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Advanced Methods and of Quantitative Research - ASG500301

Title:	Pokročilé metody kvantitativního výzkumu
Guaranteed by:	Department of Sociology (21-KSOC)
Faculty:	Faculty of Arts
Actual:	from 2024
Semester:	summer
Points:	0
E-Credits:	6
Examination process:	summer s.:
Hours per week, examination:	summer s.:2/1, Ex [HT]
Capacity:	25 / 25 (unknown)
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
Key competences:
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Level:
Note:	course can be enrolled in outside the study plan enabled for web enrollment

Guarantor:	doc. PhDr. Jiří Buriánek, CSc. Mgr. Zuzana Podaná, Ph.D. PhDr. Jiří Šafr, Ph.D.
Teacher(s):	Mgr. Martin Betinec, Ph.D. Mgr. Zuzana Podaná, Ph.D.

SS schedule Noticeboard

Annotation -

The course deepens and enlarges students' knowlege on advanced methods of multidimensional statistical analysis. 
Besines the introduction to the methods, the focus is on their proper choice, application and justification, as well as interpretation and presentation of the results. 
Seminary part (realized by four workshops (3 hours /each) serves for practical exercise in usage of the statistical software for the methods application. 
 
 
The following topics will be presented: 
1. Typology of multidimensional methods. Basic descriptive methods and graphs. Smooth introduction to multidimensional geometry. 
2. Principal Component Analysis}: geometry, interpretation and usage. 
3. Factor Analysis: theoretical assumptions, geometry, implications, description, interpretation and prediction. Relation to PCA. 
4. Cluster Analysis. 
5. Discriminant analysis. Linear, Fisher's, quadratic ... Introduction to classification. 
6. Classification and Regression Trees (CART). Slight introduction to other (non-linear) methods (neural networks, SVM). Measurement of classifiers' quality. 
7. Regression and Generalized Linear Models. 
8. Logistic regression. 
9. Log-lineár regression models and analysis of contingency tables.

Last update: Podaná Zuzana, Mgr., Ph.D. (24.01.2026)

Literature -

Agresti, A. (2007). An introduction to categorical data analysis. Wiley-Interscience.
Berka, P.: Dobývání znalostí z databází, Academia, Praha (2003).
Breiman, L; Friedman, J. H., Olshen, R. A., & Stone, C. J.: Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software, (1984)
Cohen, J. (Ed.). (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates.
Disman, M.: Jak se vyrábí sociologická znalost, Karolinum, Praha (2002)
Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models (3rd Ed.). SAGE.
Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and Other Stories. Cambridge University Press.
Hebák, P. a kol.: Vícerozměrné statistické metody I-III. INFORMATORIUM, Praha, (2004 - 2005)
Harrington, D. (2009). Confirmatory Factor Analysis. Oxford University Press.
Hendl, J. (2012). Přehled statistických metod: Analýza a metaanalýza dat (4. vyd.). Portál.
Hox, J. J., Moerbeek, M., & Van de Schoot, R. (2017). Multilevel analysis: Techniques and applications. Routledge.
Littell, J. H., Corcoran, J., & Pillai, V. (2008). Systematic Reviews and Meta-Analysis. Oxford University Press.
Norusis, M. (2012). IBM SPSS Statistics 19 Advanced Statistical Procedures Companion. Pearson Education.
Norusis, M. (2012). IBM SPSS Statistics 19 Statistical Procedures Companion. Prentice Hall.
Peňa, D.: Análisis de datos multivariantes. McGraw-Hill, Madrid (2002)
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Pearson/Allyn & Bacon.
Venables, W. N. and Ripley, B. D.: Modern Applied Statistics with S. Springer-Verlag, New York(2002)
Thereneau, T.M. a Atkinson, E. J.: An Introdiction ro recursive Partitioning Using the RPARTRoutines. Mayo Foundation, (2011). Documentation to R-package.

Last update: Betinec Martin, Mgr., Ph.D. (05.02.2025)

Agresti, A. (2007). An introduction to categorical data analysis. Wiley-Interscience.
Berka, P.: Dobývání znalostí z databází, Academia, Praha (2003).
Breiman, L; Friedman, J. H., Olshen, R. A., & Stone, C. J.: Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software, (1984)
Cohen, J. (Ed.). (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates.
Disman, M.: Jak se vyrábí sociologická znalost, Karolinum, Praha (2002)
Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models (3rd Ed.). SAGE.
Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and Other Stories. Cambridge University Press.
Hebák, P. a kol.: Vícerozměrné statistické metody I-III. INFORMATORIUM, Praha, (2004 - 2005)
Harrington, D. (2009). Confirmatory Factor Analysis. Oxford University Press.
Hendl, J. (2012). Přehled statistických metod: Analýza a metaanalýza dat (4. vyd.). Portál.
Hox, J. J., Moerbeek, M., & Van de Schoot, R. (2017). Multilevel analysis: Techniques and applications. Routledge.
Littell, J. H., Corcoran, J., & Pillai, V. (2008). Systematic Reviews and Meta-Analysis. Oxford University Press.
Norusis, M. (2012). IBM SPSS Statistics 19 Advanced Statistical Procedures Companion. Pearson Education.
Norusis, M. (2012). IBM SPSS Statistics 19 Statistical Procedures Companion. Prentice Hall.
Peňa, D.: Análisis de datos multivariantes. McGraw-Hill, Madrid (2002)
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Pearson/Allyn & Bacon.
Venables, W. N. and Ripley, B. D.: Modern Applied Statistics with S. Springer-Verlag, New York(2002)
Thereneau, T.M. a Atkinson, E. J.: An Introdiction ro recursive Partitioning Using the RPARTRoutines. Mayo Foundation, (2011). Documentation to R-package.

Last update: Betinec Martin, Mgr., Ph.D. (05.02.2025)

Teaching methods -

Realization of the course in case of distant study

The course will be held in line with the schedule published on the web of the Dept. of Sociology
On-line platform : MS Teams (Teams Pokročilé statistické metody
https://teams.microsoft.com/l/team/19%3a983d34f761ec4d4391fd45c93d60b4b1%40thread.tacv2/conversations?groupId=f06fe134-108b-48a9-bfb4-694a5def8677&tenantId=71cbe59b-f59f-49d8-bed9-6de6b6468917)
Supporting materials: MS Teams
(https://teams.microsoft.com/_#/school/files/Obecn%C3%A9?threadId=19%3A983d34f761ec4d4391fd45c93d60b4b1%40thread.tacv2&ctx=channel&context=slajdy&rootfolder=%252Fsites%252Felearning-Pokroilstatistickmetody%252FSdilene%2520dokumenty%252FGeneral%252Fslajdy)
Course graduation requests: the same as under the regular conditions
Typo of exam: written form, might be on-line

Last update: Betinec Martin, Mgr., Ph.D. (05.02.2025)

Course assessment methods and requirements for successful completion, grading scheme -

Reaching at least 50% score in the final exam of written form is a necessary condition for passing the course as well as attendance on 3 workshops (at least).

There will be two exam dates given in May/June and one in September. Precise dates will be specified during the course.

in case of an epidemiology quaranteen, the exam might be proceed in a distant form.

The exam may be passed in the next year too.

Last update: Betinec Martin, Mgr., Ph.D. (05.02.2025)