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

Daniel McNeish, Ellen L. Hamaker

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

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.

Original languageEnglish (US)
JournalPsychological Methods
DOIs
StateAccepted/In press - 2019

Keywords

  • Dynamic structural equation modeling
  • Intensive longitudinal data
  • Multilevel modeling
  • Time-series analysis

ASJC Scopus subject areas

  • Psychology (miscellaneous)

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