Detection of Uniform and Nonuniform Differential Item Functioning by Item-Focused Trees

Autor/inn/en	Berger, Moritz; Tutz, Gerhard
Titel	Detection of Uniform and Nonuniform Differential Item Functioning by Item-Focused Trees
Quelle	In: Journal of Educational and Behavioral Statistics, 41 (2016) 6, S.559-592 (34 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998616659371
Schlagwörter	Test Bias; Regression (Statistics); Nonparametric Statistics; Statistical Analysis; Visual Aids; Test Items; Goodness of Fit + Suchen Sie Ihr Suchwort? Testkritik; Regression; Regressionsanalyse; Statistische Analyse; Anschauungsmaterial; Test content; Testaufgabe
Abstract	Detection of differential item functioning (DIF) by use of the logistic modeling approach has a long tradition. One big advantage of the approach is that it can be used to investigate nonuniform (NUDIF) as well as uniform DIF (UDIF). The classical approach allows one to detect DIF by distinguishing between multiple groups. We propose an alternative method that is a combination of recursive partitioning methods (or trees) and logistic regression methodology to detect UDIF and NUDIF in a nonparametric way. The output of the method are trees that visualize in a simple way the structure of DIF in an item showing which variables are interacting in which way when generating DIF. In addition, we consider a logistic regression method, in which DIF can be induced by a vector of covariates, which may include categorical but also continuous covariates. The methods are investigated in simulation studies and illustrated by two applications. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01