What is the difference between SEM and regression analysis?

What is the difference between SEM and regression analysis?

SEM, or Structural Equation Modeling, is a comprehensive statistical approach that includes regression analysis but also allows for complex relationships between variables, including latent variables. Regression analysis, on the other hand, focuses on the relationship between independent and dependent variables without considering latent constructs.

Can you give me an example of when to use SEM instead of regression?

Sure! Use SEM when you want to test a theoretical model that includes multiple dependent variables and latent constructs, like in social sciences. For instance, if you're studying the impact of education on job satisfaction and income, SEM can help you understand the indirect effects and relationships.

What are the advantages of using SEM?

Some advantages of SEM include its ability to model complex relationships, handle measurement error, and test theoretical models with multiple variables simultaneously. It provides a more holistic view of the data.

Are there any limitations to SEM?

Yes, SEM can be complex and requires large sample sizes for reliable results. It also assumes that the model is correctly specified, which can be challenging in practice.