The comparison of idiographic network structures to determine the presence of heterogeneity is a challenging endeavor in many applied settings. Previously, researchers eyeballed idiographic networks, computed correlations, and used techniques that make use of the multilevel structure of the data (e.g., group iterative multiple model estimation and multilevel vector autoregressive) to investigate individual differences. However, these methods do not allow for testing the (in)equality of idiographic network structures directly. In this article, we propose the Individual Network Invariance Test (INIT), which we implemented in the R package INIT. INIT extends common model comparison practices in structural equation modeling to idiographic network structures to test for (in)equality between idiographic networks. In a simulation study, we evaluated the performance of INIT on both saturated and pruned idiographic network structures by inspecting the rejection rate of the χ² difference test and model selection criteria, such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Results show INIT performs adequately when t = 100 per individual. When applying INIT on saturated networks, the AIC performed best as a model selection criterion, while the BIC showed better results when applying INIT on pruned networks. In an empirical example, we highlight the possibilities of this new technique, illustrating how INIT provides researchers with a means of testing for (in)equality between idiographic network structures and within idiographic network structures over time. To conclude, recommendations for empirical researchers are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

---

测试纵向网络中的相似性：个体网络不变性测试。


在许多应用环境中，通过比较具体的网络结构来确定异质性的存在是一项具有挑战性的工作。此前，研究人员关注具体网络、计算相关性，并使用利用数据多级结构的技术（例如，组迭代多模型估计和多级向量自回归）来研究个体差异。然而，这些方法不允许直接测试具体网络结构的（不）相等性。在本文中，我们提出了个体网络不变性测试（INIT），我们在 R 包 INIT 中实现了该测试。 INIT 将结构方程建模中的常见模型比较实践扩展到具体网络结构，以测试具体网络之间的（不）相等性。在模拟研究中，我们通过检查 χ2 差异检验的拒绝率和模型选择标准（例如赤池信息准则（AIC）和贝叶斯信息准则（BIC））来评估 INIT 在饱和和修剪的具体网络结构上的性能。结果显示，当每个人 t = 100 时，INIT 表现良好。当在饱和网络上应用 INIT 时，AIC 作为模型选择标准表现最佳，而 BIC 在修剪网络上应用 INIT 时表现出更好的结果。在一个实证示例中，我们强调了这种新技术的可能性，说明 INIT 如何为研究人员提供一种测试特定网络结构之间以及特定网络结构内部随时间变化的（不）相等性的方法。最后，为实证研究人员提供了建议。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。