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Магистерская программа «Прикладная статистика с методами сетевого анализа»

Categorical Data Analysis

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты
Статус:
Курс по выбору
Когда читается:
1-й курс, 3 модуль

Преподаватель


Кускова Валентина Викторовна

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

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

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

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

Assessment Elements

  • non-blocking Take-home project
  • non-blocking Homework Assignments (5 x Varied points)
  • non-blocking In-Class Labs (9-10 x Varied points)
Interim Assessment

Interim Assessment

  • 2021/2022 3rd module
    0.3 * Homework Assignments (5 x Varied points) + 0.2 * In-Class Labs (9-10 x Varied points) + 0.5 * Take-home project
Bibliography

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
  • 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
  • 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

  • Fienberg, S. E. (2007). The Analysis of Cross-Classified Categorical Data (Vol. 2nd ed). New York, NY: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=212853