Mcmc Z-G: An Irt Computer Program For Forced-Choice Noncognitive Measurement

Keywords

forced-choice; item response theory; MCMC; noncognitive measurement; Zinnes–Griggs or Z-G model

Abstract

In recent years, there has been a surge of interest in measuring noncognitive constructs in educational and managerial/organizational settings. For the most part, these noncognitive constructs have been and continue to be measured using Likert-type (ordinal response) scales, which are susceptible to several types of response distortion. To deal with these response biases, researchers have proposed using forced-choice format, which requires respondents or raters to evaluate cognitive, affective, or behavioral descriptors presented in blocks of two or more. The workhorse for this measurement endeavor is the item response theory (IRT) model developed by Zinnes and Griggs (Z-G), which was first used as the basis for a computerized adaptive rating scale (CARS), and then extended by many organizational scientists. However, applications of the Z-G model outside of organizational contexts have been limited, primarily due to the lack of publicly available software for parameter estimation. This research effort addressed that need by developing a Markov chain Monte Carlo (MCMC) estimation program, called MCMC Z-G, which uses a Metropolis-Hastings-within-Gibbs algorithm to simultaneously estimate Z-G item and person parameters. This publicly available computer program MCMC Z-G can run on both Mac OS® and Windows® platforms.

Publication Date

10-1-2016

Publication Title

Applied Psychological Measurement

Volume

40

Issue

7

Number of Pages

551-553

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1177/0146621616663682

Socpus ID

84987942390 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84987942390

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