lsirm12pl: An R package for latent space item response modeling

TL;DR

The lsirm12pl R package implements Bayesian latent space item response modeling, enabling analysis of unobserved respondent-item interactions, with extensions for various data types and improved interpretability.

Contribution

This paper introduces the lsirm12pl package, providing tools for Bayesian estimation, extensions for different response types, and methods for better model interpretation in latent space item response modeling.

Findings

Effective modeling of respondent-item interactions.

Extensions for various response data types.

Tools for visualization and interpretation.

Abstract

The item response model in latent space (LSIRM; Jeon et al., 2021) uncovers unobserved interactions between respondents and items in the item response data by embedding both in a shared latent metric space. The R package lsirm12pl implements Bayesian estimation of the LSIRM and its extensions for various response types, base model specifications, and missing data handling. Furthermore, lsirm12pl package provides methods to improve model utilization and interpretation, such as clustering item positions on an estimated interaction map. The package also offers convenient summary and plotting options to evaluate and process the estimated results. In this paper, we provide an overview of the LSIRM's methodological foundation and describe several extensions included in the package. We then demonstrate the use of the package with real data examples contained within it.