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Summary
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This study introduces a multidimensional item response tree model to address response styles in surveys. The model effectively explains score heterogeneity, improving the validity of survey inferences.

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explanatory item response theoryitem response treemultidimensionalityresponse style

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Area of Science:

  • Psychometrics
  • Survey Methodology
  • Statistical Modeling

Background:

  • Response styles like extreme or midpoint responding can bias survey results and threaten the validity of score interpretations.
  • Existing models may not adequately account for individual differences in response styles.

Purpose of the Study:

  • To incorporate person-level covariates into a multidimensional item response tree model to explain heterogeneity in response styles.
  • To assess the model's parameter recovery and performance using simulation studies and an empirical example.

Main Methods:

  • Developed and applied a multidimensional item response tree model incorporating person-level covariates.
  • Utilized Markov chain Monte Carlo (MCMC) estimation, specifically the Gibbs sampler, for parameter estimation.
  • Conducted two simulation studies and analyzed an empirical dataset (National Longitudinal Study of Adolescent to Adult Health).

Main Results:

  • The model demonstrated small mean bias and root mean square error for item intercepts and discrimination parameters, especially with larger sample sizes (>1,000).
  • Covariates significantly predicted extreme and midpoint response styles; for instance, non-White race, male gender, and high parental support were linked to extreme responding.
  • Item and regression parameters were estimated with sufficient accuracy, indicating the model's practical utility.

Conclusions:

  • The multidimensional item response tree model with covariates effectively explains individual differences in response styles, enhancing survey data validity.
  • The model provides accurate parameter estimates, particularly with sample sizes over 1,000 and MCMC estimation.
  • Identifying predictors of response styles offers valuable insights for survey design and interpretation.