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. 2024 May;48(3):104-124.
doi: 10.1177/01466216241238741. Epub 2024 Mar 11.

Linking Methods for Multidimensional Forced Choice Tests Using the Multi-Unidimensional Pairwise Preference Model

Naidan Tu  1 , Lavanya S Kumar  1 , Sean Joo  2 , Stephen Stark  1
Affiliations

Linking Methods for Multidimensional Forced Choice Tests Using the Multi-Unidimensional Pairwise Preference Model

Naidan Tu et al. Appl Psychol Meas. 2024 May.

Abstract

Applications of multidimensional forced choice (MFC) testing have increased considerably over the last 20 years. Yet there has been little, if any, research on methods for linking the parameter estimates from different samples. This research addressed that important need by extending four widely used methods for unidimensional linking and comparing the efficacy of new estimation algorithms for MFC linking coefficients based on the Multi-Unidimensional Pairwise Preference model (MUPP). More specifically, we compared the efficacy of multidimensional test characteristic curve (TCC), item characteristic curve (ICC; Haebara, 1980), mean/mean (M/M), and mean/sigma (M/S) methods in a Monte Carlo study that also manipulated test length, test dimensionality, sample size, percentage of anchor items, and linking scenarios. Results indicated that the ICC method outperformed the M/M method, which was better than the M/S method, with the TCC method being the least effective. However, as the number of items "per dimension" and the percentage of anchor items increased, the differences between the ICC, M/M, and M/S methods decreased. Study implications and practical recommendations for MUPP linking, as well as limitations, are discussed.

Keywords: ideal point; item response theory; measurement invariance; multidimensional forced choice; multidimensional linking.

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Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Mean RMSEs, ABSs, and PABs for A and K across conditions for the M/M, M/S, ICC, and TCC linking methods.
Figure 2.
Figure 2.
Mean RMSEs, ABSs, and PABs for A and K across conditions for the 25% and 100% anchor items.
Figure 3.
Figure 3.
Mean RMSEs, ABSs, and PABs for A and K across conditions for the 6 and 12 IPDs.
Figure 4.
Figure 4.
Mean RMSEs, ABSs, and PABs for A and K across conditions for the 400 and 800 sample sizes.
Figure 5.
Figure 5.
Mean RMSEs, ABSs, and PABs for A and K across conditions for the HL, VL1, VL2, VL3, and VL4 linking scenarios.
Figure 6.
Figure 6.
Mean RMSEs, ABSs, and PABs for A and K across conditions for the 6 and 12 test dimensions.
See this image and copyright information in PMC

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