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Regular version of the site

Research Seminar "Quantitative Methods in Political Research"

2021/2022
Academic Year
ENG
Instruction in English
6
ECTS credits
Course type:
Elective course
When:
1 year, 3, 4 module

Course Syllabus

Abstract

This research seminar offers an overview of the key quantitative methods used in contemporary political science and helps students to master their use for their own research. It considers 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 acquaint students with statistical methods and terminology
  • To teach students how to implement statistical methods using R programming language.
  • To train students to independently develop a program and tools for conducting quantitative research
Expected Learning Outcomes

Expected Learning Outcomes

  • chooses statistical methods appropriate to his substantive research problem
  • designs a quantitative political study
  • reads (and understands) most academic PS articles
  • speaks the language of data fluently
  • uses R programming language for statistical computations
Course Contents

Course Contents

  • Design types, data types, and data summarization
  • Basic Statistical Concepts
  • Exploratory Data Analysis and Visualization
  • Inference and Hypothesis Testing
  • Simple Regression Methods
  • Confounding and Effect Modification (Interaction)
  • Multiple Regression Methods
  • Generalized Linear Models 1
  • Generalized Linear Models 2
Assessment Elements

Assessment Elements

  • non-blocking Homework (1-5)
    The most important aspects of assignments that affect grades are following: a) correctness of answers to questions given in an assignment, b) ability to write R code correctly (if necessary), c) appropriate use of statistical language, d) correctness of results’ interpretations. If all these criterions are met, you can expect an excellent grade (8-10 on 0-10 scale). Late assignments will be graded down by 1 point for each day of delay (but no more than 3 points in total). Plagiarism is prohibited.
  • non-blocking In-class Participation
  • non-blocking Midterm paper
    The most important aspects of the paper to be graded are: 1) logical reasoning, 2) correctness and efficiency of R code written, 3) accuracy of statistical methods and models used, 4) correctness and creativity of results’ interpretation, 5) fluency and accuracy of statistical terminology used.
  • non-blocking Final paper
    The most important aspects of the paper to be graded are: 1) logical reasoning, 2) correctness and efficiency of R code written, 3) accuracy of statistical methods and models used, 4) correctness and creativity of results’ interpretation, 5) fluency and accuracy of statistical terminology used.
Interim Assessment

Interim Assessment

  • 2021/2022 4th module
    0.25 * Midterm paper + 0.25 * Homework (1-5) + 0.2 * In-class Participation + 0.3 * Final paper
Bibliography

Bibliography

Recommended Core Bibliography

  • Wilcox, R. R. (2016). Understanding and Applying Basic Statistical Methods Using R. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1237377

Recommended Additional Bibliography

  • Val Todd. (2017). Field and Iles (2016) An Adventure in Statistics: The Reality Enigma. PRISM, 1(1), 195–199. https://doi.org/10.24377/LJMU.prism.vol1iss1article304
  • 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