Quantitative Methods of Political Research

2023/2024

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

Department of Political Science and International Affairs

Course type:

Elective course

When:

1 year, 3, 4 module

Course Syllabus

Abstract

The course is designed for first-year MA students of the “Comparative Politics of Eurasia” program. The course introduces the main concepts and methods of quantitative political science and is a starting point for advanced courses in quantitative methods. It aims at teaching the students the foundations of statistical analysis and its application to various research problems in political science. It covers a variety of identification strategies (from descriptive statistics and pair correlations to time-series and panel data analysis). At the end of the course, the students will be able to use the methods of descriptive and inferential statistics in their research, as well as interpret the methods and research outputs from other quantitative works. It is expected that the students are proficient in arithmetic operations and are able to solve simple linear equations. The course relies on the use of RStudio, a software integrated with the R statistical environment, as a data analysis tool.

Learning Objectives

• Learning the basic statistical terms and concepts to understand and correctly interpret quantitative research.
• Learning the necessary skills of statistical analysis to conduct independent quantitative political research.
• Developing the programming skills in the R software necessary for the quantitative data analysis.
• Conducting individual quantitative research on the topic of the student’s choice.

Expected Learning Outcomes

• Able to fit a logistic regression model on a given dataset
• Able to run the regression model
• Able to interpret the linear regression’s coefficients
• Able to run the regression model with different types of variables
• Able to interpret the logistic regression’s coefficients
• Able to run regression models with interaction terms
• The student knows the main statistical terms and concepts.
• The student can download and install R and RStudio, and install the necessary packages.
• The student knows the basics of statistical inference, including probability theory, variable types, and distributions.
• The student is able to import data to R and is capable of using R to work with statistical tools (e.g., visualise the distributions, estimate the key quantities of interest, use simulations, etc.).
• The student knows the specifics of the panel data structure and is able to choose the correct method for the analysis. The student is able to apply the fixed- and random-effects models to analyze panel data, to interpret the results, and to have data visualization skills.
• The student is able to produce their own quantitative research project in accordance with replicability and transparency standards.

Course Contents

• Introduction to quantitative methods in PolSci. Main terms and concepts.
• Descriptive statistics. Data visualization.
• Introduction to inferential statistics.
• OLS regression: the principle, interpretation, and reporting.
• Sources of regression problems. Quality of model fit.
• Generalized linear models.
• Longitudinal data analysis.

Assessment Elements

• Class attendance and participation
• In-class task
• Final research paper

Interim Assessment

• 2023/2024 4th module
  0.1 * Class attendance and participation + 0.5 * Final research paper + 0.2 * In-class task + 0.2 * In-class task

Bibliography

Recommended Core Bibliography

• 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

• Field, A. V. (DE-588)128714581, (DE-627)378310763, (DE-576)186310501, aut. (2012). Discovering statistics using R Andy Field, Jeremy Miles, Zoë Field. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.363067604