Author ORCID Identifier
Date Available
10-21-2020
Year of Publication
2020
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Education
Department/School/Program
Education Sciences
Advisor
Dr. Michael D. Toland
Co-Director of Graduate Studies
Dr. Xin Ma
Abstract
This is a simulation study that evaluates the performances of two models for the detection of uniform differential item functioning (DIF). Simulated data are generated by a multilevel partial credit model (MLPCM). The purpose of this study was to compare the accuracy of two DIF detection procedures, hierarchical ordinal logistic regression (HOLR) for multilevel data and multilevel generalized Mantel-Haenszel (MGMH: French & Finch, 2013; French, Finch, & Imekus, 2019). Conditions manipulated were the number of participants per cluster (20, 40), number of clusters (50, 100, 200), DIF magnitude (0, .4, .8), and magnitude of intraclass correlation coefficient (.05, .25, .45). Furthermore, only one grouping variable was used within-groups. Data was simulated using R (R Core Team, 2019), whereas analyses will be performed using SAS 9.4 (SAS Institute, 2013) and R. In general, HOLR maintains the Type I error rate better than MGMH and HOLR has more power than MGMH under most simulation conditions.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2020.073
Recommended Citation
Hanley, Carol, "Assessing the Performance of Two Procedures for Detecting Differential Item Functioning within the Multilevel Partial Credit Model" (2020). Theses and Dissertations--Education Sciences. 58.
https://uknowledge.uky.edu/edsc_etds/58
Included in
Educational Assessment, Evaluation, and Research Commons, Educational Psychology Commons
