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Regular version of the site

Quantitative Methods of Political Research

Academic Year
Instruction in English
ECTS credits
Course type:
Compulsory course
2 year, 3, 4 module


Course Syllabus


This course is an introduction to quantitative research methods in political science. By the end of this course, students should be able to effectively evaluate and analyze studies, which use quantitative methods of data collection and analysis; understand basic statistics and causality; and gain experience in collection, analysis, visualization and interpretation of quantitative data as part of an individual research project. No specific prerequisites are assumed for the class other than a basic understanding of algebra and ability to use a computer.
Learning Objectives

Learning Objectives

  • form the understanding of the cognitive abilities of quantitative methods of data analysis in political science research
  • promote knowledge and skills necessary for collecting quantitative data and its visualization; comparison of different data sets using statistical tests; study the relationships within quantitative data with the help of basic statistical tools
  • promote skills necessary to work with specialized statistical programs, in particular, with the statistical environment R
Expected Learning Outcomes

Expected Learning Outcomes

  • Performs regression analysis using R and interprets its results.
  • Applies the heuristic capabilities of statistical program R for data visualization.
  • Uses the heuristic capabilities of statistical program R for the data filtering, robustness checks and validation.
  • Uses specialized sources and databases to collect the relevant data for the quantitative research.
  • Presents the results of statistical analysis in a correct and understandible form.
Course Contents

Course Contents

  • Introduction to the discipline: basic concepts and R basics
  • Descriptive statistics
  • Data Visualization: Principles, Tools, Examples
  • Statistical hypotheses and errors
  • Statistics and chi square (x2)
  • Statistical tests: binominal, t test, Mann Whitney test
  • Correlation
  • Paired linear regression
  • Statistical tests: Mann Whitney test
  • Multiple OLS regression: principle, interpretation, design
  • “Technical” problems and prerequisites for OLS regression
  • Substantive problems of regression models
  • Logistic regression
  • OLS regression diagnostics
  • Ordered Logistic Regression (Overview). Course Summary
  • Panel regression and fixed effects
  • Hierarchical regression models
Assessment Elements

Assessment Elements

  • non-blocking Practical homework
  • non-blocking Test
    Test is carried out in the classroom in writing form. It consists of 4 parts. Part A: 10 multiple choice questions. Part B: 10 multiple selection questions. Part C: 5 tasks for graphs interpretation. Part D: 5 tasks for regression/test output interpretation
  • non-blocking Exam
    The exam is held in the classroom and is carried out in writing form. It consists of two broad questions covering the topics of the course. The students should use both theoretical and empirical knowledge on the respective statistical phenomena in their answers.
  • non-blocking Trainings
    Each week students should complete the training using R statistical software and provide the instructor with the training result in the form of an R script.
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.2 * Exam + 0.29 * Practical homework + 0.26 * Test + 0.25 * Trainings


Recommended Core Bibliography

  • Barbara Geddes. (1990). How the Cases You Choose Affect the Answers You Get: Selection Bias in Comparative Politics. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.633931A5
  • Crawley, M. J. (2011). Statistics : An Introduction Using R. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=415639
  • Crawley, M. J. (2014). Statistics : An Introduction Using R (Vol. Second edition). Chichester, West Sussex, UK: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=846213
  • Golosov, G. V., & Konstantinova, M. (2016). Gubernatorial Powers in Russia The Transformation of Regional Institutions Under the Centralizing Control of the Federal Authorities. Problems of Post-Communism, 63(4), 241–252. https://doi.org/10.1080/10758216.2016.1146906
  • Machler, M. (2007). Statistics: An Introduction using R, Michael J. Crawley. The American Statistician, 100. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.bes.amstat.v61y2007mfebruaryp100.101
  • Mann, T. E., & Wolfinger, R. E. (1980). Candidates and Parties in Congressional Elections. American Political Science Review, (03), 617. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.cup.apsrev.v74y1980i03p617.632.16
  • Robert I. Kabacoff. (2015). R in Action : Data Analysis and Graphics with R: Vol. Second edition. Manning.
  • Tabachnick, B. G., & Fidell, L. S. (2014). Using Multivariate Statistics: Pearson New International Edition (Vol. 6th ed). Harlow, Essex: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418064

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
  • Hoffman, M. aut. (2013). Religion in the Arab Spring between two competing narratives ; findings and conclusions Michael Hoffman and Amaney Jamal.