This course will introduce students to three forms of growth curve analysis: latent growth curves, latent growth trajectory analysis, and growth mixture models. This class of statistical methods provides a powerful set of tools to describe changes in a population. Students will be taught the differences between types of models, when to apply the correct model, and how to interpret results. Simple simulations of analyses will be conducted in R.
This workshop explains how to model trends across individuals or groups. It presents the foundations of growth models in standard models such as the mean and linear regression. Topics include random intercepts and slopes models, latent growth trajectory analysis, and growth mixture models.
The course also covers topics related to modeling associations with differences in intercepts and slopes including how to think about modeling time, time-invariant and time-varying predictors, and special cases such as modeling interventions.
Students will be introduced to different models through simulations in R that are then applied to a practical example. Students may follow along on their own using code from the course, which can be accessed at the course GitHub repository.
- Describe how latent growth, latent growth trajectory analysis, and growth mixture models are related to, and different from, one another;
- Interpret results all three types of growth models;
- Identify the proper modeling technique for analytical questions regarding change; and
- Clearly articulate the benefits, assumptions, and shortcomings of different models of change;