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An Information-theoretic Approach to Hypergraph Psychometrics by Marinazzo, Daniele; Van Roozendaal, Jan; Rosas, Fernando E.; Stella, Massimo; Comolatti, Renzo; Colenbier, Nigel; Stramaglia, Sebastiano; Rosseel, Yves is a scholarly article available to read on EtoBox.

What is An Information-theoretic Approach to Hypergraph Psychometrics about?

Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated cross-sectional, time-series, or panel data. These networks constitute an established methodology to assess the interactions and relative importance of nodes/indicators, providing an important complement to other approaches such as factor analysis. However, focusing the modelling solely on pairwise relationships can neglect potentially critical information shared by groups of three or more variables in the form of higher-order interdependencies. To overcome this important limitation, here we propose an information-theoretic framework based on hypergraphs as psychometric models. As edges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer representation of the interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs can highlight meaningful redundant and synergistic interactions on either simulated or state-of-art, re-analyzed psychometric datasets. Overall, our framework extends current netw

Author

Marinazzo, Daniele; Van Roozendaal, Jan; Rosas, Fernando E.; Stella, Massimo; Comolatti, Renzo; Colenbier, Nigel; Stramaglia, Sebastiano; Rosseel, Yves

Published

2022

Language

EN