Title

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

Document Type

Article

Publication Date

2018

Keywords

multidimensional forced choice, noncognitive assessment, parameter recovery, item response theory, Markov chain Monte Carlo, ideal point, faking

Digital Object Identifier (DOI)

https://doi.org/10.1177/0146621618768294

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 studies, it was examined that how the psychometric properties of statements composing MFC items, test length, and sample size influenced statement and person parameter estimation; and it was explored for the benefits of measurement using MFC triplets relative to pairs. To demonstrate this methodology, an empirical validity study was then conducted using an MFC triplet personality measure. The results and implications of these studies for future research and practice are discussed.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Applied Psychological Measurement, v. 43, issue 3, p. 226-240

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