Detecting DIF with the Multi-Unidimensional Pairwise Preference Model: Lord's Chi-square and IPR-NCDIF Methods.

Multidimensional forced choice (MFC) measures are gaining prominence in noncognitive assessment. Yet there has been little research on detecting differential item functioning (DIF) with models for forced choice measures. This research extended two well-known DIF detection methods to MFC measures. Specifically, the performance of Lord's chi-square and item parameter replication (IPR) methods with MFC tests based on the Multi-Unidimensional Pairwise Preference (MUPP) model was investigated. The Type I error rate and power of the DIF detection methods were examined in a Monte Carlo simulation that manipulated sample size, impact, DIF source, and DIF magnitude. Both methods showed consistent power and were found to control Type I error well across study conditions, indicating that established approaches to DIF detection work well with the MUPP model. Lord's chi-square outperformed the IPR method when DIF source was statement discrimination while the opposite was true when DIF source was statement threshold. Also, both methods performed similarly and showed better power when DIF source was statement location, in line with previous research. Study implications and practical recommendations for DIF detection with MFC tests, as well as limitations, are discussed.