Методология и методы политических исследований

Магистратура 2019/2020

Статус: Курс обязательный (Сравнительная политика Евразии)

Направление: 41.04.04. Политология

Кто читает: Департамент социологии

Где читается: Санкт-Петербургская школа социальных наук

Когда читается: 1-й курс, 1-3 модуль

Формат изучения: без онлайн-курса

Преподаватели: Соколов Борис Олегович

Прогр. обучения: Сравнительная политика Евразии

Язык: английский

Кредиты: 6

Контактные часы: 60

Full Syllabus

Abstract

This course offers an overview of the key quantitative methods used in contemporary political science. The course begins with the introduction to the basic principles of political inquiry. Then we consider the basic concepts of statistics and probability. We also discuss such topics as exploratory data analysis and data visualization, statistical hypothesis testing, linear regression models, and regression diagnostics, generalized linear models, and the potential outcomes framework for causal inference. R programming language is used as a primary tool for data processing and statistical computations. Students are assumed to be familiar with high school math program, have basic computer literacy and be willing to work hard to learn the essentials of data analysis.

Learning Objectives

to provide a brief introduction to the methodology of quantitative political science research

Expected Learning Outcomes

Able to develop a design for academic and applied research, including collective one, with the use of modern political science methodology

Course Contents

Introduction to Quantitative Study Designs and Statistics
Introduction to R
Data management using R
Bivariate statistical tests
Bivariate statistical tests – 2
Linear regression – 1: Basics
Linear regression – 2: Model diagnostics
Linear regression – 3: Interaction effects
Linear regression – 4: Panel data analysis
Generalized linear models: Logistic regression and Poisson regression
Generalized linear models: Ordered logistic regression and multinomial regression.
Introduction to the potential outcomes theory
Instrumental variables and difference-in-differences estimators
Matching and regression discontinuity estimation

Assessment Elements

Home assignment
Mid-term assignments 1
Mid-term assignments 2
Final exam
экзамен состоялся в марте 2020 года. Final exam is a written assignment in which you are given a real-world data set and then asked to perform statistical analysis

Interim Assessment

Interim assessment (3 module)
0.3 * Final exam + 0.3 * Home assignment + 0.2 * Mid-term assignments 1 + 0.2 * Mid-term assignments 2

Bibliography

Recommended Core Bibliography

An adventure in statistics: The reality enigma, Field, A., 2016
Denis, D. J. (2016). Applied Univariate, Bivariate, and Multivariate Statistics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1091881

Recommended Additional Bibliography

Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=941245
Discovering statistics using R, Field, A., 2012

Course Syllabus