January 19, 2018
Time Series Analysis: Dynamic Structural Equation Modeling (DSEM)
Time series analysis is used to analyze intensive longitudinal data such as those obtained with ecological momentary assessments, experience sampling methods, daily diary methods, and ambulatory assessments. Such data typically have a large number of time points, for example, twenty to two hundred. The measurements are typically closely spaced in time.
Multilevel time series analysis of intensive longitudinal data typically considers time points nested within individuals. Individual differences in level-1 parameters such as the mean, variance, and autocorrelation are represented as random effects that are modeled on level 2 in a two-level analysis.
Mplus Version 8, released April 20, 2017, offers two-level, cross-classified, as well as single-level (N=1) time series analysis. In cross-classified analysis the random effects are allowed to vary not only across individuals but also across time to represent time-varying effects.
Mplus can estimate a variety of N=1, two-level and cross-classified time series models. These include univariate autoregressive, regression, cross-lagged, confirmatory factor analysis, Item Response Theory, and structural equation models for continuous, binary, ordered categorical (ordinal), or combinations of these variable types. Bayesian analysis is used in the estimation using a flexible latent variable modeling framework referred to as dynamic structural equation modeling (DSEM).
The following papers discuss multilevel time series analysis modeling and estimation:
The following papers discuss multilevel time series analysis applications:
DSEM in Mplus Version 8 was presented to the Prevention Science Methodology Group (PSMG) in March and April 2017. Following are links to videos and handouts from these occasions:
DSEM Examples in the Mplus Version 8 User’s Guide