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Methodology and Research Methods of Political Science

2019/2020
Учебный год
ENG
Обучение ведется на английском языке
6
Кредиты
Статус:
Курс обязательный
Когда читается:
1-й курс, 1-3 модуль

Преподаватель

Course 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

Learning Objectives

  • to provide a brief introduction to the methodology of quantitative political science research
Expected Learning Outcomes

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

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

Assessment Elements

  • non-blocking Home assignment
  • non-blocking Mid-term assignments 1
  • non-blocking Mid-term assignments 2
  • non-blocking 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

  • 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

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., Miles, J., 2012