RIM: A Random Item Mixture Model to Detect Differential Item Functioning

Autor/inn/en	Frederickx, Sofie; Tuerlinckx, Francis; De Boeck, Paul; Magis, David
Titel	RIM: A Random Item Mixture Model to Detect Differential Item Functioning
Quelle	In: Journal of Educational Measurement, 47 (2010) 4, S.432-457 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/j.1745-3984.2010.00122.x
Schlagwörter	Test Bias; Models; Test Items; Difficulty Level; Statistical Inference; Bayesian Statistics; Comparative Analysis + Suchen Sie Ihr Suchwort? Testkritik; Analogiemodell; Test content; Testaufgabe; Schwierigkeitsgrad; Inferential statistics; Schließende Statistik
Abstract	In this paper we present a new methodology for detecting differential 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 difference between the DIF class and the non-DIF class is that the item difficulties in the DIF class may differ 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 data set. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10