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An Attention-Based Diffusion Model for Psychometric Analyses

Author

Listed:
  • Udo Boehm

    (University of Amsterdam)

  • Maarten Marsman

    (University of Amsterdam)

  • Han L. J. Maas

    (University of Amsterdam)

  • Gunter Maris

    (ACT)

Abstract

The emergence of computer-based assessments has made response times, in addition to response accuracies, available as a source of information about test takers’ latent abilities. The development of substantively meaningful accounts of the cognitive process underlying item responses is critical to establishing the validity of psychometric tests. However, existing substantive theories such as the diffusion model have been slow to gain traction due to their unwieldy functional form and regular violations of model assumptions in psychometric contexts. In the present work, we develop an attention-based diffusion model based on process assumptions that are appropriate for psychometric applications. This model is straightforward to analyse using Gibbs sampling and can be readily extended. We demonstrate our model’s good computational and statistical properties in a comparison with two well-established psychometric models.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:psycho:v:86:y:2021:i:4:d:10.1007_s11336-021-09783-0
    DOI: 10.1007/s11336-021-09783-0
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    References listed on IDEAS

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    1. Francis Tuerlinckx & Paul Boeck, 2005. "Two interpretations of the discrimination parameter," Psychometrika, Springer;The Psychometric Society, vol. 70(4), pages 629-650, December.
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    3. 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).
    4. Jean-Paul Fox & Cees Glas, 2001. "Bayesian estimation of a multilevel IRT model using gibbs sampling," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 271-288, June.
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    6. M. Marsman & H. Sigurdardóttir & M. Bolsinova & G. Maris, 2019. "Characterizing the Manifest Probability Distributions of Three Latent Trait Models for Accuracy and Response Time," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 870-891, September.
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