arbiter
JournalsAccount

Improving Latent Trait Estimation in Multidimensional Forced Choice Measures: Latent Regression Multi-Unidimensional Pairwise Preference Model

Sean Joo et al.

Applied Psychological Measurement2026https://doi.org/10.1177/01466216251415189article
AJG 2ABDC B
Weight
0.50

Abstract

The field of psychometrics has made remarkable progress in developing item response theory (IRT) models for analyzing multidimensional forced choice (MFC) measures. This study introduces an innovative method that enhances the latent trait estimation of the Multi-Unidimensional Pairwise Preference (MUPP) model by incorporating latent regression modeling. To validate the efficacy of the new method, we conducted a comprehensive simulation study. The results of the study provide compelling evidence that the proposed latent regression MUPP (LR-MUPP) model significantly improves the accuracy of the latent trait estimation. This study opens new avenues for future research and encourages further development and refinement of MFC IRT models and their applications.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1177/01466216251415189

Or copy a formatted citation

@article{sean2026,
  title        = {{Improving Latent Trait Estimation in Multidimensional Forced Choice Measures: Latent Regression Multi-Unidimensional Pairwise Preference Model}},
  author       = {Sean Joo et al.},
  journal      = {Applied Psychological Measurement},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/01466216251415189},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Improving Latent Trait Estimation in Multidimensional Forced Choice Measures: Latent Regression Multi-Unidimensional Pairwise Preference Model

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.