• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Quantitative Methods of Political Research

2019/2020
Academic Year
ENG
Instruction in English
5
ECTS credits
Course type:
Elective course
When:
2 year, 3, 4 module

Instructor

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

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 structure of the course and forms of control, basic terms and concepts of statistics
  • Understands the basic functions and logic of work in the statistical program R
  • 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 essence of the least squares methods and the scope of its application.
  • Able to apply the heuristic capabilities of the statistical program R to use the least squares method.
  • 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 basics of statistics
  • R basics
  • Correlation
  • Data Visualization: Principles, Tools, Examples
  • Descriptive statistics
  • Least squares method
  • Logistic regression
  • Statistical tests: Mann Whitney test
  • Multiple OLS regression: principle, interpretation, design
  • OLS regression diagnostics
  • Ordered Logistic Regression (Overview). Course Summary
  • Paired linear regression
  • Statistical hypotheses and errors
  • Statistical tests: binominal, t test, Mann Whitney test
  • Statistics and chi square (x2)
  • Substantive problems of regression models
  • “Technical” problems and prerequisites for OLS regression
Assessment Elements

Assessment Elements

  • non-blocking Seminar participation
  • non-blocking Practical homework
  • non-blocking Test
  • non-blocking Exam
    Экзамен проводится в устной форме. Экзамен проводится на платформе Zoom (https://zoom.us/). К экзамену необходимо подключиться за 10 минут до начала. На платформе Zoom предусмотрен тестирование системы. Компьютер студента должен удовлетворять требованиям: работающая камера и микрофон. Для участия в экзамене студент обязан: включить камеру и микрофон, подтвердить личность. Во время экзамена студентам запрещено: общаться (в социальных сетях, с людьми в комнате), списывать, использовать дополнительные девайсы и средства связи, кроме компьютера. Во время экзамена студентам разрешено: пользоваться собственными письменными конспектами (в тетради или на распечатанных листах). Кратковременным нарушением связи во время экзамена считается прерывание связи до 3 минут. Долговременным нарушением связи во время экзамена считается прерывание связи на 3 минуты и более. При долговременном нарушении связи студент не может продолжить участие в экзамене. Процедура пересдачи аналогична процедуре сдачи.
Interim Assessment

Interim Assessment

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