Latent Growth Curve Modeling

A 2-Day Seminar Taught by Gregory Hancock, Ph.D.  

Read reviews 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 two-day 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. 

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

Who should attend?

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.


LOCATION, FORMAT AND MATERIALS

The class will meet from 9 am to 4 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. 

Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking. 


Registration and lodging

The fee of $995.00 includes all seminar materials. The early registration fee of $895.00 is available until May 1.

Refund Policy
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). 

Lodging Reservation Instructions

A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $154. This location is about a 5 minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STA531 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, May 1, 2017. 

If you make reservations after the cut-off date ask for the Statistical Horizons room rate (do not use the code) and they will try to accommodate your request. 


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

Read COMMENTS FROM Participants 

“Even as a methodologist who has studied some of the models in the course previously, I found the information and examples very informative and helpful. Plus, Greg Hancock was a great instructor.”
  Fraser Bocell, University of Washington

“The course was excellent in applying models to real world data problems. The instruction was clear, with time for questions about specific problems.”
  Vivia McCutcheon, Washington University in St. Louis

“This is my first time attending a workshop by Statistical Horizons. If the quality is same for other courses, I will definitely attend more workshops. I was so grateful that the instructor (Dr. Hancock) spent time before class and during lunch to provide his individual consultations.”
  Suk-Young Kang, Binghamton University

“I thought that the content and pace were excellent. He laid out a good foundation or review at the beginning and had interesting examples with real data. Some of these were very relevant to the things I’m doing and will no doubt help me with my continued study in this field.”
  Lien Vu, Temple University

“I like the way the instructor delivered the course with simple common language as well as real world examples that made it easier for me to imagine how each model will be utilized into my own work.”
  Edimansyah Abdin, Institute of Mental Health