A Primer on Two-Level Dynamic Structural Equation Models for Intensive Longitudinal Data

Technological advances have led to an increase in intensive longitudinal data and the statistical literature on modeling such data is rapidly expanding, as are software capabilities. Common methods in this area are related to time-series analysis, a framework that historically has received little exposure in psychology. There is a scarcity of psychology-based resources introducing the basic ideas of time-series analysis, especially for datasets featuring multiple people. We begin with basics of N=1 time-series analysis and build up to complex dynamic structural equation models available in the newest release of Mplus. The goal is to provide readers with a basic conceptual understanding of common models, template code, and result interpretation. We provide short descriptions of some advanced issues, but our main priority is to supply readers with a solid knowledge base so that the more advanced literature on the topic is more readily digestible to a larger group of researchers

科技进步导致了密集纵向数据的增加，以及针对此类数据建模的统计文献的迅速扩展，同时软件功能也在不断提升。本领域常见的处理方法与时间序列分析框架相关，而该框架在心理学领域历史上曝光度较低。心理学基础资源中关于时间序列分析基本概念的引入相对匮乏，尤其是针对涉及多个人数据的集。本书从N=1时间序列分析的基本原理出发，逐步深入至Mplus最新版本中提供的复杂动态结构方程模型。旨在为读者提供对常见模型、模板代码和结果解释的基本概念理解。我们提供了某些高级问题的简要描述，但我们的主要目标是向读者提供坚实的知识基础，以便使更多高级文献对该领域的研究者更加易于消化吸收。