Master
2024/2025
Categorical Data Analysis
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