Naidan Tu, Lavanya S Kumar, Sean Joo, Stephen Stark
Applications of multidimensional forced choice (MFC) testing have increased considerably over the last 20 years. Yet there has been little, if any, research on methods for linking the parameter estimates from different samples. This research addressed that important need by extending four widely used methods for unidimensional linking and comparing the efficacy of new estimation algorithms for MFC linking coefficients based on the Multi-Unidimensional Pairwise Preference model (MUPP). More specifically, we compared the efficacy of multidimensional test characteristic curve (TCC), item characteristic curve (ICC; Haebara, 1980), mean/mean (M/M), and mean/sigma (M/S) methods in a Monte Carlo study that also manipulated test length, test dimensionality, sample size, percentage of anchor items, and linking scenarios...
May 2024: Applied Psychological Measurement