In many areas of psychology, correlation-based network approaches (i.e., psychometric networks) have become a popular tool. In this paper, we propose an approach that recursively splits the sample based on covariates in order to detect significant differences in the structure of the covariance or correlation matrix. Psychometric networks or other correlation-based models (e.g., factor models) can be subsequently estimated from the resultant splits. We adapt model-based recursive partitioning and conditional inference tree approaches for finding covariate splits in a recursive manner. The empirical power of these approaches is studied in several simulation conditions. Examples are given using real-life data from personality and clinical research.

中文翻译：

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网络树：一种递归划分协方差结构的方法

在心理学的许多领域，基于相关性的网络方法（即心理测量网络）已成为一种流行的工具。在本文中，我们提出了一种基于协变量递归拆分样本的方法，以检测协方差或相关矩阵结构的显着差异。心理测量网络或其他基于相关性的模型（例如，因子模型）可以随后从产生的分裂中估计出来。我们采用基于模型的递归分区和条件推理树方法以递归方式查找协变量拆分。在几个模拟条件下研究了这些方法的经验能力。使用来自个性和临床研究的真实数据给出了示例。