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

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 data sets 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 Version 8. 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. Translational Abstract The way that researchers collect data has been transformed by technological advances. New types of data are being collected which are accompanied by new statistical methods necessary to analyze such data. Specifically, intensive longitudinal data have become more widespread in psychology where each person in the data provides a large amount of data over a relatively short interval. The statistical literature for modeling this type of data has moved more quickly than the uptake by researcher who collect and analyze their data. Therefore, the goal of this article is to walkthrough the basic idea of these models for intensive longitudinal data and how they can be fit in the popular Mplus software program, which was recently added specific routines for facilitating these types of models.