Graduation Year
2021
Document Type
Thesis
Degree
M.A.
Degree Name
Master of Arts (M.A.)
Degree Granting Department
Psychology
Major Professor
Stephen Stark, Ph.D.
Committee Member
Seang-Hwane Joo, Ph.D.
Committee Member
Brenton M. Wiernik, Ph.D.
Committee Member
Marina A. Bornovalova, Ph.D.
Keywords
Markov chain Monte Carlo (MCMC), Item Response Theory (IRT), Generalized Graded Unfolding Model (GGUM), Forced Choice
Abstract
Multidimensional forced choice (MFC) testing has been proposed as a way of reducing response biases in noncognitive measurement. Although early item response theory (IRT) research focused on illustrating that trait scores with normative properties could be obtained using various MFC models and formats, more recent attention has been devoted to exploring the processes involved in test construction and how that influences MFC scores. This research compared two approaches for estimating Multi-Unidimensional Pairwise Preference model (MUPP; Stark et al., 2005) parameters based on the Generalized Graded Unfolding Model (GGUM; Roberts et al., 2000). More specifically, we compared the efficacy of statement and person parameter estimation based on a “two-step” process, developed by Stark et al. (2005) with a more recently developed “direct” estimation approach (Lee et al., 2019) in a Monte Carlo study that also manipulated test length, test dimensionality, sample size, and the correlations between generating thetas for each dimension. Results indicated that the two approaches had similar scoring accuracy, although the two-step approach had better statement parameter recovery than the direct approach. Implications, limitations, and recommendations for future MFC research and practice are discussed.
Scholar Commons Citation
Tu, Naidan, "Comparison of Parameter Estimation Approaches for Multi-Unidimensional Pairwise Preference Tests" (2021). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/9725
