Harvard University, Cambridge, MA, USA.
Universität Innsbruck, Innsbruck, Austria.
Psychometrika. 2020 Dec;85(4):926-945. doi: 10.1007/s11336-020-09731-4. Epub 2020 Nov 4.
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.
在心理学的许多领域中，基于相关的网络方法（即心理计量网络）已成为一种流行的工具。在本文中，我们提出了一种方法，该方法根据协变量递归地对样本进行分割，以检测协方差或相关矩阵结构中的显著差异。可以从所得的分割中随后估计心理计量网络或其他基于相关的模型（例如因子模型）。我们采用基于模型的递归分区和条件推断树方法，以递归方式找到协变量的分割。在几种模拟条件下研究了这些方法的经验效力。使用人格和临床研究中的实际数据给出了示例。
