Methods Core Seminars

Upcoming seminars


Title:    ASA-sponsored webinar: Intensive Longitudinal Data Analysis Using Mplus

Presenters:  Bengt Muthen, Tihomir Asparouhov, and Ellen Hamaker, 

University of California, Los Angeles

 Date:             Thursday, April 20, 2017

Time:              9-11

Location:       AmFAR Conference room MH-3700

550 16th Street (at 4th Street), 3rd Floor

Mission Bay, SF 94158

This talk discusses new methods for analyzing intensive longitudinal data, such as obtained with ecological momentary assessments, experience method sampling, ambulatory assessments, and daily diaries. Typically, such data have a large number of time points, T = 20-150. Single-level (N=1) as well as multilevel (N > 1) time series models with random effects varying across subjects are handled using a dynamic structural equation model (DSEM) and Bayesian estimation implemented in the Mplus Version 8 software. DSEM for N=1 time series analysis can be used to model the dynamics within a particular individual over time. Additionally, N > 1 multilevel DSEM includes extensions of time series models, such that at level 1 a time series model is used to model the within-person dynamics of a process over time, while at level 2 individual differences in the parameters that capture these dynamics are modeled. DSEM can handle multivariate outcomes as well as latent variables, and random effects can be both predicted from but also predictors of level 2 variables. DSEM is available with auto-regressive and moving-average components both for observed-variable models such as regression and cross-lagged analysis and for latent variable models such as factor analysis, IRT, structural equation modeling, and mixture modeling. DSEM also handles time-varying effect modeling (TVEM) where parameters change not only across individuals but also across time, making it suitable for assessing intervention effects. Several examples are discussed from application areas such as:

  • multilevel AR(1) model with random mean, random AR, and random variance
  • multilevel AR(1) model with measurement error
  • multilevel ARMA(1,1) model
  • multilevel cross-lagged modeling
  • multilevel AR modeling with a trend
  • latent multilevel AR(1) model with multiple indicators
  • latent multilevel VAR(1) model and dynamical networks
  • dynamic SEM
  • dynamic latent class analysis using hidden Markov and Markov-switching AR models

Here is the webinar information:

Please RSVP to Estie Hudes if you are interested in attending this webinar.


Materials from past seminars



  • March 7, 2017 – David Benkeser, PhD:  Optimally Combining Outcomes To Improve Prediction
  • January 20, 2017 – Carl A. Latkin, PhD: Randomized clinical trials of social network approaches to HIV prevention and care: Lessons learned
  • May 17, 2016 —   John A. Schneider MD, MPH, PhD: Social Network Data Collection Approaches and Strategies for Introductory Analysis and Intervention PlanningVideo
  • May 3, 2016 — Tor Neilands, PhD, Kim Koester, MA & Troy Wood, MA: An Introduction to Survey Scale Development and Cognitive Interviewing
  • April 19-20, 2016 — Blair Johnson, PhD & Tania Huedo-Medina, PhD: Meta-Analysis workshop