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Growth Modeling

August 6-7, 2020

Course Information

This workshop is designed to introduce participants to the use of growth modeling in longitudinal research and developmental science. We work through the basics of growth modeling and into advanced topics such as multiple group growth models and growth mixture models. The training is intended for faculty, postdocs and advanced graduate students who are familiar with longitudinal data and the basics of structural equation modeling or multilevel modeling. Course materials include lecture notes, readings, and a full set of Mplus and R scripts. The workshop is structured around a series of Lecture sessions that cover the theory and construction of growth models, Lab sessions that walk though programming code and how the various models are specified and applied to empirical data, and Question & Answer sessions where participants can discuss the material and their own data and modeling interests with the instructors.

Instructors: Kevin J. Grimm, Department of Psychology at Arizona State University; Nilam Ram, Departments of Psychology and Communication at Stanford University 

Format: The workshop has multiple components – (a) pre-recorded lectures, (b) pre-recorded Lab sessions that work through code, and (c) live Q&A sessions via Zoom.

(a) Lectures.A collection of pre-recorded video lectures can be accessed through our web-site. Handouts of the lecture slides are available for download.

(b) Labs. Parallel pre-recorded videos where an instructor walks through coding details and interpretation of real empirical examples are available for various software programs (e.g., Mplus, R) can be accessed through the website. The code examples are available for download.

(c) Q & A sessions. In addition to the pre-recorded lectures and labs, there is a live question and answer session each day, about 30 minutes for each topic. These are done over Zoom. During this time, participants are encouraged to ask questions pertinent to the lecture/lab material and how the covered techniques might apply in other situations. The Q & A sessions will be recorded and made available for later viewing.

The language of the course is English; no translation services will be provided.

We encourage participants to watch the lectures prior to the live sessions and submit questions through the web site or via chat in Zoom. We encourage participants to apply the longitudinal models reviewed in the lectures and lab sessions to their own data and to ask questions about the programming and interpretation of the modeling results.

Course Topic Schedule

Day 1: Introduction to Growth Modeling

  • Linear Growth Model (Mplus, R)
  • Linear Growth Models with Time-Invariate Covariates (Mplus, R)
  • Linear Growth Models with Time-Varying Covariates (Mplus, R)

Day 2: Growth Modeling with Multiple Groups

  • Multiple Group Growth Modeling: Known Groups (Mplus, R)
  • Growth Mixture Modeling: Unknown Groups (Mplus)
  • Bivariate (Multivariate) Growth Models (Mplus, R)

Session Listing

  • 1. Introduction to Structural Equation Modeling (60 minutes)
  • 2. Introduction to Multilevel Modeling (34 minutes)
  • 3. Linear Growth Models (35 minutes)
    • 3a. Linear Growth Models in Mplus (30 minutes)
    • 3b. Linear Growth Models in R – Multilevel (30 min)
    • 3c. Linear Growth Models in R – SEM (34 min)
  • 4. Continuous Timing Metrics in Linear Growth Models (21 minutes)
    • 4a. Continuous Timing Metrics in Mplus (18 minutes)
    • 4b. Continuous TIming Metrics in R (24 min)
  • 5. Time-Invariant Covariates in Growth Models (30 min)
    • 5a. Time-Invariant Covariates in Mplus (15 minutes)
    • 5b. Time-Invariant Covariates in R (26 min)
  • 6. Multiple Group Growth Models (Nilam)
    • 6a. Multiple Group Growth Models in Mplus (15 minutes)
    • 6b. Multiple Group Growth Models in R (32 minutes)
  • 7. Growth Mixture Models (44 minutes)
    • 7a. Growth Mixture Models in Mplus (26 minutes)
  • 8. Bivariate Growth Models (21 minutes)
    • 8a. Bivariate Growth Models in Mplus (12 minutes)
    • 8b. Bivariate Growth Models in R (18 minutes)