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Fitting item response theory models using deep learning computational frameworks

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  • Luo, Nanyu
  • Ji, Feng
  • Han, Yuting
  • He, Jinbo
  • Zhang, Xiaoya

Abstract

PyTorch and TensorFlow are two widely adopted, modern deep learning frameworks that offer comprehensive computation libraries for deep learning models. We illustrate how to utilize these deep learning computational platforms and infrastructure to estimate a class of popular psychometric models, dichotomous and polytomous Item Response Theory (IRT) models, along with their multidimensional extensions. Through simulation studies, the estimation performance on the simulated datasets demonstrates low mean square error and bias for model parameters. We discuss the potential of integrating modern deep learning tools and views into psychometric research.

Suggested Citation

  • Luo, Nanyu & Ji, Feng & Han, Yuting & He, Jinbo & Zhang, Xiaoya, 2024. "Fitting item response theory models using deep learning computational frameworks," OSF Preprints tjxab, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:tjxab
    DOI: 10.31219/osf.io/tjxab
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    References listed on IDEAS

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