Advanced Statistics in SPSS and AMOS - JSB526

• Title: Advanced Statistics in SPSS and AMOS
• Guaranteed by: Department of Sociology (23-KS)
• Faculty: Faculty of Social Sciences
• Actual: from 2020 to 2021
• Semester: summer
• E-Credits: 6
• Examination process: summer s.:
• Hours per week, examination: summer s.:1/1, C [HT]
• Capacity: unlimited / unlimited (unknown)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Teaching methods: full-time
• Additional information: http://samba.fsv.cuni.cz/~soukp6as/ADVANCED_STATISTICS/
• Note: course can be enrolled in outside the study plan; enabled for web enrollment
• Guarantor: PhDr. Ing. Petr Soukup, Ph.D.
• Teacher(s): PhDr. Ing. Petr Soukup, Ph.D.
• Class: Courses for incoming students

Literature

Last update: PhDr. Ing. Petr Soukup, Ph.D. (13.02.2023)

Tarling, R. 2009. Statistical Modelling for Social Researchers, Routledge. 

 

Requirements to the exam

Last update: PhDr. Ing. Petr Soukup, Ph.D. (15.02.2024)

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. 

Syllabus

Last update: PhDr. Ing. Petr Soukup, Ph.D. (06.02.2023)

The course will be held in Pekarska building, computer lab 308

Online access is possible via ZOOM:

https://cesnet.zoom.us/j/4677639176?pwd=dVAyWkJISGRrOGp2ZUJRc0I2Y3p2UT09

Meeting ID: 467 763 9176

Passcode: 355749

 

 1.      Introduction to jamovi. Descriptive statistics and correlation analysis in Jamovi. Missing values, results, handling and replacing. (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)

4.      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)

5.      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)

6. Intro to SEM, correlation, regression and path analysis.

7.      Confirmatory factor analysis for cardinal, ordinal and binary indicators. Model fit indices and criteria. Basic equations and graphical presentation. Modification indices, Bayesian estimation for nominal and ordinal data. (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.