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

COMPUTING

Mplus will be used for all worked examples, but prior knowledge of Mplus is not essential. Participants are welcome and encouraged to bring their own laptop computer with the basic Mplus package installed, which will be used for hands-on exercises. Doing so is not required, however. Participants will still greatly benefit from the instruction, comprehensive set of slides, and software syntax that they can apply at home.  


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 5 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 includes all course materials. The early registration fee of $895 is available until September 2.

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 $174 per night. 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 STH101 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, September 1, 2020.

If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) 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 

“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