ERIC Number: EJ1353902
Record Type: Journal
Publication Date: 2022-Dec
Pages: 31
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0013-1644
EISSN: EISSN-1552-3888
Available Date: N/A
A Regression Discontinuity Design Framework for Controlling Selection Bias in Evaluations of Differential Item Functioning
Koziol, Natalie A.; Goodrich, J. Marc; Yoon, HyeonJin
Educational and Psychological Measurement, v82 n6 p1247-1277 Dec 2022
Differential item functioning (DIF) is often used to examine validity evidence of alternate form test accommodations. Unfortunately, traditional approaches for evaluating DIF are prone to selection bias. This article proposes a novel DIF framework that capitalizes on regression discontinuity design analysis to control for selection bias. A simulation study was performed to compare the new framework with traditional logistic regression, with respect to Type I error and power rates of the uniform DIF test statistics and bias and root mean square error of the corresponding effect size estimators. The new framework better controlled the Type I error rate and demonstrated minimal bias but suffered from low power and lack of precision. Implications for practice are discussed.
Descriptors: Regression (Statistics), Item Analysis, Validity, Testing Accommodations, Test Items, Guidelines, Error of Measurement, Simulation, Comparative Analysis, Effect Size, Statistical Bias, Sample Size, Test Length
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
Authoring Institution: N/A
Grant or Contract Numbers: 1749275
Author Affiliations: N/A