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Бакалавриат 2021/2022

# Количественные методы в политических исследованиях

Лучший по критерию «Новизна полученных знаний»
Статус: Курс обязательный (Политология и мировая политика)
Направление: 41.03.04. Политология
Когда читается: 2-й курс, 3, 4 модуль
Формат изучения: без онлайн-курса
Охват аудитории: для своего кампуса
Язык: английский
Кредиты: 5
Контактные часы: 68

### 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

• 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

• 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

• Descriptive statistics
• 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
• Logistic regression
• Ordered Logistic Regression (Overview). Course Summary
• Panel regression and fixed effects
• Hierarchical regression models

#### Assessment Elements

• Project
• 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
• 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.
• 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.

#### Interim Assessment

• 2021/2022 4th module
0.2 * Exam + 0.26 * Test + 0.29 * Project + 0.25 * Trainings