Doctor of Philosophy (Ph.D.)
Degree Granting Department
Stephen Stark, Ph.D.
Michael Coovert, Ph.D.
Walter Borman, Ph.D.
Joseph Vandello, Ph.D.
Robert Dedrick, Ph.D.
Oleksandr S. Chernyshenko, Ph.D.
Item Information, Item Response Theory, Monte Carlo Simulation, Multidimensional Forced Choice Format, Parameter Recovery
To control various response biases and rater errors in noncognitive assessment, multidimensional forced choice (MFC) measures have been proposed as an alternative to single-statement Likert-type scales. Historically, MFC measures have been criticized because conventional scoring methods can lead to ipsativity problems that render scores unsuitable for inter-individual comparisons. However, with the recent advent of classical test theory and item response theory scoring methods that yield normative information, MFC measures are surging in popularity and becoming important components of personnel and educational assessment systems. This dissertation presents developments concerning a GGUM-based MFC model henceforth referred to as the GGUM-RANK. Markov Chain Monte Carlo (MCMC) algorithms were developed to estimate GGUM-RANK statement and person parameters directly from MFC rank responses, and the efficacy of the new estimation algorithm was examined through computer simulations and an empirical construct validity investigation. Recently derived GGUM-RANK item information functions and information indices were also used to evaluate overall item and test quality for the empirical study and to give insights into differences in scoring accuracy between two-alternative (pairwise preference) and three-alternative (triplet) MFC measures for future work. This presentation concludes with a discussion of the research findings and potential applications in workforce and educational setting.
Scholar Commons Citation
Lee, Philseok, "Investigating Parameter Recovery and Item Information for Triplet Multidimensional Forced Choice Measure: An Application of the GGUM-RANK Model" (2016). Graduate Theses and Dissertations.