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Modeling Multidimensional Forced Choice Measures with the Zinnes and Griggs Pairwise Preference Item Response Theory
Seang-Hwane Joo1, Philseok Lee2, Stephen Stark3
1The University of Kansas.
Multivariate Behavioral Research
|August 9, 2021
Summary
A new ideal point item response theory (IRT) model, ZG-MUPP, was developed for multidimensional forced choice (MFC) measures. This model accurately estimates parameters and yields comparable scores to existing methods, even with small sample sizes.
Area of Science:
- Psychometrics
- Educational Measurement
- Item Response Theory
Background:
- Multidimensional forced choice (MFC) measures are increasingly used in assessments.
- Existing models like MUPP and TIRT have limitations in capturing complex response patterns.
- There is a need for robust IRT models tailored for MFC data.
Purpose of the Study:
- To develop and validate a new ideal point item response theory (IRT) model for multidimensional forced choice (MFC) measures.
- To adapt existing IRT frameworks (Zinnes and Griggs, Multi-Unidimensional Pairwise Preference) into a novel ZG-MUPP model.
- To assess the psychometric properties and parameter estimation accuracy of the ZG-MUPP model.
Main Methods:
- Developed the ZG-MUPP model by adapting the Zinnes and Griggs (ZG) and Multi-Unidimensional Pairwise Preference (MUPP) models.
- Derived the information function to evaluate psychometric properties of MFC measures.
- Employed Markov chain Monte Carlo (MCMC) for model parameter estimation.
- Conducted a simulation study with varying sample sizes, item numbers, and parameter ranges.
Main Results:
- The ZG-MUPP model demonstrated accurate parameter estimation, even with a sample size as low as 500.
- Simulation results confirmed the model's efficacy across different experimental conditions.
- Empirical scores from the ZG-MUPP model were comparable to those obtained from the MUPP and Thurstonian IRT (TIRT) models.
Conclusions:
- The ZG-MUPP model offers a viable and accurate approach for analyzing multidimensional forced choice measures.
- The model provides reliable parameter estimates and comparable scores, enhancing psychometric evaluations.
- This research contributes a valuable tool for researchers and practitioners using MFC assessments.
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