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.
翻译:心理网络方法提议将症状或问卷项目视为相互关联的节点,在它们之间建立联系,反映对称的统计依赖性,评价了跨部门、时间序列或小组数据。这些网络是评估节点/指标相互作用和相对重要性的既定方法,为诸如要素分析等其他方法提供了重要的补充。然而,仅仅将模拟作为对称关系,可能会忽视由三个或三个以上变数组成的群体以较高层次相互依存形式共享的潜在关键信息。为了克服这一重要的局限性,我们在此提议一个信息理论框架,其依据是作为心理计量模型的超镜。由于超镜的边缘能够将几个节点结合在一起,因此这一扩展可以更充分地反映各组心理变量之间可能存在的相互作用。我们的结果表明,心理计量超镜可以突出模拟或最新、再分析的心理计量数据集中有意义的冗余和协同互动。总体而言,我们的框架扩展了当前的网络方法,同时导致新的方法,评估其核心数据不同于其他方法,扩展了心理计量工具箱,打开了未来调查的前景。