The course will be held in Jinonice building, computer lab 229

Online access is possible via MS Teams:

1. Introduction to SPSS syntax language. Descriptive statistics and correlation analysis in SPSS. Missing values, results, handling and replacing. Data weighting. (1 lecture)

2. Linear regression analysis - simple and multiple regression. Assumptions, model fit, possible modification of regression model. Model evaluation and interpretation. Dummy variables, multicollinearity, influential points, heteroscedasticity. Robust regression.(1st HW) (2 lectures)

3. Logistic regression - binary, ordinal and polytomous model. Odds, odd ratio, logit. Model evaluation and interpretation. (2nd HW) (2 lectures)

5. Latent class analysis (typology from binary and nominal variables). Explanatory and confirmatory approach. Unconditional latent class probability and conditional probability of individual answer. Comparison of models (decision about the number of latent classes). (3rd HW) (1 lectures)

6. Exploratory factor analysis. Assumptions, number of factors, Extraction and rotation. Factor weights and interpretation of factors. Factor scores and it’s usage. (4th HW) (1 lecture)

7. Introduction to SEM. Correlation and regression as SEM model. Path analysis. Evaluation of SEM.

8. Confirmatory factor analysis for cardinal, ordinal and binary indicators. Model fit indices and criteria. Basic equations and graphical presentation. Modification indices. (5th HW) (2 lectures)

Exam consist of 5 homework and oral exam (every part is evaluated separately 0-100 %).

Weights for final evalution: every hw 10 %, oral exam 50 % (for BA students).

Final grading: 0-50 % 4 (failed), 51 % - 60 % E, ), 61 % - 70 % D, 71-80 % C, ), 81 % - 90 % B and 91 % and more A.