• A
• A
• A
• АБВ
• АБВ
• АБВ
• А
• А
• А
• А
• А
Обычная версия сайта

# Quantitative Methods of Political Research

2023/2024
Учебный год
ENG
Обучение ведется на английском языке
5
Кредиты
Статус:
Курс обязательный
Когда читается:
2-й курс, 3, 4 модуль

### 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 I
Test I is carried out in the classroom in writing form at the end of the first study module. 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 test output interpretation
• Test II
Test II is carried out in the classroom in writing form at the second module of study. 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 output interpretation
• Project
Project is the final form of control, where students should demonstrate their data collection, data filtration, data visualization, data analysis and interpretation skills
• 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.

#### Interim Assessment

• 2023/2024 4th module
0.2 * Exam + 0.29 * Project + 0.13 * Test I + 0.13 * Test II + 0.25 * Trainings

#### 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.