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A mixed-binomial model for Likert-type personality measures.

Jüri Allik1

  • 1Department of Psychology, University of Tartu Tartu, Estonia ; Estonian Academy of Sciences Tallinn, Estonia.

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Summary

This study introduces a binomial Item Response Theory (IRT) model as an alternative for personality measurement. This new model offers a workable approach for analyzing personality traits using response probabilities.

Keywords:
Likert-scaleNEO Personality Inventorymeasurement invariancemixed-binomial modelpersonality measurement modelsresponse biasself- and observer-ratings

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Area of Science:

  • Psychometrics
  • Personality Psychology
  • Statistical Modeling

Background:

  • Personality measurement commonly uses Item Response Theory (IRT) with normal or logistic functions for latent variables.
  • These models assume continuous latent variables influencing Likert-scale item responses.

Purpose of the Study:

  • To propose and evaluate a binomial Item Response Theory (IRT) model as an alternative for personality trait measurement.
  • To explore the fit of a binomial model compared to traditional IRT models.

Main Methods:

  • Analysis of 1731 self- and other-rated responses on the 240 NEO PI-3 questionnaire items.
  • Application and evaluation of a binomial IRT model, including mixed-binomial distributions.
  • Investigated improvements by incorporating random noise and response biases.

Main Results:

  • A binomial IRT model, using a single probability parameter, demonstrated a viable alternative for personality measurement.
  • Mixed-binomial distributions provided the best fit for most items, suggesting subpopulations with different endorsement probabilities.
  • Model fit improved with the inclusion of random noise and response biases.

Conclusions:

  • The binomial response model presents a practical alternative to conventional normal and logistic IRT models for personality trait measurement.
  • This approach offers a new perspective on understanding the latent structure of personality responses.