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A Bayesian Random Block Item Response Theory Model for Forced-Choice Formats.

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The Bayesian random block item response theory (BRB IRT) model accurately estimates parameters in forced-choice tests. This model, incorporating negative item keys, performs comparably to existing methods and offers flexibility for complex analyses.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Traditional item response theory (IRT) models struggle with local dependence in forced-choice formats.
  • Testlet models provide a framework for addressing item dependence within blocks.

Purpose of the Study:

  • To propose the Bayesian random block IRT (BRB IRT) model for analyzing forced-choice item blocks.
  • To evaluate the performance of the BRB IRT model in parameter estimation and trait screening.
  • To explore the model's adaptability for more complex measurement scenarios.

Main Methods:

  • Developed the BRB IRT model incorporating a random block effect to handle local dependence.
  • Utilized Markov Chain Monte Carlo (MCMC) for simultaneous estimation of item and trait parameters.
  • Conducted simulation studies and an empirical application comparing BRB IRT with the Thurstonian IRT model.

Main Results:

  • The BRB IRT model demonstrated good performance in estimating item and trait parameters.
  • The model effectively screened individuals with low scores on target traits.
  • Model performance was contingent on item block composition, requiring negatively keyed items.
  • BRB IRT showed equivalent performance to the Thurstonian IRT model in empirical application.

Conclusions:

  • The BRB IRT model is a viable and effective tool for analyzing forced-choice data.
  • The inclusion of negatively keyed items is crucial for optimal model performance.
  • The BRB IRT model offers a flexible foundation for advanced psychometric modeling, such as incorporating covariates like gender.