Research Seminar “Analytical Sociology and Big Data”
- 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.
- understand modern features and issues of big data analytics
- learn basic methodological principles and major methods applicable for big data analysis
- be able to apply the methods of analytical sociology and social statistics to the analysis of big data
- use basic rules of statistical inference
- employ major sociological concepts as instruments of sociological research
- read and discuss journal articles and book chapters; participate in group research projects; give presentations on their research projects and topics of their interest
- Introduction to analytical sociology and applicationsBasic principles of analytical sociology. Key authors in the field of analytical sociology
- Sources of big data; quality of dataTypology of data sources. Principles of data collection. Big data quality assessment
- Literature review: basic principles and search for the articlesBasic principles. Logic of literature review. Sources of literature
- Operationalization of theoretical concepts and measurementOperationalization. Measurement principles in sociology
- Research design for the big data analysisTypology of research designs. Most common research designs for big data researches
- Studying stratification and intergenerational mobility using big dataGeneral idea of social stratification analysis. Big data sources. Example article
- Social movements analysis using big dataGeneral idea of social movements analysis. Big data sources. Example article
- Educational research using big dataGeneral idea of education research. Big data sources. Example article
- Health research using big dataGeneral idea of health research. Big data sources. Example article
- Ethical issues of the big data researchGeneral ethical principles. Ethical issues in big data research
- Presentation of the research resultsGeneral principles of good presentation. Practical session
- Participation in class discussionsParticipation 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.
- In-class assignmentsIn-class assignments grade will be calculated as an average score for all types of written activities during the seminars.
- Presentation of the individual projectPresentation 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 (4 module)0.3 * In-class assignments + 0.4 * Participation in class discussions + 0.3 * Presentation of the individual project
- Van Rijmenam, M. (2014). Think Bigger : Developing a Successful Big Data Strategy for Your Business. New York: AMACOM. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=686831
- Manzo, G. (2014). Analytical Sociology : Actions and Networks. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=714658