Bayesian Estimation of Latent Space Item Response Models with <tt>JAGS</tt>, <tt>Stan</tt>, and <tt>NIMBLE</tt> in <tt>R</tt>

Abstract

The latent space item response model (LSIRM) is a newly-developed approach to analyzing and visualizing conditional dependencies in item response data, manifested as the interactions between respondents and items, between respondents, and between items. This paper provides a practical guide to the Bayesian estimation of LSIRM using three open-source software options, JAGS, Stan, and NIMBLE in R. By means of an empirical example, we illustrate LSIRM estimation, providing details on the model specification and implementation, convergence diagnostics, model fit evaluations and interaction map visualizations.

Keywords

• latentspace item response models
• Bayesian estimation
• item response data
• conditional dependencies
• interaction map
• <tt>JAGS</tt>