Магистратура
2021/2022
Анализ категорийных переменных
Статус:
Курс по выбору (Прикладная статистика с методами сетевого анализа)
Направление:
01.04.02. Прикладная математика и информатика
Где читается:
Международная лаборатория прикладного сетевого анализа
Когда читается:
1-й курс, 3 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Кускова Валентина Викторовна
Прогр. обучения:
Прикладная статистика с методами сетевого анализа
Язык:
английский
Кредиты:
4
Контактные часы:
40
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
- Take-home project
- Homework Assignments (5 x Varied points)
- In-Class Labs (9-10 x Varied points)
Interim Assessment
- 2021/2022 3rd module0.5 * Take-home project + 0.2 * In-Class Labs (9-10 x Varied points) + 0.3 * Homework Assignments (5 x Varied points)
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