A Scalable Parametric Item Calibration Engine (SPICE) for Explanatory IRT with Sparse Data

TL;DR

This paper introduces SPICE, a scalable Bayesian multidimensional explanatory IRT model with MCMC estimation, tailored for large, sparse psychometric datasets such as adaptive assessments.

Contribution

It presents a novel scalable method for explanatory IRT that efficiently handles sparse data and supports various psychometric analyses.

Findings

Developed a Bayesian multidimensional explanatory IRT model.

Created calibration software suitable for large, sparse datasets.

Demonstrated the model's scalability and applicability to adaptive assessments.

Abstract

We describe a Bayesian multidimensional explanatory IRT model, and an associated Markov Chain Monte Carlo (MCMC) estimation procedure and the corresponding development of calibration software, designed for psychometric analyses of large numbers of sparsely-linked persons and items. Such data structures can arise, for example, from adaptive assessments using large banks of automatically generated items with individual test takers receiving a very small proportion of the entire bank. We discuss how our choices for model specification, data structures, and algorithm implementation combine to create a scalable method for explanatory IRT that can support a variety of psychometric operations with sparse data.