Categorical Data Analysis

2024/2025

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Международная лаборатория прикладного сетевого анализа

Статус:

Курс по выбору

Когда читается:

1-й курс, 4 модуль

Преподаватель

Павлова Ирина Анатольевна

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

Assessment Elements

• Homework Assignment
• Final project

Interim Assessment

• 2024/2025 4th module
  0.6 * Final project + 0.4 * Homework Assignment

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

Authors

• Pavlova Irina Anatolevna