Multidimensional Extension of Multiple Indicators Multiple Causes Models to Detect DIF

Autor/inn/en	Lee, Soo; Bulut, Okan; Suh, Youngsuk
Titel	Multidimensional Extension of Multiple Indicators Multiple Causes Models to Detect DIF
Quelle	In: Educational and Psychological Measurement, 77 (2017) 4, S.545-569 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164416651116
Schlagwörter	Test Bias; Test Items; Models; Item Response Theory; Statistical Analysis; Test Length; Correlation; Sample Size; Differences + Suchen Sie Ihr Suchwort? Testkritik; Test content; Testaufgabe; Analogiemodell; Item-Response-Theorie; Statistische Analyse; Korrelation; Unterscheiden
Abstract	A number of studies have found multiple indicators multiple causes (MIMIC) models to be an effective tool in detecting uniform differential item functioning (DIF) for individual items and item bundles. A recently developed MIMIC-interaction model is capable of detecting both uniform and nonuniform DIF in the unidimensional item response theory (IRT) framework. The goal of the current study is to extend the MIMIC-interaction model for detecting DIF in the context of multidimensional IRT modelling and examine the performance of the multidimensional MIMIC-interaction model under various simulation conditions with respect to Type I error and power rates. Simulation conditions include DIF pattern and magnitude, test length, correlation between latent traits, sample size, and latent mean differences between focal and reference groups. The results of this study indicate that power rates of the multidimensional MIMIC-interaction model under uniform DIF conditions were higher than those of nonuniform DIF conditions. When anchor item length and sample size increased, power for detecting DIF increased. Also, the equal latent mean condition tended to produce higher power rates than the different mean condition. Although the multidimensional MIMIC-interaction model was found to be a reasonably useful tool for identifying uniform DIF, the performance of the model in detecting nonuniform DIF appeared to be questionable. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01