Fitting item response theory models using deep learning computational frameworks

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

No tjxab, OSF Preprints from Center for Open Science

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.

Date: 2024-10-28
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:tjxab

DOI: 10.31219/osf.io/tjxab

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