1. Factor Analysis

2. Description
Goal: Discover m < p underlying Factors (aka latent variables) from Covariances or Correlations among p observed variables
Makes use of Principal Components and Rotation to obtain the Factors
Exploratory Factor Analysis: Using observed responses to obtain factor structure.
Confirmatory Factor Analysis: Uses new data to determine whether hypothesized Factor structure is appropriate.

3. Orthogonal Factor Model - I

4. Orthogonal Factor Model - II

5. Example 1

6. Example 2

7. Estimation – Principal Factor Method - I

8. Estimation – Principal Factor Method - II

9. Selecting m, the Number of Retained Factors

10. Maximum Likelihood Estimation I – Normal F, e

11. Maximum Likelihood Estimation – Normal F, e

12. Large-Sample Test for # of Common Factors (m)

13. Factor Rotation

14. Estimating Factor Scores – Weighted Least Squares

15. Estimating Factor Scores – Regression Approach - I

16. Estimating Factor Scores – Regression Approach - II