Park Jonathan J, Chow Sy-Miin, Fisher Zachary F, Molenaar Peter C M
The Pennsylvania State University.
University of North Carolina at Chapel Hill.
Eur J Psychol Assess. 2020;36(6):1009-1023. doi: 10.1027/1015-5759/a000612.
The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches-GIMME, uSEM, and LASSO gVAR-in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three approaches dynamic networks provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches' respective strengths and limitations.
近年来，动态网络模型的应用不断增加。这些模型使研究人员能够捕捉纵向数据中的滞后效应和同期效应，通常是作为标准向量自回归（VAR）模型的变体、重新表述或扩展。到目前为止，许多这些动态网络尚未相互进行明确比较。我们从建模假设、估计程序、基于蒙特卡罗模拟的统计特性以及对情感和人格研究人员的影响等方面，比较了三种流行的动态网络方法——GIMME、uSEM和LASSO gVAR。我们发现所有这三种动态网络方法都得出了部分支持情感和人格理论的群体层面实证结果。然而，个体层面的结果显示不同方法和参与者之间存在很大的异质性。在讨论这些差异原因的同时，也阐述了这些方法各自的优势和局限性。
