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 is available until March 27.

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 $159 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 SH0426 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, March 26, 2018. 

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 

“This course helped solidify material I already knew and built on this knowledge in an approachable, easy-to-understand manner. I can easily take these materials, syntax, and information and apply to my own work.”
  Quin Denfeld, Oregon Health & Science University

“I took Dr. Gregory Hancock’s Latent Growth Curve Modeling seminar. In the 2-days workshop a lot of latent growth models were covered in the materials. Mplus codes were provided. Even though the workshop is short the materials are very helpful for my future research project. Dr. Hancock is very professional, patient, helpful and supportive!”
  Qin Lu, University of Kansas

“The instructor had an incredibly helpful pace with the material. The combination of content, examples, and pace made it easy to follow and track the information throughout both days.”
  Amanda Mitchell, The Ohio State University, Wexner Medical Center 

“This is my first time attending a workshop conducted by Statistical Horizons and I immediately loved it. Compared to other workshops I took before, the quality is phenomenal. The course contents are clear and easy to follow and the flow is smooth. Further, the workshop (LGCM) provides tons of examples that can be modified and can be applied in my field. I highly recommend this workshop and I would definitely come back for others.”
  Yu-Chih Chen, Washington University in St. Louis 

“With Dr. Hancock, I could figure out LGCM, which is a very complex model, in a simpler way. He is the best scholar to teach this course.”
  Anonymous  

“This course is a terrific overview of growth curve modeling for anyone interested in understanding and exploring growth/­­­­trajectory models. Dr. Hancock is a pro in the method. He is also a very skilled instructor. The material builds on basic concepts. Overall, I highly recommend.”
  Judith Havlicek, University of Illinois Urbana-Champaign

“Incredibly helpful; the instructor was patient and covered the material in a way that individuals from all skill levels and knowledge could understand.”
  Kelley Quirk, Colorado State University

“Hugely helpful course with nuanced perspectives on fit and interpretation. I left motivated and inspired to conceptualize my data in new ways that better reflect my research questions.”
  Stephanie Black, Penn State Harrisburg