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Member Training: A Guide to Latent Variable Models

by Jeff Meyer

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.

Not a Member? Join!

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.

Tagged With: Confirmatory Factor Analysis, Growth Mixture Model, latent class analysis, Latent Growth Curve Model, Latent Profile Analysis, Latent Transition Analysis, latent variable, Structural Equation Modeling

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  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Member Training: Introduction to Structural Equation Modeling
  • Member Training: Reporting Structural Equation Modeling Results

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