Advanced Statistics - JSM010

• Title: Advanced Statistics
• Guaranteed by: Department of Sociology (23-KS)
• Faculty: Faculty of Social Sciences
• Actual: from 2022
• Semester: summer
• E-Credits: 8
• Examination process: summer s.:written
• Hours per week, examination: summer s.:1/1, Ex [HT]
• Capacity: unlimited / unlimited (15)
• 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://ICS/
• 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

Annotation

Last update: Mirna Jusić, M.A., Ph.D. (26.02.2024)

The course is designed mainly for students of SCS, SOCOME and Sociology study programmes. All other students will 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, latent class analysis, exploratory factor analysis and confirmatory factor analysis. All procedures will be demonstrated and practised in SPSS, JASP and Jamovi(2024).

It is possible to follow the lecture online via MS Teams:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_OTNiNDFjOTYtMDQxZC00ODE3LTgxYmQtZjBmY2I5NDBjZmFh%40thread.v2/0?context=%7b%22Tid%22%3a%2273844aaf-f10c-4dee-aaaf-5eeb27962a5d%22%2c%22Oid%22%3a%2244019797-e6cf-458d-996e-9e9b298c7895%22%7d

Recordings will be available for all lectures:
https://drive.google.com/drive/folders/1a9xBZCu9Um8RAYAkUoUVlPDGtdWIeGgU?usp=sharing

Link for questionnaire for 1st lecture: https://forms.gle/44UJ2b5wQVkuRQbR6

Course completion requirements

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

Exam consist of 5 homework, oral exam and draft (article) (every part is evaluated separately 0-100 %). Draft (article) should use classical structure:  problem formulation (derived from theory), hypotheses formulation and statistical analysis for hypotheses evaluation (at least two techniques from sylabus).

Weights for final evalution: every hw 10 %, oral exam 20 % ( for MA students) and draft 30 % (only for MA students).

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

Literature

Last update: Mirna Jusić, M.A., Ph.D. (26.02.2024)

Field. A. 2009 Discovering statistics using SPSS. Sage

Norusis, M. 2005. Advanced Statistical Procedure Companion. Prentice Hall.

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

Syllabus

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

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.