Structural Equation Modeling provides a general analysis framework for investigating associations between observed and unobserved variables. Numerous models can be fit within this flexible framework. This flexibility comes from the ability to model variances, covariances, and means in observed data.
This tutorial video includes information and Q&A about the purposes, approaches, and statistical underpinnings of the basics of Structural Equation Models. It begins with the basics of SEM and is followed by 3 case studies, one for each of the two SEM building blocks (linear regression and confirmatory factor analysis) and one to highlight new JMP Pro 17 capabilities for multi-group analysis. It concludes with a summary of the key benefits of SEM and using JMP Pro for SEM. Beginning times for each section are included below. Use the attached JMP tables to try SEM in your copy of JMP Pro 17.
See how to:
- Understand the what, why, and how of SEM
- Handle missing data with cutting edge methods without the hassle of multiple imputation
- Specify a model in which variables are both predictors and outcomes
- Understand mechanisms by which things happen
- Test specific theories about the association of variables and leverage your domain expertise
- Model variables that cannot be measured directly (aka latent variables)
- Model variables that have measurement error (and account for it)
- Create Diagrams that describe your statistical models intuitively
- Understand the statistics behind the models
- Building Block #1 – Simple Linear Regression and Path Analysis (Starts at ~ time 42:34)
- Building Block #2 – Confirmatory Factor Analysis (Starts at ~ time 1:08:00)
- Multiple Group Analysis (Starts at ~ time 1:21:13)
- Extends all models fit in SEM
- Investigate similarities and differences across populations
- Requires grouping variable (often with few levels)
