• A
• A
• A
• ABC
• ABC
• ABC
• А
• А
• А
• А
• А
Regular version of the site
Bachelor 2020/2021

## Quantitative Methods of Political Research

Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (Political Science and World Politics)
Area of studies: Political Science
When: 2 year, 3, 4 module
Mode of studies: offline
Instructors: Aleksei Sorbale
Language: English
ECTS credits: 5

### 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

• Performs regression analysis using R and interprets its results.
• Applies the heuristic capabilities of statistical program R for data visualization.
• Uses the heuristic capabilities of statistical program R for the data filtering, robustness checks and validation.
• Uses specialized sources and databases to collect the relevant data for the quantitative research.
• Presents the results of statistical analysis in a correct and understandible form. #### Course Contents

• Introduction to the discipline: basic concepts and R basics
• Descriptive statistics
• Data Visualization: Principles, Tools, Examples
• Statistical hypotheses and errors
• Statistics and chi square (x2)
• Statistical tests: binominal, t test, Mann Whitney test
• Statistical tests: Mann Whitney test
• Correlation
• Paired linear regression
• Multiple OLS regression: principle, interpretation, design
• OLS regression diagnostics
• “Technical” problems and prerequisites for OLS regression
• Substantive problems of regression models
• Panel regression and fixed effects
• Hierarchical regression models
• Logistic regression
• Ordered Logistic Regression (Overview). Course Summary #### Assessment Elements

• Practical homework
• 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
• 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.
• 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. #### Interim Assessment

• Interim assessment (4 module)
0.2 * Exam + 0.29 * Practical homework + 0.26 * Test + 0.25 * Trainings 