1. Intro to SEM
P. Soukup

2. Literature
Kline Principles and practice of structural equation modeling. New York : Guilford Press
Byrne Structural equation modeling with AMOS :basic concepts, applications, and programming. New Jersey: Lawrence Erlbaum
Maruyama Basics of structural equation modeling. Sage Publications
Raykov and Marcoulides A first course in structural equation modeling. Mahwah : Lawrence Erlbaum Associates
Schumacker and Lomax.2004.A beginner’s guide to structural equation modeling. Mahwah : Lawrence Erlbaum Associates
Articles:
Journal Structural Equation Modeling

3. Exploratory FA
Confirmatory FA

4. Why SEM (CFA)? Testing of hypothesis(es)
Complex model of relashionships between latent and manifest vars
Also possible: compare groups, longitudinal analysis etc.

5. Correlation, regression and path analysis
Before CFA
Correlation, regression and path analysis

6. Correlation and simple regression
Both sided relationship=correlation
One sided relationship = regression (simple). E

7. Multiple Regression analysis
More ind. vars
Y' = a + b1X1 +b2X2 +b3X3

8. Some statistical notes
Nr. of estimated parameters (what are these)
Correlation
Regression
Nr. of individual pieces of info from data
Degrees of freedom
Test of model: chi-square
Examples in regression and correlation

9. Path analysis=more regressions
Two or more regressions at once
Dependent and independent vars – necessary to exchange by exogenous and endogenous
Direct and indirect effects
Measurement error (E) E E

10. Path analysis-example
Duncan’s model
Evaluation of different models
Constraining of parameters
The best model? (AIC or BIC for selection)

11. Intro to CFA

12. Exploratory FA
Confirmatory FA

13. CFA – equations (I)
Equations for vars: E E

14. CFA – equations (II) Equations for covariances:
We have: covariance matrix for manifest vars in our data (Σ)
We estimate covariance matrix of latent vars (Ψ), measurement errors (Θ) and factor weights (Λ) E E

15. CFA – estimates Many techniques max. likelihood generalized LS
unweighted LS
generalized LS
max. likelihood E E

16. CFA – evaluation Overall evaluation – test and criterias
Individual parameters – statistical and substantive significance
Change in model – modification indeces E E

17. Software for SEM
AMOS
EQS
LISREL
MPlus
SAS – CALIS
Statistica - SEPATH