Title

Linking Methods for the Zinnes–Griggs Pairwise Preference IRT Model

Document Type

Article

Publication Date

2016

Keywords

linking, pairwise preference, ideal point, item response theory (IRT), Monte Carlo simulation, forced choice, noncognitive assessment

Digital Object Identifier (DOI)

https://doi.org/10.1177/0146621616675836

Abstract

Forced-choice item response theory (IRT) models are being more widely used as a way of reducing response biases in noncognitive research and operational testing contexts. As applications have increased, there has been a growing need for methods to link parameters estimated in different examinee groups as a prelude to measurement equivalence testing. This study compared four linking methods for the Zinnes and Griggs (ZG) pairwise preference ideal point model. A Monte Carlo simulation compared test characteristic curve (TCC) linking, item characteristic curve (ICC) linking, mean/mean (M/M) linking, and mean/sigma (M/S) linking. The results indicated that ICC linking and the simpler M/M and M/S methods performed better than TCC linking, and there were no substantial differences among the top three approaches. In addition, in the absence of possible contamination of the common (anchor) item subset due to differential item functioning, five items should be adequate for estimating the metric transformation coefficients. Our article presents the necessary equations for ZG linking and provides recommendations for practitioners who may be interested in developing and using pairwise preference measures for research and selection purposes.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Applied Psychological Measurement, v. 41, issue 2, p. 130-144

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