Latent Growth Curve Modeling

A 3-Day Remote Seminar Taught by
Gregory R. Hancock, Ph.D.

Read reviews of the in-person version of this seminar

To see a sample of the course materials, click here.


Longitudinal data are ubiquitous throughout the social and behavioral sciences and beyond, where researchers have questions about the nature of change over time as well as its determinants. This seminar provides a thorough introduction to latent growth curve models, which facilitate an assessment of longitudinal change from within the structural equation modeling (SEM) framework. 

Starting October 1, we are offering this seminar as a 3-day synchronous*, remote workshop for the first time. Each day will consist of a 4-hour, live morning lecture held via the free video-conferencing software Zoom. Participants are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if they are unable to attend at the scheduled time.

Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on one’s own. An additional session will be held Thursday and Friday afternoons as an “office hour”, where participants can review the exercise results with the instructor and ask any questions.

*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session, meaning that you will get all of the class content and discussions even if you cannot participate synchronously. 


MORE DETAILS ABOUT THE COURSE CONTENT

The seminar will start with a quick review of SEM with measured and latent variables, illustrating the use of Mplus for such models. Next, latent means models, which add a mean structure to typical covariance-based structural models, will be introduced and illustrated with Mplus. The seminar will then review more traditional longitudinal models within an SEM framework (repeated measure models, panel models, etc.) to finish laying the necessary foundations.

The seminar will then move into a thorough coverage of traditional linear latent growth models, including but not limited to different time centering, uneven and varied time points, and time-independent covariates. Then topics will transition into more complex modeling variations, drawing from the following areas as time allows:

  • nonlinear models
  • spline models
  • time-dependent covariates
  • growth models for treatments and interventions
  • multidomain models
  • cohort-sequential models for planned missing data
  • second-order growth models
  • latent-difference score models
  • growth models with categorical data
  • growth mixture models
  • power analysis in latent growth models

COMPUTING

This remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Before the seminar begins, participants will receive an email with the meeting code and password you must use to join.

Mplus will be used for all worked examples, but prior knowledge of Mplus is not essential.


Who should Register?

To benefit from this seminar, participants should have had exposure to statistical methods up through structural equation modeling, which includes topics such as measured variable path models, confirmatory factor models, latent variable path models, multisample covariance structure models, model identification, estimation, data-model fit assessment, and model modification/respecification. Familiarity with, or access to, Mplus software is not required for this seminar.


Outline

Foundations

  • quick review of SEM with measured and latent variables
  • latent means models
  • traditional longitudinal models framed in SEM (e.g., repeated measure models, panel models)
  • traditional linear latent growth models
  • varied reference points
  • uneven and varied time points
  • time-independent covariates
  • reparameterizing linear models

Beyond (as time allows)

  • nonlinear models
  • reparameterizing nonlinear models
  • spline models
  • time-dependent covariates
  • growth models for treatments and interventions
  • multidomain models
  • cohort-sequential models for planned missing data
  • second-order growth models
  • latent-difference score models
  • power analysis in latent growth models
  • growth mixture models
  • growth models with categorical data

REVIEWS OF Latent Growth Curve Modeling 

“Greg Hancock is an outstanding teacher! He presents material clearly, moves at a good pace, and welcomes questions. I had some basic knowledge about latent growth modeling, and this class was extremely informative. I am confident I will be able to apply what I have learned here in more complex models.”
  Mary Mitchell, Friends Research Institute

“Dr. Hancock’s friendly and approachable demeanor was refreshing. The course provided valuable information that is highly applicable to my health outcomes research.”
  Windy Alonso, University of Nebraska

“Dr. Hancock’s LGCM seminar was GREAT! He made complex concepts easier to understand because he explained material in simple (applied) terms without using too much technical jargon. Hands down the best stats lecture I have attended. I highly recommend this seminar.”
  Miguel Á. Cano, Florida International University

“This course makes growth curve modeling easy to understand with plenty of examples that are very relatable. Although the syntax is given for Mplus, the way it was coded can be easily translated to other software packages with similarly simple model specification statements. I highly recommend this course as a first step to starting any growth curve modeling and to get a flavor of more advanced LGCM topics.”
  Andy Kin On Wong, McMaster University

“This course was taught at a very high but accessible level. Professor Hancock is not only skilled in terms of his knowledge of the subject matter, but he is also a very skilled and approachable teacher. This is simply not a job to him, but he intrinsically cares about his students learning and applying Latent Growth Curve Modeling to their respective research projects. Excellent professor and course.”
  Horace Bartilow, University of Kentucky