Quantitative Methods of Political Research

2018/2019

Учебный год

ENG

Обучение ведется на английском языке

5

Кредиты

Кто читает:

Департамент политологии и международных отношений

Статус:

Курс по выбору

Когда читается:

2-й курс, 3, 4 модуль

Преподаватель

Сорбалэ Алексей Борисович

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

Bibliography

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
• 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

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
• 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