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Comparison of parameter estimation approaches for multi-unidimensional pairwise preference tests.

Naidan Tu1, Sean Joo2, Philseok Lee3

  • 1Department of Psychology, University of South Florida, Tampa, FL, USA. naidantu@usf.edu.

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Summary
This summary is machine-generated.

Multidimensional forced-choice (MFC) testing reduces response biases. A two-step estimation approach showed better statement parameter recovery than a direct method for MFC models, with similar overall scoring accuracy.

Keywords:
Generalized graded unfolding model (GGUM)Ideal point modelsItem response theory (IRT)Markov chain Monte Carlo (MCMC)Multidimensional forced choice (MFC)

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Psychological Testing

Background:

  • Multidimensional forced-choice (MFC) testing is a method to reduce response biases in noncognitive measurement.
  • Item response theory (IRT) research has explored obtaining person parameter estimates with normative properties using MFC models.
  • Recent research focuses on test construction processes and their influence on MFC scores.

Purpose of the Study:

  • To compare two parameter estimation approaches for the multi-unidimensional pairwise preference (MUPP) model.
  • To evaluate the efficacy of a "two-step" estimation process versus a "direct" estimation approach.
  • To examine how factors like test length, dimensionality, and sample size affect estimation accuracy.

Main Methods:

  • A Monte Carlo simulation study was conducted.
  • Two estimation approaches were compared: a "two-step" process (Stark et al., 2005) and a "direct" approach (Lee et al., 2019).
  • Parameters manipulated included test length, dimensionality, sample size, and correlations between person parameters.

Main Results:

  • Both estimation approaches demonstrated similar scoring accuracy.
  • The "two-step" approach exhibited superior statement parameter recovery compared to the "direct" approach.
  • The study identified factors influencing the performance of each estimation method.

Conclusions:

  • The choice of estimation approach impacts parameter recovery in MUPP models.
  • The "two-step" method offers advantages for statement parameter estimation in MFC test construction.
  • Findings inform best practices for MFC test development and scoring.