1. Regression, Factor Analysis and SEM Ulf H. Olsson Professor of Statistics

2. Ulf H. Olsson Regression with observed variables Significant effects R-sq Distributional assumptions for OLS Measurement Errors Bias (attenuation towards zero)

3. Ulf H. Olsson Path Analysis (Simultanuous equations with observed variables) Can all the parmeters be identified Does the model fit the data ML and the chi-sq. test

4. Ulf H. Olsson EFA How many factors? Rotation Does it make sense?

5. Ulf H. Olsson CFA/Measurement Models Does the model fit the data? Reliability Can the model re-specified

6. Ulf H. Olsson SEM Structural equations = Multiple regression models with latent variables The fit of the SEM model will never be better than a ”saturated model” I.e,. The measurement model will have the best ”fit-measures”

7. Ulf H. Olsson Assumptions Continuous data Normal Non-normal Ordinal variables Ordered categories Structural assumptions Chi-square distribution Non-central Chi-square distribution