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Cheng, Ying; Shao, Can; Lathrop, Quinn N. – Educational and Psychological Measurement, 2016
Due to its flexibility, the multiple-indicator, multiple-causes (MIMIC) model has become an increasingly popular method for the detection of differential item functioning (DIF). In this article, we propose the mediated MIMIC model method to uncover the underlying mechanism of DIF. This method extends the usual MIMIC model by including one variable…
Descriptors: Test Bias, Models, Simulation, Sample Size
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Cheong, Yuk Fai; Kamata, Akihito – Applied Measurement in Education, 2013
In this article, we discuss and illustrate two centering and anchoring options available in differential item functioning (DIF) detection studies based on the hierarchical generalized linear and generalized linear mixed modeling frameworks. We compared and contrasted the assumptions of the two options, and examined the properties of their DIF…
Descriptors: Test Bias, Hierarchical Linear Modeling, Comparative Analysis, Test Items
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Jin, Ying; Kang, Minsoo – Large-scale Assessments in Education, 2016
Background: The current study compared four differential item functioning (DIF) methods to examine their performances in terms of accounting for dual dependency (i.e., person and item clustering effects) simultaneously by a simulation study, which is not sufficiently studied under the current DIF literature. The four methods compared are logistic…
Descriptors: Comparative Analysis, Test Bias, Simulation, Regression (Statistics)
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Sachse, Karoline A.; Roppelt, Alexander; Haag, Nicole – Journal of Educational Measurement, 2016
Trend estimation in international comparative large-scale assessments relies on measurement invariance between countries. However, cross-national differential item functioning (DIF) has been repeatedly documented. We ran a simulation study using national item parameters, which required trends to be computed separately for each country, to compare…
Descriptors: Comparative Analysis, Measurement, Test Bias, Simulation