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Modeling Changes in Response Style with Longitudinal IRTree Models.

Allison J Ames1, Brian C Leventhal2

  • 1Educational Statistics and Research Methods, University of Arkansas.

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Summary
This summary is machine-generated.

This study introduces the longitudinal IRTree model for analyzing complex cognitive responses over time. It reveals how changes in response styles can offset latent trait changes, impacting observed scores.

Keywords:
Bayesian estimationResponse styleitem response treelongitudinal model

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Area of Science:

  • Psychometrics
  • Cognitive Psychology
  • Longitudinal Data Analysis

Background:

  • Traditional psychometric models oversimplify cognitive response processes.
  • Analysis of ordinal responses is often limited to single traits and time points.

Purpose of the Study:

  • Propose the longitudinal IRTree model for complex response processes in longitudinal studies.
  • Investigate proportionality between response style changes and latent trait changes.

Main Methods:

  • Developed a novel longitudinal IRTree model.
  • Conducted a simulation study using a Bayesian framework.
  • Applied the model to an empirical dataset.

Main Results:

  • Adequate item parameter recovery observed, especially with larger sample sizes (N=2000).
  • Longitudinal change parameters recovered well, improved with informative priors.
  • Empirical data showed stable observed scores resulted from decreasing response styles offsetting increasing latent traits.

Conclusions:

  • The longitudinal IRTree model offers a significant expansion for analyzing complex cognitive responses over time.
  • Understanding response style dynamics is crucial for accurate interpretation of latent trait changes.
  • The model provides insights into how response behaviors can mask or amplify true trait progression.