Advanced Statistics - JSB526

• Anglický název: Advanced Statistics
• Zajišťuje: Katedra sociologie (23-KS)
• Fakulta: Fakulta sociálních věd
• Platnost: od 2022
• Semestr: letní
• E-Kredity: 6
• Způsob provedení zkoušky: letní s.:
• Rozsah, examinace: letní s.:1/1, Z [HT]
• Počet míst: neomezen / neomezen (10)
• Minimální obsazenost: neomezen
• 4EU+: ne
• Virtuální mobilita / počet míst pro virtuální mobilitu: ne
• Stav předmětu: vyučován
• Jazyk výuky: angličtina
• Způsob výuky: prezenční
• Způsob výuky: prezenční
• Další informace: http://samba.fsv.cuni.cz/~soukp6as/ADVANCED_STATISTICS/
• Poznámka: předmět je možno zapsat mimo plán; povolen pro zápis po webu
• Garant: PhDr. Ing. Petr Soukup, Ph.D.
• Vyučující: PhDr. Ing. Petr Soukup, Ph.D.
• Třída: Courses for incoming students

Anotace

Poslední úprava: PhDr. Ing. Petr Soukup, Ph.D. (06.02.2023)

The course is designed mainly for students of following study programmes: Social Sciences and Sociology (in Czech). All other students wiil be allowed to enter only if the capacity is not full.

This course is an overview course over topics of the use of statistics in sociology, which will cover advanced statistical procedures: linear regression, logistic regression,
loglinear models, latent class analysis, exploratory factor analysis and confirmatory factor analysis. All procedures will be demonstrated and practised in Jamovi(2023).

Literatura

Poslední úprava: PhDr. Ing. Petr Soukup, Ph.D. (13.02.2023)

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

 

Požadavky ke zkoušce

Poslední úprava: 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. 

Sylabus

Poslední úprava: 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.