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# Quantitative Methods of Political Research

2018/2019
ENG
Instruction in English
5
ECTS credits
Course type:
Elective course
When:
2 year, 3, 4 module

### Course Syllabus

#### Abstract

This discipline refers to the professional cycle, the basic part of the profile. The study of this discipline is based on the following disciplines: Mathematics and Statistics, Comparative Politics, Research Seminar (first and second years). The main provisions of the discipline can be used in the preparation of term papers and BA diplomas. As a result of mastering the course, students will get an idea of ​​the heuristic abilities of quantitative methods of daat analysis in political studies; increase the skills necessary for collecting quantitative data and visualizing them, comparing various samples using statistical tests, studying quantitative data with basic statistical tools; gain the knowledge necessary to work with specialized statistical programs, in particular, with the statistical environment R.

#### 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

• Understands the structure of the course and forms of control, basic terms and concepts of statistics
• 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.
• Performs regression analysis using R and interprets its results.
• Applies 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 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.
• Able to apply the heuristic capabilities of the statistical program R to conduct ordinal logistic regression.

#### Course Contents

• Descriptive statistics
• Introduction to the discipline: basic concepts and R basics
• 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
• OLS regression diagnostics
• Logistic regression
• Ordered Logistic Regression (Overview). Course Summary

#### Assessment Elements

• Seminar participation
• Practical homework
• Test
• Exam

#### Interim Assessment

• Interim assessment (4 module)
0.3 * Exam + 0.2 * Practical homework + 0.2 * Seminar participation + 0.3 * Test

#### Recommended Core Bibliography

• 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
• 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
• 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

• 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
• 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
• Rasch, D., Verdooren, L. R., & Pilz, J. (2019). Applied Statistics : Theory and Problem Solutions with R. Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2218318