Master of Arts (M.A.)
Degree Granting Department
Stephen Stark, Ph.D.
Eun Sook Kim, Ph.D.
Oleksandr S. Chernyshenko, Ph.D.
Michael Coovert, Ph.D.
Constrained, Fee, and Sequential-Free baseline approaches, Differential Item Functioning, Multiple Indicator Multiple Cause Method
This study investigated the efficacy of multiple indicators, multiple causes (MIMIC) methods in detecting uniform and nonuniform differential item functioning (DIF) among multiple groups, where the underlying causes of DIF was different. Three different implementations of MIMIC DIF detection were studied: sequential free baseline, free baseline, and constrained baseline. In addition, the robustness of the MIMIC methods against the violation of its assumption, equal factor variance across comparison groups, was investigated. We found that the sequential-free baseline methods provided similar Type I error and power rates to the free baseline method with a designated anchor, and much better Type I error and power rates than the constrained baseline method across four groups, resulting from the co-occurrence background variables. But, when the equal factor variance assumption was violated, the MIMIC methods yielded the inflated Type I error. Also, the MIMIC procedure had problems correctly identifying the sources DIF, so further methodological developments are needed.
Scholar Commons Citation
Chun, Seokjoon, "Using MIMIC Methods to Detect and Identify Sources of DIF among Multiple Groups" (2014). Graduate Theses and Dissertations.