标题： 一种信息论的超图心理测量方法 显示英文标题

标题： An information-theoretic approach to hypergraph psychometrics

摘要： 心理网络方法提出将症状或问卷项目视为相互连接的节点，节点之间的联系反映了通过横断面、时间序列或面板数据评估的成对统计依赖性。 这些网络构成了一种既定的方法来评估节点/指标之间的交互和相对重要性，为诸如因素分析等其他方法提供了重要的补充。 然而，仅仅专注于成对关系的建模可能会忽略由三个或更多变量以高阶相互依赖形式共享的潜在关键信息。 为了解决这一重要局限性，我们在此提出了一种基于超图的信息论框架作为心理计量模型。 由于超图中的边能够同时包含多个节点，这种扩展可以更丰富地表示心理变量集合之间可能存在的交互。 我们的结果显示，心理计量超图可以在模拟数据或最新的重新分析的心理计量数据集上突出有意义的冗余和协同交互。 总体而言，我们的框架扩展了当前的网络方法，同时导致了与核心不同的数据评估新方式，扩展了心理计量工具箱，并为未来的探索开辟了有希望的途径。 显示英文摘要

摘要： 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.