Member Training: Latent Growth Curve Models

What statistical model would you use for longitudinal data to analyze between-subject differences with within-subject change?

Most analysts would respond, “a mixed model,” but have you ever heard of latent growth curves? How about latent trajectories, latent curves, growth curves, or time paths, which are other names for the same approach?

Latent Growth Curve models use a Structural Equation Modeling approach to model change over time, which introduces quite a bit of flexibility.

The object of this webinar is to familiarize you with this type of statistical modeling, and answer these questions:

• How does it vary from the traditional mixed/repeated measures approach to analyzing longitudinal data?
• Is one of these approaches better for specific types of data?
• What are the data requirements for using a latent growth curve?
• Do these models produce residuals and/or variances and covariances?
• What statistics are used to determine goodness of fit?

As part of this webinar, we will not only run a model and examine the output, but we will then compare to the output of a mixed model.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

About the Instructor

Jeff Meyer is a statistical consultant and the Stata expert at The Analysis Factor. He teaches workshops and provides Stata examples for a number of our workshops, including Intro to Stata, Missing Data, and Repeated Measures.

He also runs his own consulting firm, Optimizing Outcomes, which helps non-profits determine the impact of their outcomes.

Jeff has an MBA from the Thunderbird School of Global Management and an MPA with a focus on policy from NYU Wagner School of Public Service.

Not a Member Yet?

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You’ll get exclusive access to this training webinar, plus live Q&A sessions, a private stats forum, 75+ other stats trainings, and more.

Tagged as: between-subject, latent growth curve models, Longitudinal Data, Mixed, model, modeling, SEM, structural equation, within-subject