Network Approaches to Binary Assessment Data: Network Psychometrics Versus Latent Space Item Response Models.

Abstract

This study compares two network-based approaches for analyzing binary psychological assessment data: network psychometrics and latent space item response modeling (LSIRM). Network psychometrics, a well-established method, infers relationships among items or symptoms based on pairwise conditional dependencies. In contrast, LSIRM is a more recent framework that represents item responses as a bipartite network of respondents and items embedded in a latent metric space, where the likelihood of a response decreases with increasing distance between the respondent and item. We evaluate the performance of both methods through simulation studies under varying data-generating conditions. In addition, we demonstrate their applications to real assessment data, showcasing the distinct insights each method offers to researchers and practitioners.