Date of Award
Spring 5-12-2023
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Educational Policy Studies
First Advisor
Dr. Hongli Li
Second Advisor
Dr. Audrey Leroux
Third Advisor
Dr. T. Chris Oshima
Fourth Advisor
Dr. Yinying Wang
Abstract
The Differential Test Functioning (DTF) statistic, with the Item Parameter Replication (IPR) procedure, can measure Differential Item Functioning (DIF) within the Differential Functioning of Items and Tests (DFIT) framework for Item Response Theory (IRT) models. However, it comes with many practical costs and theoretical assumptions. In some reasonably anticipated circumstances, the DTF statistic cannot be evaluated easily, and DFIT analysis consequentially remains beyond the scope of impacted IRT models. A straightforward, diagnostic statistic would add value to typical IRT model fitting. It was hypothesized that a statistic based on Mahalanobis distances and standard errors of an IRT model could perform as a reliable flag for likely DIF. To test this hypothesis, a Monte Carlo simulation study compared the performance of the traditional DTF measure to the new statistic. Although easy to calculate, the statistic proved unproductive in flagging models with DIF present. Related performance analysis and recommendations were provided.
DOI
https://doi.org/10.57709/35505275
Recommended Citation
Fikis, David, "A Comparison Study of the Differential Functioning of Tests Statistic and a New Mahalanobis Distance-Based Statistic For Pre-Screening Item Response Theory Models." Dissertation, Georgia State University, 2023.
doi: https://doi.org/10.57709/35505275
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