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Jean-Paul Fox1, Konrad Klotzke1, Ahmet Salih Simsek2

  • 1Faculty of Behavioral, Management, and Social Sciences, University of Twente, Enschede, Netherlands.

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|June 22, 2023
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Summary

This study introduces the LNIRT R-package for joint modeling of response accuracy and response times in computer-based testing. It enhances latent variable measurement by integrating speed of working data for improved test analysis.

Keywords:
IRT modelsJoint modelsMCMCModel-fit toolsR-codeR-package LNIRTRT modelsVariable working-speed

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Software

Background:

  • Computer-based testing commonly collects response accuracy (RA) and response times (RTs).
  • Item Response Theory (IRT) models traditionally use RA for latent variable measurement (e.g., ability).
  • RT data offers insights into working speed and can enhance test analysis.

Purpose of the Study:

  • To introduce the LNIRT R-package for fitting joint models that integrate RA and RT data.
  • To demonstrate how joint models improve latent variable estimation and provide insights into response speed.
  • To offer a user-friendly tool for advanced psychometric analysis in educational and psychological testing.

Main Methods:

  • Development and application of the LNIRT R-package in R.
  • Utilizing joint modeling approaches to simultaneously analyze response accuracy and response times.
  • Employing Gibbs sampling for model fitting, with optional MCMC analysis tools (coda, mcmcse).

Main Results:

  • The LNIRT package provides a streamlined interface for fitting complex joint IRT models.
  • Demonstrated the utility of integrating RT data for a more comprehensive understanding of test-taker characteristics.
  • Successful application of the package to two real-world datasets, validating its functionality.

Conclusions:

  • The LNIRT package facilitates the integration of response time data into psychometric analysis.
  • Joint modeling offers a richer assessment of latent variables by incorporating speed-of-working information.
  • This approach enhances the utility of computer-based testing for both measurement and operational insights.