A Residual-Based Differential Item Functioning Detection Framework in Item Response Theory

Autor/inn/en	Lim, Hwanggyu; Choe, Edison M.; Han, Kyung T.
Titel	A Residual-Based Differential Item Functioning Detection Framework in Item Response Theory
Quelle	In: Journal of Educational Measurement, 59 (2022) 1, S.80-104 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/jedm.12313
Schlagwörter	Test Items; Item Response Theory; Identification; Robustness (Statistics); Simulation; Regression (Statistics); Standards + Suchen Sie Ihr Suchwort? Test content; Testaufgabe; Item-Response-Theorie; Identifikation; Identifizierung; Widerstandsfähigkeit; Simulation program; Simulationsprogramm; Regression; Regressionsanalyse; Standard
Abstract	Differential item functioning (DIF) of test items should be evaluated using practical methods that can produce accurate and useful results. Among a plethora of DIF detection techniques, we introduce the new "Residual DIF" (RDIF) framework, which stands out for its accessibility without sacrificing efficacy. This framework consists of three item response theory (IRT) residual statistics: RDIF[subscript R], RDIF[subscript S], and RDIF[subscript RS]. We conducted a simulation study with a 40-item test to assess the performance of RDIF in comparison with the Mantel-Haenszel, logistic regression, and IRT-based likelihood ratio test methods. Even when analyzing small sample sizes, the results revealed RDIF[subscript RS] to be the most robust DIF detection statistic with strict control of Type I error across all simulated conditions when paired with the purification procedure. Also, RDIF[subscript R] and RDIF[subscript S] proved to be powerful indicators of uniform and nonuniform DIF, respectively. Therefore, RDIF[subscript RS] should serve as the primary flagging criterion, whereas RDIF[subscript R] and RDIF[subscript S] best serve as indicators of DIF type. An empirical DIF study also showed that the RDIF framework could perform satisfactorily with real data from a large-scale assessment. Overall, the RDIF framework demonstrated its potential as a new standard for IRT-based DIF detection methodology. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01