Магистратура
2024/2025
Количественные методы в политических исследованиях
Статус:
Курс по выбору (Сравнительная политика Евразии)
Направление:
41.04.04. Политология
Где читается:
Санкт-Петербургская школа социальных наук
Когда читается:
1-й курс, 3 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Зубарев Никита Сергеевич
Прогр. обучения:
Сравнительная политика Евразии
Язык:
английский
Кредиты:
3
Course Syllabus
Abstract
The course is designed for the first-year MA "Comparative Politics of Eurasia" students introducing basic and more advanced concepts and methods of quantitative political science. It aims at familiarising the students with nuts and bolts of statistical analysis and its application to various research problems in political science. It covers a variety of identification strategies (from linear regression to time-series) with examples from existing scholarship. The course requires a modest degree of prior familiarity with the qualitative methods and statistics.
Learning Objectives
- Students are able to perform statistical analysis of data and solve research and parctical problems using various modeling techniques.
Expected Learning Outcomes
- chooses statistical methods appropriate to his substantive research problem
- uses R programming language for statistical computations
Course Contents
- Basic Statistical Concepts
- Exploratory Data Analysis and Visualization
- Simple Regression Methods
- Multiple Regression Methods
- Generalized Linear Models 1
- Generalized Linear Models 2
Assessment Elements
- HomeworksStudents complete assignments where they have to analyze datasets assigned by the teacher.
- Report based on given data
Bibliography
Recommended Core Bibliography
- Discovering statistics using R, Field, A., 2012
- Wickham, H., & Grolemund, G. (2016). R for Data Science : Import, Tidy, Transform, Visualize, and Model Data (Vol. First edition). Sebastopol, CA: Reilly - O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1440131
Recommended Additional Bibliography
- An R companion to applied regression, Fox, J., 2019
- Wickham H. ggplot2: elegant graphics for data analysis. Second edition. Cham: Springer, 2016. 260 p.