An extremely useful area of statistics is a set of models that use latent variables: variables whole values we can’t measure directly, but instead have to infer from others. These latent variables can be unknown groups, unknown numerical values, or unknown patterns in trajectories.
In this training we will present an overview of seven types of latent variable models. For each of the following techniques, we will discuss when to use it, what it does, and give examples:
- Latent Class Analysis
- Latent Transition Analysis
- Latent Profile Analysis
- Confirmatory Factor Analysis
- Structural Equation Modeling
- Latent Growth Curve Models
- Growth Mixture Models
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 with The Analysis Factor, a stats mentor for Statistically Speaking membership, and a workshop instructor. Read more about Jeff here.
Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians.
Just head over and sign up for Statistically Speaking.
You'll get access to this training webinar, 100+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.