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Regular version of the site

Quantitative Methods of Political Research

2020/2021
Academic Year
ENG
Instruction in English
5
ECTS credits
Course type:
Compulsory course
When:
2 year, 3, 4 module

Instructor

Course Syllabus

Abstract

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

  • Understands the functions of descriptive statistics in a study with quantitative design.
  • Able to apply the heuristic capabilities of the statistical program R to obtain descriptive statistics.
  • Understands the role of visualization in a study with quantitative design.
  • Able to apply the heuristic capabilities of statistical program R for data visualization.
  • Understands the types and meaning of statistical hypotheses and errors.
  • Able to apply the heuristic capabilities of the statistical program R to test statistical hypotheses and the presence of statistical errors.
  • Understands the meaning of chi-square (X2) in a study with quantitative design.
  • Able to apply the heuristic capabilities of the statistical program R to calculate the chi-square (X2).
  • Understands the significance of statistical tests in a study with quantitative design.
  • Understands the essential differences between statistical tests.
  • Able to apply the heuristic capabilities of the statistical program R for statistical tests.
  • Understands the significance of the Mann-Whitney test in a study with quantitative design.
  • Understands the essential differences between the Mann-Whitney test and other statistical tests.
  • Able to apply the heuristic capabilities of the statistical program R for the Mann-Whitney test.
  • Understands the meaning and function of correlation in research with quantitative design.
  • Able to apply the heuristic capabilities of the statistical program R to calculate the correlation coefficient.
  • Understands the role of paired linear regression in a study with quantitative design.
  • Able to apply the heuristic capabilities of the statistical program R for paired linear regression.
  • Understands the importance of OLS regression in a study with quantitative design.
  • Understands the principles of OLS regression.
  • Able to apply the heuristic capabilities of the statistical program R for OLS regression.
  • Understands the importance of conducting OLS regression models.
  • Understands the essence of technical problems and preconditions for conducting OLS regression.
  • Able to apply the heuristic capabilities of statistical program R to check the OLS regression for technical problems.
  • Able to apply the heuristic capabilities of the statistical program R for the diagnosis of OLS regression models.
  • Understands the essence of the substantive problems of OLS regression.
  • Able to apply the heuristic capabilities of the statistical program R to check OLS regression for substantive problems.
  • Understands the importance of logistic regression for a study with quantitative design.
  • Able to apply the heuristic capabilities of the statistical program R for conducting logistic regression.
  • Understands the importance of ordinal logistic regression for a study with quantitative design.
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
  • Statistical tests: Mann Whitney test
  • Correlation
  • Paired linear regression
  • Multiple OLS regression: principle, interpretation, design
  • OLS regression diagnostics
  • “Technical” problems and prerequisites for OLS regression
  • Substantive problems of regression models
  • Panel regression and fixed effects
  • Hierarchical regression models
  • Logistic regression
  • Ordered Logistic Regression (Overview). Course Summary
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
Bibliography

Bibliography

Recommended Core Bibliography

  • 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
  • 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