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Fitting Diffusion Item Response Theory Models for Responses and Response Times Using the R Package diffIRT

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  • Molenaar, Dylan
  • Tuerlinckx, Francis
  • van der Maas, Han L. J.

Abstract

In the psychometric literature, item response theory models have been proposed that explicitly take the decision process underlying the responses of subjects to psychometric test items into account. Application of these models is however hampered by the absence of general and flexible software to fit these models. In this paper, we present diffIRT, an R package that can be used to fit item response theory models that are based on a diffusion process. We discuss parameter estimation and model fit assessment, show the viability of the package in a simulation study, and illustrate the use of the package with two datasets pertaining to extraversion and mental rotation. In addition, we illustrate how the package can be used to fit the traditional diffusion model (as it has been originally developed in experimental psychology) to data.

Suggested Citation

  • Molenaar, Dylan & Tuerlinckx, Francis & van der Maas, Han L. J., 2015. "Fitting Diffusion Item Response Theory Models for Responses and Response Times Using the R Package diffIRT," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i04).
    • Handle: RePEc:jss:jstsof:v:066:i04
    DOI: http://hdl.handle.net/10.18637/jss.v066.i04
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    References listed on IDEAS

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    Cited by:

    1. Inhan Kang & Minjeong Jeon & Ivailo Partchev, 2023. "A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 830-864, September.
    2. Udo Boehm & Maarten Marsman & Han L. J. Maas & Gunter Maris, 2021. "An Attention-Based Diffusion Model for Psychometric Analyses," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 938-972, December.
    3. Inhan Kang & Paul Boeck & Roger Ratcliff, 2022. "Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 725-748, June.
    4. Kang, Inhan & De Boeck, Paul & Partchev, Ivailo, 2022. "A randomness perspective on intelligence processes," Intelligence, Elsevier, vol. 91(C).
    5. Inhan Kang & Dylan Molenaar & Roger Ratcliff, 2023. "A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 940-974, September.
    6. Bunji, Kyosuke & Okada, Kensuke, 2019. "Item Response and Response Time Model for Personality Assessment via Linear Ballistic Accumulation," OSF Preprints knuy7, Center for Open Science.
    7. Jochen Ranger & Jörg-Tobias Kuhn, 2018. "Estimating Diffusion-Based Item Response Theory Models: Exploring the Robustness of Three Old and Two New Estimators," Journal of Educational and Behavioral Statistics, , vol. 43(6), pages 635-662, December.

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