Quantitative Methods of Political Research

2022/2023

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

Instructor

Semyonov, Andrey

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

• Learning the basic statistical skills necessary to conduct quantitative political study
• Developing the programming skills in the R software.
• Conducting quantitative analysis on the topic of student's choice.

Expected Learning Outcomes

• - Knows the basic concepts of statistical inference including probability theory, variable types, and distributions.
• - Knows about the core approaches to statistical inference (maximum likelihood, frequentist, and Bayesian) and its assumptions.
• - Is able to read and understand the papers using quantitative methods, assess the validity of the results and critically evaluate the findings.
• - Is able to produce their own quantitative research projects in accordance with replicability and transparency standards.
• - Is capable of using R to work with statistical tools (e.g. visualise the distributions, estimate the key quantities of interest, use simulations etc.).

Course Contents

• The Foundation of Statistical Inference
• Linear Regression
• Generalized Linear Models
• Time-Series Models
• Panel Models
• Event History and Survival Models
• Instrumental Variable Approach
• Regression Discontinuity Models
• Difference-in-Difference Design
• Replicability and Transparency

Assessment Elements

• Class attendance.
  Since the most important content of the course will be discussed in class and due to its technical nature, attendance and participation in class is extremely important. Valid excuses for absence should be filed no less than 24 hours before the class, otherwise points will be taken from the final grade. It constitutes 10% of the final grade.
• Problem sets.
  On selected seminars, problem sets (R scripts) will be distributed that should be solved individually. It constitute 20% of the final grade.
• Proposal
  Each student should design, present, and deliver the paper using quantitative methods discussed in the class. Research proposal will be delivered in the third module, while final presentation and paper — by the end of the fourth module. Research proposal and presentation comprise 10% of the final grade each, the final paper — 30%. The paper should be accompanied with R script tat allows the replicability of the main figures, tables, and results.
• Presentation
  Each student should design, present, and deliver the paper using quantitative methods discussed in the class. Research proposal will be delivered in the third module, while final presentation and paper — by the end of the fourth module. Research proposal and presentation comprise 10% of the final grade each, the final paper — 30%. The paper should be accompanied with R script tat allows the replicability of the main figures, tables, and results.
• Research paper
  Each student should design, present, and deliver the paper using quantitative methods discussed in the class. Research proposal will be delivered in the third module, while final presentation and paper — by the end of the fourth module. Research proposal and presentation comprise 10% of the final grade each, the final paper — 30%. The paper should be accompanied with R script tat allows the replicability of the main figures, tables, and results. Recommendations for research papers The presentations should be 10-12 minutes long and encompass the following elements: 1. Motivation (why this particular case was chosen) 2. Case location and necessary background information 3. Responses to the key study questions 4. Conclusion
• Replication.
  Each student will be assigned to replicate the existing study (the key graphs and estimates) and interpret the results. The replication task is 20% of the final grade.

Interim Assessment

• 2022/2023 4th module
  0.2 * Replication. + 0.2 * Problem sets. + 0.1 * Proposal + 0.1 * Presentation + 0.1 * Class attendance. + 0.3 * Research paper

Bibliography

Recommended Core Bibliography

• Regression and other stories, Gelman, A., 2021

Recommended Additional Bibliography

• The essentials of political analysis, Pollock III, P. H., 2016

Authors

• SEMENOV ANDREY VLADIMIROVICH