Master
2024/2025
Categorical Data Analysis
Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type:
Elective course (Data Analytics and Social Statistics)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
International Laboratory for Applied Network Research
Where:
Faculty of Social Sciences
When:
1 year, 4 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Irina Pavlova
Master’s programme:
Аналитика данных и прикладная статистика
Language:
English
ECTS credits:
3
Course Syllabus
Abstract
This course is designed to introduce basic concepts and common statistical models and analyses for categorical data; to provide enough theory, examples of applications in a variety of disciplines (especially in social and behavioral science); and practice using categorical techniques and computer software so that students can use these methods in their own research; to attain knowledge necessary to critically read research papers that use such methods.
Learning Objectives
- The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.
Expected Learning Outcomes
- Be able to work with major linear modeling programs, especially SAS, so that they can use them and interpret their output.
- Have the skill to work with statistical software, required to analyze the data.
- Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
- Have the skill to meaningfully develop an appropriate model for the research question.
- Be able to interpret the results of models with non-linear outcomes.
- Be able to to criticize constructively and determine existing issues with applied linear models in published work .
- Have an understanding of the basic principles of binary models and lay the foundation for future learning in the area.
- Know the approaches to building the binary logit and probit models.
- Know the foundation of multinomial logit models.
- Know the most fundamental regression models for binary, ordinal, nominal and count outcomes.
Course Contents
- Introduction to Categorical data analysis
- Contingency tables
- Generalized linear models
- Logistic regression
- Loglinear models for contingency tables
- Models for matched pairs
- Random effects – GL Mixed Models
Bibliography
Recommended Core Bibliography
- Agresti, A. (2013). Categorical Data Analysis (Vol. Third edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=769330
- Sutradhar, B. C. (2014). Longitudinal Categorical Data Analysis. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=881131
- Upton, G. J. G. (2016). Categorical Data Analysis by Example. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1402878
Recommended Additional Bibliography
- Ark, L. A. van der, Croon, M. A., & Sijtsma, K. (2005). New Developments in Categorical Data Analysis for the Social and Behavioral Sciences. Mahwah, N.J.: Psychology Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=125950
- Categorical data analysis, Agresti, A., 2002