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Area of Science:

  • Psychometrics
  • Item Response Theory
  • Statistical Modeling

Background:

  • Item response tree (IRT) models offer nuanced analysis by separating agreement and intensity decisions.
  • Directional invariance is crucial, as item and person parameters may vary by agreement direction.

Purpose of the Study:

  • Investigate a novel family of IRT models for unfolding responses.
  • Incorporate hyperbolic cosine and graded response models for agreement and intensity decisions.
  • Examine item- and person-level directional invariance within this framework.

Main Methods:

  • Developed a family of IRT models integrating hyperbolic cosine and graded response models.
  • Conducted a simulation study to assess parameter recovery using a Bayesian approach with JAGS.
  • Applied the models to two real-world datasets, comparing models with varying directional invariance and process assumptions.

Main Results:

  • The proposed IRT models effectively handle unfolding response processes.
  • Parameter recovery was evaluated through simulation, demonstrating model utility.
  • Model comparisons revealed insights into directional invariance and unfolding versus dominance processes.

Conclusions:

  • The new IRT model family provides a robust framework for analyzing unfolding responses.
  • The approach facilitates the investigation of directional invariance in psychological measurements.
  • Developed methods for visualizing item response functioning enhance interpretability.