Flexible Item Response Theory Models for Educational and Healthcare Data

Music City Center 

Item Response Theory (IRT) has long been a cornerstone of educational testing, enabling accurate measurement of student ability across diverse types of assessments. Recently, these models have also shown promise in healthcare, capturing latent traits like quality of life, patient satisfaction, and symptom severity. In this work, we present a flexible approach to IRT accommodating multiple item types (e.g., dichotomous, polytomous) and leveraging modern computational methods for parameter estimation. We introduce our open-source Python package IRTorch, which streamlines model building and parameter estimation while offering robust tools for handling large-scale datasets. We demonstrate how these models handle complex response structures in Swedish SAT data and patient-reported outcomes on stroke recovery from the Swedish Stroke Register. We also highlight key insights for practitioners, including guidelines for model selection, diagnostics, and handling missing or noisy data. These findings underscore the broad applicability of modern IRT methods for quantitative research across domains, leading to more nuanced and actionable insights in both education and healthcare.

Item Response Theory

Psychometrics

Healthcare

Statistical software

PyTorch 

Section on Statistical Learning and Data Science