Quantitative Methods of Political Research

2024/2025

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Political Science and International Affairs

Course type:

Elective course

When:

1 year, 3 module

Instructor

Zubarev, Nikita

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

• Homeworks
  Students complete assignments where they have to analyze datasets assigned by the teacher.
• Report based on given data

Interim Assessment

• 2024/2025 3rd module
  0.6 * Homeworks + 0.4 * 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.

Authors

• Zubarev Nikita Sergeevich