Quantitative Methods of Political Research

2022/2023

Учебный год

ENG

Обучение ведется на английском языке

5

Кредиты

Кто читает:

Департамент политологии и международных отношений

Статус:

Курс обязательный

Когда читается:

2-й курс, 3, 4 модуль

Преподаватель

Сорбалэ Алексей Борисович

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

• 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.

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

• Introduction to the discipline: basic concepts and R basics
• Descriptive statistics
• Data Visualization: principles, tools, examples
• Statistical hypotheses and errors. Comparison of samples
• Chi-squared (x2) statistics
• Correlation
• Paired linear regression: principle, interpretation, design
• Multiple OLS regression: principle, interpretation, design
• Technical problems and prerequisites for OLS regression
• Substantive problems of regression models
• Panel regression, random and fixed effects
• Binary logistic regression: principle, interpretation, design
• Ordinal logistic regression: principle, interpretation, design

Assessment Elements

• 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
• Project
• Exam
  The exam format is as follows. The student should prepare an R script reflecting the completion of the Exam tasks. The script should be workable, that is one should be able to get the desired result while using it. There are 4 blocks in the exam (4 broad questions). For each correctly completed task, the student receives a certain amount of points depending on the complexity of the task and the completeness of the answer to the question reflected in the R script and interpretation of the output. The exam is a closed format meaning that no preparatory materials are allowed, one should write a script from the scratch.
• 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.
• Bonus quizzes
  At the beginning of each seminar the Instructor conducts a short five-minute quiz covering the material of the seminar and the thematic lecture. In accordance with the results of the quiz, each student receives a certain number of points, which is determined by the speed and correctness of the answers to the quiz questions. The results of all quizzes are put in the open access rating. At the end of the course, each student receives a bonus point from 0 to 1, which is added to the course grade, calculated composed of the grades for the mandatory forms of control.

Interim Assessment

• 2022/2023 4th module
  0.29 * Test + 0.25 * Trainings + 0.2 * Exam + 0.26 * Project + 0 * Bonus quizzes

Bibliography

Recommended Core Bibliography

• Boso, À. (2006). KING, Gary; KEOHANE, Robert; VERBA, Sidney. Designing Social Inquiry: Scientific Inference in Qualitative Research. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrac&AN=edsrac.52780
• 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.

Recommended Additional 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
• 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
• 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