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Linking Methods for the Zinnes-Griggs Pairwise Preference IRT Model.

Philseok Lee1, Seang-Hwane Joo2, Stephen Stark2

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Summary
This summary is machine-generated.

Linking parameters in forced-choice item response theory (IRT) models is crucial for measurement equivalence. Item characteristic curve (ICC) and simpler mean/mean (M/M), mean/sigma (M/S) linking methods are effective for the Zinnes and Griggs (ZG) model.

Keywords:
Monte Carlo simulationforced choiceideal pointitem response theory (IRT)linkingnoncognitive assessmentpairwise preference

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Psychological Testing

Background:

  • Forced-choice item response theory (IRT) models are increasingly used to mitigate response biases in noncognitive assessments.
  • Linking parameters across different examinee groups is essential for measurement equivalence testing in these models.

Purpose of the Study:

  • To compare the performance of four different linking methods for the Zinnes and Griggs (ZG) pairwise preference ideal point model.
  • To provide practical recommendations for linking parameters in ZG models for research and selection.

Main Methods:

  • A Monte Carlo simulation was employed to evaluate four linking methods: Test Characteristic Curve (TCC) linking, Item Characteristic Curve (ICC) linking, Mean/Mean (M/M) linking, and Mean/Sigma (M/S) linking.
  • The simulation assessed the accuracy of estimating metric transformation coefficients under varying conditions.

Main Results:

  • Item Characteristic Curve (ICC) linking, Mean/Mean (M/M) linking, and Mean/Sigma (M/S) linking demonstrated superior performance compared to Test Characteristic Curve (TCC) linking.
  • No significant differences were found among the top three methods (ICC, M/M, M/S).
  • Approximately five common items are sufficient for accurate metric transformation coefficient estimation when differential item functioning is absent.

Conclusions:

  • The study recommends ICC, M/M, or M/S linking methods for the ZG pairwise preference ideal point model due to their effectiveness and relative simplicity.
  • Practitioners can confidently use these methods for developing and applying pairwise preference measures in research and selection contexts.