GGUM-RANK Statement and Person Parameter Estimation With Multidimensional Forced Choice Triplets.

ABSTRACT: Historically, multidimensional forced choice (MFC) measures have been criticized because conventional scoring methods can lead to ipsativity problems that render scores unsuitable for interindividual comparisons. However, with the recent advent of item response theory (IRT) scoring methods that yield normative information, MFC measures are surging in popularity and becoming important components in high-stake evaluation settings. This article aims to add to burgeoning methodological advances in MFC measurement by focusing on statement and person parameter recovery for the GGUM-RANK (generalized graded unfolding-RANK) IRT model. Markov chain Monte Carlo (MCMC) algorithm was developed for estimating GGUM-RANK statement and person parameters directly from MFC rank responses. In simulation stud

SUBMITTER: Lee P 

PROVIDER: S-EPMC6463341 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature