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Regular version of the site
Master 2019/2020

Data Analysis in the Social Sciences

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type: Compulsory course (Politics. Economics. Philosophy)
Area of studies: Political Science
Delivered by: Department of Higher Mathematics
When: 1 year, 2-4 module
Mode of studies: Full time
Instructors: Egor Yureskul
Master’s programme: Политика. Экономика. Философия
Language: English
ECTS credits: 6

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.
Learning Objectives

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

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
  • Know:  The general structure of research design;  methodology and methods of empirical research;  advantages and limitations of different analytical approaches;  basics of statistical analysis.
  • Have:  Experience with applying empirical methods to data  Working skills of empirical research in social science
Course Contents

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
  • Quantitative modelling in social sciences
  • Text as data in social sciences
Assessment Elements

Assessment Elements

  • non-blocking Seminar activity
  • non-blocking Tests
  • non-blocking Research project
  • non-blocking Exam
    The exam will be held in written form. On the day of the exam, students will receive the exam task over email and have a limited amount of time (5 hours) to complete it. The exam is in open book format, that is, the students are free to use any sources they wish. However, copying from other students or from previous work is strictly forbidden. The students are expected to turn in their complete tasks in the form of R scripts (*.r) with commentary. The students will require a working network connection to receive and turn in the task, and a working computer with Rstudio software installed to complete the task.
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.4 * Exam + 0.2 * Research project + 0.2 * Seminar activity + 0.2 * Tests
Bibliography

Bibliography

Recommended Core Bibliography

  • Qualitative inquiry & research design : choosing among five approaches, Creswell J. W., 2013

Recommended Additional Bibliography

  • Field experiments : design, analysis, and interpretation, Gerber A. S., Green D. P., 2012
  • Политология. Методы исследования, Мангейм Д. Б., Рич Р. К., 1999
  • Стратегия социологического исследования : описание, объяснение, понимание социальной реальности: учеб. пособие, Ядов В. А., 2012