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

Naidan Tu1, Lavanya S Kumar1, Sean Joo2

  • 1University of South Florida, FL, USA.

Applied Psychological Measurement
|April 8, 2024
PubMed
Summary
This summary is machine-generated.

Linking parameter estimates from different samples in multidimensional forced choice (MFC) testing is crucial. The item characteristic curve (ICC) method proved most effective for linking MFC coefficients, outperforming other tested methods.

Keywords:
ideal pointitem response theorymeasurement invariancemultidimensional forced choicemultidimensional linking

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Psychological Testing

Background:

  • Multidimensional forced choice (MFC) testing applications have grown significantly.
  • A research gap exists in methods for linking parameter estimates across different samples in MFC testing.

Purpose of the Study:

  • To extend existing unidimensional linking methods for MFC testing.
  • To compare the efficacy of estimation algorithms for MFC linking coefficients using the Multi-Unidimensional Pairwise Preference (MUPP) model.

Main Methods:

  • A Monte Carlo simulation study was conducted.
  • Four linking methods were evaluated: multidimensional test characteristic curve (TCC), item characteristic curve (ICC), mean/mean (M/M), and mean/sigma (M/S).
  • Study parameters included test length, dimensionality, sample size, anchor item percentage, and linking scenarios.

Main Results:

  • The ICC method demonstrated superior performance compared to M/M and M/S methods.
  • The TCC method was found to be the least effective.
  • Increased items per dimension and anchor item percentage reduced performance differences between ICC, M/M, and M/S methods.

Conclusions:

  • The ICC method is recommended for linking MUPP coefficients.
  • Practical recommendations for MUPP linking are provided based on study findings.
  • Limitations of the study are discussed.