An information-theoretic approach to hypergraph psychometrics

Abstract

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 network approaches while leading to new ways of assessing the data that differ at their core from other methods, extending the psychometric toolbox and opening promising avenues for future investigation.

References in corpus (26)

• A Tutorial on Regularized Partial Correlation Networks
• High-dimensional graphs and variable selection with the Lasso
• Networks beyond pairwise interactions: structure and dynamics
• Granger causality and transfer entropy are equivalent for Gaussian variables
• Exploratory graph analysis: a new approach for estimating the number of dimensions in psychological research
• The physics of higher-order interactions in complex systems
• Simplicial models of social contagion
• Extended Bayesian Information Criteria for Gaussian Graphical Models
• What are higher-order networks?
• Higher-order interactions in complex networks of phase oscillators promote abrupt synchronization switching
• Quantifying unique information
• Principles of scientific research team formation and evolution
• Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes
• Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems
• Quantifying High-order Interdependencies via Multivariate Extensions of the Mutual Information
• Estimating Psychological Networks and their Accuracy: A Tutorial Paper
• Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data
• Disentangling high-order mechanisms and high-order behaviours in complex systems
• Unveiling the higher-order organization of multivariate time series
• Multivariate Dependence Beyond Shannon Information
• An information-theoretic approach to self-organisation: Emergence of complex interdependencies in coupled dynamical systems
• Understanding interdependency through complex information sharing
• An operational information decomposition via synergistic disclosure
• Quantifying synergistic information using intermediate stochastic variables
• Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI
• Quantifying high-order interdependencies on individual patterns via the local O-information: theory and applications to music analysis