Master
2022/2023
Data Analysis in the Social Sciences
Type:
Compulsory course (Politics. Economics. Philosophy)
Area of studies:
Political Science
Delivered by:
School of Politics and Governance
Where:
Faculty of Social Sciences
When:
1 year, 2, 3 module
Mode of studies:
distance learning
Online hours:
20
Open to:
students of one campus
Master’s programme:
Политика. Экономика. Философия
Language:
English
ECTS credits:
6
Contact hours:
64
Course Syllabus
Abstract
The goal of this course is to introduce students to data analysis methods and procedures commonly used in social sciences. During the course, students will acquire practical skills to be able to gather, generate, visualize and analyze quantitative data in social science research. As an additional learning tool, students are encouraged to complete an online course on Datacamp: https://learn.datacamp.com/courses/free-introduction-to-r
Learning Objectives
- The main goal of the course is to familiarize students with a variety of data analysis methods which should be useful in quantitative research. The course is aimed at developing a datadriven mentality through understanding data fundamentals, as well as areas of application for different analytical methods and approaches. Students should be able to understand the limitations, value added and heuristic mechanisms of different data analysis methods.
Expected Learning Outcomes
- Be able to: Identify the best data sources Gather, generate, aggregate and visualize qualitative and quantitative data; Identify the correct method for a given research task; Apply acquired knowledge about research methods and techniques to their own works; Build their own research in accordance with given methodological requirements; Use the R package to analyze data
- Have: Experience with applying empirical methods to data Working skills of empirical research in social science
- Know: The general structure of research design; methodology and methods of empirical research; advantages and limitations of different analytical approaches; basics of statistical analysis.
Course Contents
- Data analysis: an introduction
- Data sources and databases
- Data visualization
- Random variables: an application of statistics to social science data
- Data Structure and Clustering
- Confidence intervals and hypothesis testing
- Statistical inference: correlation and crosstabulation
- Hidden data structure and Factor Analysis
- Regression models in social sciences
- Network analysis in social sciences: basic concepts
- Quantitative modelling in social sciences
- Text as data in social sciences
- Introduction to R
Interim Assessment
- 2022/2023 3rd module0.4 * Exam + 0.2 * Research project + 0.1 * Seminar activity + 0.1 * Tests
