Journal article
Li Guan, Tianjun Sun, Nathan T. Carter
2021
2021
Semantic Scholar
DOI
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Cite
APA
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Guan, L., Sun, T., & Carter, N. T. (2021). MUPPscore: An R script for expected a posteriori scoring of multi-unidimensional pairwise preference items.
Chicago/Turabian
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Guan, Li, Tianjun Sun, and Nathan T. Carter. “MUPPscore: An R Script for Expected a Posteriori Scoring of Multi-Unidimensional Pairwise Preference Items” (2021).
MLA
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Guan, Li, et al. MUPPscore: An R Script for Expected a Posteriori Scoring of Multi-Unidimensional Pairwise Preference Items. 2021.
BibTeX Click to copy
@article{li2021a,
title = {MUPPscore: An R script for expected a posteriori scoring of multi-unidimensional pairwise preference items},
year = {2021},
author = {Guan, Li and Sun, Tianjun and Carter, Nathan T.}
}
Abstract
In this manual, we present a flexible and freely available tool for obtaining latent trait scores from multi-unidimensional pairwise preference (MUPP) tests: An R script named MUPPscore. The development of the MUPPscore script provides a solution to the issue that is the previously inconvenient estimation of forced choice item pairs. Instead of using the computationally-intensive multidimensional Bayes modal procedure, the MUPPscore script employs the expected a posterior (EAP) scoring procedure, which provides plausible latent trait score estimates and is also consistent with scoring algorithms used in existing software programs intended for single stimulus measures (e.g., GGUM2004, IRTPRO). The MUPPscore script also returns the empirical marginal reliability of EAP theta estimates and outputs a series of files that can be used to easily create and modify three-dimensional surface charts for plotting MUPP item response function (IRF) in Microsoft Excel.