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Using Hierarchical Logistic Regression to Study DIF and DIF Variance in Multilevel Data
Author(s) -
Shear Benjamin R.
Publication year - 2018
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/jedm.12190
Subject(s) - differential item functioning , statistics , logistic regression , variance (accounting) , multilevel model , item response theory , regression analysis , econometrics , test (biology) , psychology , mathematics , psychometrics , accounting , business , paleontology , biology
When contextual features of test‐taking environments differentially affect item responding for different test takers and these features vary across test administrations, they may cause differential item functioning (DIF) that varies across test administrations. Because many common DIF detection methods ignore potential DIF variance, this article proposes the use of random coefficient hierarchical logistic regression (RC‐HLR) models to test for both uniform DIF and DIF variance simultaneously. A simulation study and real data analysis are used to demonstrate and evaluate the proposed RC‐HLR model. Results show the RC‐HLR model can detect uniform DIF and DIF variance more accurately than standard logistic regression DIF models in terms of bias and Type I error rates.