Categorial Data Analysis

  • Credits: 5
  • Ending: Examination
  • Range: 2P + 2C
  • Semester: winter
  • Year: 2
  • Faculty of Economic Informatics

Teachers

Included in study programs

Data Science in Economics

Teaching results

Successful completion of the course is a guarantee that students will gain a basic overview of the statistical methods for categorical data analysis in practice. Students will acquire the following:
abilities:
- knowledge of basic concepts, principles, methods and procedures used in categorical data analysis
- knowledge of analyses of the relationship between two variables
- knowledge of exact tests
skills:
Emphasis is placed on the application of the methods and the interpretation of results by applying the techniques to a variety of data sets.
competencies:
Students will apply knowledge especially in the socio-economic analysis and marketing.

Indicative content

Topics of this course include descriptive analysis of categorical data, analysis of contingency table data, tests for independence, comparing proportions, exact methods, and treatment of ordered data, statistical methods for analysing data where the outcome variable is categorical or discrete. The course will emphasize the theoretical underpinnings of the methods as well as an applied understanding of the computation and interpretation, both of which are necessary to succeed with real data analysis.

Support literature

1. ŘEZANKOVÁ, H. Analýza kategoriálnych dat. Praha: VŠE, 2005. ISBN 80-245-0926-1
2. RUBLÍKOVÁ, E. – LABUDOVÁ, V. – SANDTNEROVÁ, S. Analýza kategoriálnych údajov. Bratislava: EKONÓM, 2009.
3. ŘEZANKOVÁ, H. Analýza dat z dotazníkových šetření. Praha: Professional Publishing, 2010.
4. PECÁKOVÁ, I. Statistika v terénních průzkumech. Praha: Professional Publishing, 2011.
5. AGRESTI, A. An Introduction to Categorial Data Analysis. John and Wiley, 2019.
6. POWERS, D.A. Statistical Methods for Categorical Data Analysis. Emerald Publishing Limited, 2008.

Syllabus

1. Classification of categorical variables, scales of measurement, data coding. 2. Questionnaire survey. Scales of Measurement. 3. Frequency distribution and descriptive statistics. 4. Bernoulli and Binomial probability distribution. 5. Estimates of parameter π. 6. Tests of hypotheses concerning frequencies. 7. Contingency table. Tests of independency of two variables in the contingency tables. 8. Symmetric and asymmetric measures of contingency. 9. Association table. Tests of independency of two dichotomous variables (chi-square goodness-of-fit test, exact test). 10. Generalized Linear Model for binary data. Logistic Regression model. 11. Inference for logistic regression 12. Multiple logistic regression 13. Summary of the lectured subject matter

Requirements to complete the course

Seminars (40%):
Assignment (20 %)
Written essay (20 %)
60% final paper (20 % theoretical part, 40% practical – examples solution)

Student workload

Total study load (in hours): 130 hours
Distribution of study load
Lectures participation: 26 hours,
Seminar participation: 26 hours,
Preparation for seminars: 13 hours,
Written assignment: 13 hours,
Seminar essay preparation: 22 hours,
Final exam preparation: 30 hours

Language whose command is required to complete the course

Slovak

Date of approval: 10.02.2023

Date of the latest change: 03.02.2022