@article{01J4NZCZWGANAYTF7J46QE7NKR,
  abstract     = {{Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with linksbetween them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. Thesenetworks constitute an established methodology to visualise and conceptualise the interactions and relative importance ofnodes/indicators, providing an important complement to other approaches such as factor analysis. However, limiting therepresentation to pairwise relationships can neglect potentially critical information shared by groups of three or more variables(higher-order statistical interdependencies). To overcome this important limitation, here we propose an information-theoreticframework to assess these interdependencies and consequently to use hypergraphs as representations in psychometrics. Asedges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer account onthe interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs canhighlight meaningful redundant and synergistic interactions on either simulated or state-of-the-art, re-analysed psychometricdatasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data thatdiffer at their core from other methods, enriching the psychometrics toolbox, and opening promising avenues for futureinvestigation.}},
  author       = {{Marinazzo, Daniele and van Roozendaal, Jan and Rosas, Fernando E. and Stella, Massimo and Comolatti, Renzo and Colenbier, Nigel and Stramaglia, Sebastiano and Rosseel, Yves}},
  issn         = {{1554-351X}},
  journal      = {{BEHAVIOR RESEARCH METHODS}},
  keywords     = {{psychometrics,networks,hypergraphs,psychological networks,information theory,HIGHER-ORDER INTERACTIONS,EMOTION REGULATION,NETWORK ANALYSIS,EMPATHY,CENTRALITY}},
  language     = {{eng}},
  title        = {{An information-theoretic approach to build hypergraphs in psychometrics}},
  url          = {{http://doi.org/10.3758/s13428-024-02471-8}},
  year         = {{2024}},
}

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