Department of Psychology, Lancaster University, Lancaster, UK.
Department of Psychology, University of Edinburgh, Edinburgh, UK.
Multivariate Behav Res. 2024 May-Jun;59(3):620-637. doi: 10.1080/00273171.2023.2288575. Epub 2024 Feb 14.
Recent technological advances have provided new opportunities for the collection of intensive longitudinal data. Using methods such as dynamic structural equation modeling, these data can provide new insights into moment-to-moment dynamics of psychological and behavioral processes. In intensive longitudinal data ( > 20), researchers often have theories that imply that factors that change from moment to moment within individuals act as moderators. For instance, a person's level of sleep deprivation may affect how much an external stressor affects mood. Here, we describe how researchers can implement, test, and interpret dynamically changing within-person moderation effects using two-level dynamic structural equation modeling as implemented in the structural equation modeling software Mplus. We illustrate the analysis of within-person moderation effects using an empirical example investigating whether changes in spending time online using social media affect the moment-to-moment effect of loneliness on depressive symptoms, and highlight avenues for future methodological development. We provide annotated Mplus code, enabling researchers to better isolate, estimate, and interpret the complexities of within-person interaction effects.
最近的技术进步为密集纵向数据的收集提供了新的机会。使用动态结构方程建模等方法，这些数据可以为心理和行为过程的即时动态提供新的见解。在密集的纵向数据（>20）中，研究人员通常有这样的理论，即暗示个体内部随时间变化的因素可以作为调节因素。例如，一个人的睡眠剥夺程度可能会影响外部压力源对情绪的影响程度。在这里，我们描述了研究人员如何使用结构方程建模软件 Mplus 中实现的两级动态结构方程建模来实施、测试和解释动态变化的个体内调节效应。我们使用一个实证示例来说明个体内调节效应的分析，该示例研究了使用社交媒体在线花费时间的变化是否会影响孤独感对抑郁症状的即时影响，并强调了未来方法发展的途径。我们提供了带注释的 Mplus 代码，使研究人员能够更好地隔离、估计和解释个体内交互效应的复杂性。
