Bayesian Estimation of Latent Space Item Response Models with JAGS, Stan, and NIMBLE in R
Jinwen Luo, Ludovica De Carolis, Biao Zeng, Minjeong Jeon
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.
Topics & Concepts
Bayesian probabilityComputer scienceEstimationSoftwareSpace (punctuation)Convergence (economics)Machine learningBayes estimatorData miningArtificial intelligenceProgramming languageEngineeringEconomicsOperating systemSystems engineeringEconomic growthStatistical Methods and Bayesian InferenceMental Health Research TopicsPsychometric Methodologies and Testing