# HBMIRT: A SAS macro for estimating uni- and multidimensional 1- and 2-parameter item response models in small (and large!) samples

## TL;DR

The paper introduces HBMIRT, a SAS macro for estimating item response models in small or large samples, making complex psychometric analyses more accessible.

## Contribution

The novel contribution is a user-friendly SAS macro with hierarchical priors that improves IRT estimation in small samples.

## Key findings

- HBMIRT enables estimation of uni- and multidimensional IRT models with dichotomous items.
- Hierarchical priors outperform weakly informative priors and maximum likelihood in small samples.
- The macro is accessible via SAS OnDemand for Academics, supporting academic researchers.

## Abstract

Item response theory (IRT) has evolved as a standard psychometric approach in recent years, in particular for test construction based on dichotomous (i.e., true/false) items. Unfortunately, large samples are typically needed for item refinement in unidimensional models and even more so in the multidimensional case. However, Bayesian IRT approaches with hierarchical priors have recently been shown to be promising for estimating even complex models in small samples. Still, it may be challenging for applied researchers to set up such IRT models in general purpose or specialized statistical computer programs. Therefore, we developed a user-friendly tool – a SAS macro called HBMIRT – that allows to estimate uni- and multidimensional IRT models with dichotomous items. We explain the capabilities and features of the macro and demonstrate the particular advantages of the implemented hierarchical priors in rather small samples over weakly informative priors and traditional maximum likelihood estimation with the help of a simulation study. The macro can also be used with the online version of SAS OnDemand for Academics that is freely accessible for academic researchers.