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Regular version of the site
Bachelor 2021/2022

Quantitative Methods of Political Research

Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (Political Science and World Politics)
Area of studies: Political Science
When: 2 year, 3, 4 module
Mode of studies: offline
Open to: students of one campus
Instructors: Aleksei Sorbale
Language: English
ECTS credits: 5
Contact hours: 68

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

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

Course Contents

  • 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
  • Multiple OLS regression: principle, interpretation, design
  • “Technical” problems and prerequisites for OLS regression
  • Substantive problems of regression models
  • Logistic regression
  • Ordered Logistic Regression (Overview). Course Summary
  • Panel regression and fixed effects
  • Hierarchical regression models
Assessment Elements

Assessment Elements

  • non-blocking Project
  • 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 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.
  • 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.
Interim Assessment

Interim Assessment

  • 2021/2022 4th module
    0.2 * Exam + 0.26 * Test + 0.29 * Project + 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
  • 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
  • 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

  • Chinn, S. (1997). Statistics: Principles and Methods, 3rd edition (1996). Richard A. Johnson and Gouri Bhattacharyya. John Wiley & Sons, Inc., New York. Price: {pound}21.50. ISBN: 0-471-04194-7. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.1875E2BE
  • Hoffman, M., & Jamal, A. (2014). Religion in the Arab Spring: Between Two Competing Narratives. Journal of Politics, 76(3), 593–606. https://doi.org/10.1017/S0022381614000152
  • King, G. (DE-588)135604311, (DE-576)166299405. (1994). Designing social inquiry : scientific inference in qualitative research / Gary King; Robert O. Keohane; Sidney Verba. Princeton, NJ: Princeton Univ. Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.039730549