  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.
