Department of Psychological Methods, University of Amsterdam.
Center for Philosophy of Science, University of Pittsburgh.
Psychol Methods. 2023 Dec;28(6):1273-1285. doi: 10.1037/met0000476. Epub 2022 Apr 11.
Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
网络方法在心理计量学结构中的应用，其中结构是根据其组成因素之间的相互作用来建模的，在心理学中迅速流行起来。最近，这种网络方法在各种心理结构上的应用已经从描述性的立场转变，其目标是估计与一个结构相关的网络结构，到更具比较性的立场，其目标是比较不同人群的网络结构。然而，缺乏这样做的统计工具。在本文中，我们提出了网络比较测试（NCT），它使用基于重采样的置换测试来比较两个独立的、横截面数据集的网络结构，以测试（a）网络结构、（b）边缘（连接）强度和（c）全局强度的不变性。在模拟中评估了 NCT 的性能，这些模拟表明，对于所有三个测试，NCT 在各种情况下的表现都很好：I 型错误率接近名义显著水平，如果样本量和网络之间的差异很大，则证明功率足够高。我们通过比较男性和女性的抑郁症状网络来举例说明 NCT。讨论了 NCT 的可能扩展。（PsycInfo 数据库记录（c）2024 APA，保留所有权利）。
