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Psychological network approaches propose to see symptoms or questionnaireitems as interconnected nodes, with links between them reflecting pairwisestatistical dependencies evaluated cross-sectional, time-series, or panel data.These networks constitute an established methodology to assess the interactionsand relative importance of nodes/indicators, providing an important complementto other approaches such as factor analysis. However, focusing the modellingsolely on pairwise relationships can neglect potentially critical informationshared by groups of three or more variables in the form of higher-orderinterdependencies. To overcome this important limitation, here we propose aninformation-theoretic framework based on hypergraphs as psychometric models. Asedges in hypergraphs are capable of encompassing several nodes together, thisextension can thus provide a richer representation of the interactions that mayexist among sets of psychological variables. Our results show how psychometrichypergraphs can highlight meaningful redundant and synergistic interactions oneither simulated or state-of-art, re-analyzed psychometric datasets. Overall,our framework extends current network approaches while leading to new ways ofassessing the data that differ at their core from other methods, extending thepsychometric toolbox and opening promising avenues for future investigation.

An information-theoretic approach to hypergraph psychometrics