Detection of Differential Item Functioning for More than Two Groups: A Monte Carlo Comparison of Methods

Autor/in	Finch, W. Holmes
Titel	Detection of Differential Item Functioning for More than Two Groups: A Monte Carlo Comparison of Methods
Quelle	In: Applied Measurement in Education, 29 (2016) 1, S.30-45 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2015.1102916
Schlagwörter	Test Bias; Monte Carlo Methods; Comparative Analysis; Population Groups; Regression (Statistics); Statistical Analysis + Suchen Sie Ihr Suchwort? Testkritik; Monte-Carlo-Methode; Regression; Regressionsanalyse; Statistische Analyse
Abstract	Differential item functioning (DIF) assessment is a crucial component in test construction, serving as the primary way in which instrument developers ensure that measures perform in the same way for multiple groups within the population. When such is not the case, scores may not accurately reflect the trait of interest for all individuals in the population. Most DIF research has focused on the two groups case. However, in practice researchers may wish to investigate DIF for more than two groups; that is, for examinee ethnicity, nation of origin, or treatment condition, among others. DIF detection methods for such cases have been proposed, but little empirical work has been done to investigate their performance. Therefore, the goal of the current study was to use a simulation methodology to compare four proposed methods for assessing DIF in the multiple groups case, including the Generalized Mantel-Haenszel test, Generalized Logistic Regression, Lord's chi-square test, and the multiple group alignment procedure. Results showed that the Generalized Mantel-Haenszel and alignment procedures provided the optimal combination of Type I error control and power. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01