🤖 AI Summary
This study addresses the long-standing divide between latent variable models and network models in psychometrics, which has hindered theoretical integration and methodological innovation. Through an exploratory literature review, cross-disciplinary comparison of statistical models, and visualization techniques, it systematically examines the intrinsic connections among Item Response Theory (IRT), Structural Equation Modeling (SEM), Generalized Linear Models (GLM), and network analysis. The work proposes a unified modeling paradigm that elucidates both the commonalities and complementarities across these approaches, establishing an integrative framework bridging latent variable and network perspectives. This framework not only offers a novel lens for addressing longstanding debates about the nature of psychological constructs but also facilitates the development of reproducible, modular psychometric tools, thereby advancing interdisciplinary collaboration and methodological synthesis.
📝 Abstract
Since the introduction of network psychometrics, several connections to statistical models in "classical" psychometrics (i.e., IRT, SEM, GLM) as well as to approaches from other research fields have been established. In this paper, these developments have been reviewed and synthesized and, based on an exploratory literature search, further advanced and presented in an accessible visual format. This perspective opens up promising opportunities to extend the psychometric-toolbox by incorporating and learning from statistical methodologies developed in other research domains, which often address similar or even identical problems. Highlighting these methodological commonalities may also foster collaboration across research fields that have traditionally remained largely independent. Moreover, awareness of these connections may render methodological development more systematic and goal-directed and may enable a meaningful division of labor, for example between the development of statistical methodology and its practical implementation for empirical research through software tools. Finally, these methodological advances provide new opportunities for empirical research and may contribute to a reconciliation with longstanding conceptual issues concerning psychometric constructs and, more broadly, psychological phenomena.