Digital Methods

After completing the course, the student is expected to be able to:

Knowledge:

•  Show familiarity with the basic techniques, use scenarios, and validity criteria of computer-assisted qualitative methods, i.e. online ethnography, content analysis, and exploratory network analysis.
• Account for the procedures, potentials, and pitfalls of combining and integrating qualitative and quantitative data sources.
• Account for the relationship between digital methods’ emphasis on the media context of digital data and the broader questions, claims and biases of social data science.

Skills:

• Identify the procedures of qualitative content analysis for designing appropriate semantic categories, including for use in subsequent machine learning with quantitative text (and/or visual) data.
• Extract and communicate network patterns, ideas, and behaviour characteristics of specific social settings and public issues, usind the appropriate qualitative method(s).
• Combine qualitative data with quantitative data sources, thereby integrating heterogeneous digital data formats into comprehensive social analyses.

Competences:

• Evaluate and analyze a social data problem from both qualitative and quantitative perspectives, including determining when to deploy specific method designs taught on the course.
• Design and implement small-scale online ethnography campaigns, along with exploratory network analysis and content analysis, to obtain insights into social networks, ideas, and behaviour at individual and organizational levels.
• Produce persuasive qualitative-quantitative reports on social data problems for a range of organizational use scenarios, bu integrating qualitative and quantitative sources of data as well as forms of narration and visualization.