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Killing Two Birds with One Stone: Accounting for Unfolding Item Response Process and Response Styles Using Unfolding

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The new Unfolding Item Response Tree (UIRTree) model accurately distinguishes between unfolding responses and response styles in Likert-type items. This model improves parameter estimation and fits personality data better than existing methods.

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Personality assessment

Background:

  • Two parallel research streams, unfolding models and individual response styles (RSs), exist for Likert-type items.
  • Accurate understanding of Likert-type item responses requires distinguishing between unfolding responses and RSs.

Purpose of the Study:

  • Propose and evaluate the Unfolding Item Response Tree (UIRTree) model.
  • Compare UIRTree's performance against existing models like Samejima's Graded Response Model, Generalized Graded Unfolding Model, and Dominance Item Response Tree model.
  • Assess UIRTree's ability to accurately recover model parameters and fit empirical data.

Main Methods:

  • Monte Carlo simulation study comparing UIRTree with three other models.
  • Analysis of empirical datasets, specifically personality data.
  • Model parameter estimation using R code.

Main Results:

  • The UIRTree model was accurately selected by AIC when data included unfolding processes and RSs.
  • Model parameters in UIRTree were accurately recovered under realistic conditions.
  • UIRTree demonstrated superior fit for personality datasets and provided more reasonable parameter estimates compared to competing models, revealing a strong presence of RSs.

Conclusions:

  • The UIRTree model effectively parses unfolding responses from response styles in Likert-type items.
  • Mis-specifying the item response process or ignoring RSs negatively impacts parameter estimation.
  • UIRTree offers a valuable tool for modeling Likert-type item responses and understanding individual differences in response behavior.