Pages :

432-457

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 18 June 2010

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