Berkhout Sophie W, Schuurman Noémi K, Hamaker Ellen L
Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University.
Psychol Methods. 2025 Jun;30(3):599-621. doi: 10.1037/met0000575. Epub 2023 May 25.
ynamic models are becoming increasingly popular to study the dynamic processes of dyadic interactions. In this article, we present a Dyadic Interaction Dynamics (DID) Shiny app which provides simulations and visualizations of data from several models that have been proposed for the analysis of dyadic data. We propose data generation as a tool to inspire and guide theory development and elaborate on how to connect substantive ideas to specific features of these models. We begin by discussing the basics of dynamic models with dyadic interactions. Then we present several models and illustrate model-implied behavior through generated data, accompanied by the DID Shiny app which allows researchers to generate and visualize their own data. Specifically, we consider: (a) the first-order vector autoregressive (VAR(1)) model; (b) the latent VAR(1) model; (c) the time-varying VAR(1) model; (d) the threshold VAR(1) model; (e) the hidden Markov model; and (f) the Markov-switching VAR(1) model. Finally, we demonstrate these models using empirical examples. We aim to give researchers more insight into what dynamic modeling approach fits their research question and data best. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
动态模型在研究二元互动的动态过程中越来越受欢迎。在本文中,我们展示了一个二元互动动力学(DID)Shiny应用程序,它提供了对为分析二元数据而提出的几个模型的数据进行模拟和可视化。我们提出将数据生成作为一种激发和指导理论发展的工具,并详细阐述如何将实质性想法与这些模型的特定特征联系起来。我们首先讨论具有二元互动的动态模型的基础知识。然后我们展示几个模型,并通过生成的数据说明模型隐含的行为,同时展示DID Shiny应用程序,该应用程序允许研究人员生成和可视化他们自己的数据。具体来说,我们考虑:(a)一阶向量自回归(VAR(1))模型;(b)潜在VAR(1)模型;(c)时变VAR(1)模型;(d)阈值VAR(1)模型;(e)隐马尔可夫模型;以及(f)马尔可夫切换VAR(1)模型。最后,我们使用实证例子展示这些模型。我们旨在让研究人员更深入地了解哪种动态建模方法最适合他们的研究问题和数据。(PsycInfo数据库记录(c)2025美国心理学会,保留所有权利)