Data Analysis Tools for Qualitative Research in Social Sciences

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Course Description: 

The digital turn in the past few decades caused dramatic changes in qualitative research in social sciences. Scholars in sociology, anthropology, cultural studies, ethnology, political science, history, and cultural heritage studies study patterns of social behavior in the online realm as a specific focus or combined with sources from the offline sphere. This new direction of research – sometimes called digital anthropology or cyberethnography – with a new type of data sources led to the development of new analytical approaches, methods of data collection and analysis, and software, which has not been part of the curricula of educational programs in the above-mentioned fields.

This course provides a comprehensive understanding of how to collect/generate, manage, analyze, and visualize data in qualitative research. Course participants will learn about the contemporary analytical tools and software applied in social sciences through hands-on experience. The focus will be on practices and mechanisms of data crawling and processing them with specialized software. The course instructors and invited experts will demonstrate the advantages and limitations of some most popular tools on concrete examples. The course aims to provide new skills to analyze data with powerful tools but will approach these tools critically.

The course addresses the three challenges which students are embracing qualitative research face: 1) the increasing role of sources of qualitative data is on the internet (especially social media); 2) the rising number of new sophisticated techniques and software in qualitative data analysis; and 3) the increasing significance of the multidisciplinary collaborative projects and demand for respective skills in qualitative research and mix-method research designs. 

The course consists of three modules. The first module provides a detailed overview of qualitative methods of data collection. The course participants will get an overview of each method and explore how to plan for data collection, including developing data collection guides and discussing techniques for managing data collection. The second module is a crash course on doing content and discourse analysis in the MAXQDA program and relevant web scrapping plugins. The third module includes several methodological case studies focusing on certain methods and software to illustrate the methodological issues raised through the course.

Learning Outcomes: 

The students completing the course should be able to:

  • Apply the most popular methods and techniques of qualitative data collection, management, and analysis in their research;
  • Select adequate software tools to process their data efficiently;
  • Process and visualize qualitative data;
  • Be able to connect theoretical approaches with methodological practices and empirical cases.

  • Pre-class collaborative reading assignments (on Perusall): 20%
  • Active participation in synchronous online classes: 20%
  • QDA lab: 60%
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