Магистратура
2026/2027
Анализ категорийных переменных
ID 1194240
Статус:
Курс по выбору (Аналитика данных и прикладная статистика / Data Analytics and Social Statistics)
Где читается:
Факультет социальных наук
Когда читается:
1-й курс, 4 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
28
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
Assessment Elements
- Homework 1For Homework 1 the student needs to solve six problems from Agresti (2007). This homework accounts for 20% of the course performance.
- Homework 2For Homework 2 the student needs to conduct several analyses on two different datasets. This homework accounts for 20% of the course performance.
- Final ProjectFor the Final Project, the student works with the data from the real study which will be provided. They need to demonstrate everything they have learned in this course and in the program in general. Specifically, they need to come up with a well-rounded, cohesive, comprehensive analytical project on a research question of interest, with the use of (predominantly) categorical DA methods. The Final Project constitutes 60% of the final grade for the course.
Bibliography
Recommended Core Bibliography
- An introduction to categorical data analysis, Agresti, A., 2007
Recommended Additional 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