Advanced Statistics - JSM010

• Title: Advanced Statistics
• Form of teaching: lecture+practicals
• Guaranteed by: Department of Sociology (23-KS)
• Faculty: Faculty of Social Sciences
• Actual: from 2022
• Duration in semesters: 1
• Semester: summer
• E-Credits: 8
• Examination process: summer s.:written
• Hours per week, examination: summer s.:1/1, Ex [HT]
• Capacity: unlimited / unlimited (15)
• Maximum number of enrolled students: 15
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Additional information: http://ICS/
• Repeated enrollment: 2 / 2 / 1 / 2
• Note: course can be enrolled in outside the study plan; enabled for web enrollment; priority enrollment if the course is part of the study plan
• Guarantor: PhDr. Ing. Petr Soukup, Ph.D.
• Teacher(s): PhDr. Ing. Petr Soukup, Ph.D.
• Class: Courses for incoming students

Annotation

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 and Jamovi(2025).

It is possible to follow the lecture online via MS Teams:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_N2UwMzRiYjgtYzI4Yy00YTc3LTgyMDctZTNjYWY5MGNmZTBl%40thread.v2/0?context=%7b%22Tid%22%3a%22e09276da-f934-4086-bf08-8816a20414a2%22%2c%22Oid%22%3a%22b8a82895-c898-4e19-a4ab-bcfd4e0c03ac%22%7d

Recordings will be available for all lectures:
https://drive.google.com/drive/folders/1suZrVW5lbLcB6UxEHIgMCH0uXGDCHNa2?usp=drive_link

Link for questionnaire for 2nd lecture: https://forms.gle/84Nh5JJWKvykZPnp6

Last update: Soukup Petr, PhDr. Ing., Ph.D. (22.02.2026)

Course completion requirements

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.

Last update: Soukup Petr, PhDr. Ing., Ph.D. (10.02.2025)

Literature

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. 

Last update: Soukup Petr, PhDr. Ing., Ph.D. (10.02.2025)

Syllabus

 1.      Introduction to SPSS and amovi. 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. 

Last update: Soukup Petr, PhDr. Ing., Ph.D. (10.02.2025)

Learning outcomes

1. Define and explain key terms, concepts, and models of advanced statistics.
2. Apply individual models for real data sets.
3. Develop skills for data analyses for at least two different statistical packages.
4. Interpret results of analyses.
5. Compare models and critically assess possible changes.
6. Design future surveys that can serve as data sources for advanced statistics.

Last update: Soukup Petr, PhDr. Ing., Ph.D. (26.11.2025)