6.2 Reading

Notes:

This article is quite general and provides an overview of things we have discussed so far in this course. This article also also adds an important new idea: combining factor analysis with path modeling to produce a full Structural Equation Model (SEM).
Skip the part on GFI (p. 741).
- The GFI has been shown to be too dependent on sample size and is not recommended any longer.
Skip the part on missing data.
- There is nothing wrong with this section, but missing data analysis is a broad and difficult topic that we cannot adequately cover in this course.
- If you would like to learn more about missing data and how to treat them, you can take two courses offered by our department:
  - Conducting a Survey
  - Missing Data Theory and Causal Effects

The authors state three similarities and two big differences between SEM and other multivariate statistical techniques (e.g., ANCOVA, regression). What are these similarities and differences?
Do you agree with the relative strengths and weaknesses of SEM vs. other methods that the authors present?
The authors miss at least one additional advantage of SEM over other multivariate methods. What is this missing advantage?
Explain what the terms “measurement model” and “structural model” mean in the SEM context.
What are the 6 steps of doing an SEM-based analysis given by the authors?
The authors claim that testing an SEM using cross-validation is a good idea. When is cross-validation helpful in SEM?
- Hint: You may have to do some independent (internet, literature) research to learn how cross-validation can be implemented in SEM.

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