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.
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