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Research Seminar “Analytical Sociology and Big Data”

2019/2020
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты
Статус:
Курс по выбору
Когда читается:
2-й курс, 1-4 модуль

Преподаватели

Course Syllabus

Abstract

The purpose of the course is to provide students with skills necessary for conducting social research based on big data analysis. During the course different features of analytical approach towards big data will be covered as well as a variety of examples of reports and articles relevant for the field.
Learning Objectives

Learning Objectives

  • be able to read and critically discuss articles from the field of the big data analysis and conduct empirical research using different sources of the data.
Course Contents

Course Contents

  • Introduction to analytical sociology and applications
    Basic principles of analytical sociology. Key authors in the field of analytical sociology
  • Sources of big data; quality of data
    Typology of data sources. Principles of data collection. Big data quality assessment
  • Literature review: basic principles and search for the articles
    Basic principles. Logic of literature review. Sources of literature
  • Operationalization of theoretical concepts and measurement
    Operationalization. Measurement principles in sociology
  • Research design for the big data analysis
    Typology of research designs. Most common research designs for big data researches
  • Studying stratification and intergenerational mobility using big data
    General idea of social stratification analysis. Big data sources. Example article
  • Social movements analysis using big data
    General idea of social movements analysis. Big data sources. Example article
  • Educational research using big data
    General idea of education research. Big data sources. Example article
  • Health research using big data
    General idea of health research. Big data sources. Example article
  • Ethical issues of the big data research
    General ethical principles. Ethical issues in big data research
  • Presentation of the research results
    General principles of good presentation. Practical session
Assessment Elements

Assessment Elements

  • non-blocking Participation in class discussions
    Participation in class discussion is evaluated by instructors after each seminar and is based student’s contribution in a discussion during the class. Answers to instructor questions, valid examples and thought-provoking questions may be considered as three main forms of contribution to discussion. After each seminar students will receive a raw score which will be standardized into 10-points scale at the end of the course.
  • non-blocking In-class assignments
    In-class assignments grade will be calculated as an average score for all types of written activities during the seminars.
  • non-blocking Presentation of the individual project
    Presentation of the individual project includes final presentation on the topic of student’s course work and should represent a solid presentation of research framework, literature review, data description, data analysis and main conclusions.
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.3 * In-class assignments + 0.4 * Participation in class discussions + 0.3 * Presentation of the individual project