Using digital methods is a specific approach to doing digital social research. In digital methods, focus is placed on the digital media contexts where data is generated as a by-product of social interaction, and on new ways of combining quantitative and qualitative methods of digital inquiry and analysis. This course provides students with practical skills in implementing three sets of computer-assisted qualitative methods: exploratory network analysis, online ethnography, and content analysis. As such, it supplements the various quantitative techniques taught in other courses on the degree programme, and provides tools for mixing qualitative methods with textual and/or visual quantitative data into qualitative-quantitative social-science analyses. Students train these skills by conducting their own integrated mapping of a public issue, involving networks, ideas, and behaviour across individual and organizational levels and across multiple digital platforms.
Mandatory course on MSc programme in Social Data Science at University of Copenhagen. The course is only open for students enrolled in the MSc programme in Social Data Science.
At the end of the course, students are able to:
- 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 contexts of digital data and the broader questions, claims and biases of social data science.
- 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, using the appropriate qualitative method(s).
- Combine qualitative data with quantitative data sources, thereby integrating heterogeneous digital data formats into comprehensive social analyses.
- Evaluate and analyse 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, by integrating qualitative and quantitative sources of data as well as forms of narration and visualization.
Teaching combines lectures and in-class method exercises with extensive out-of-class project work. Throughout the course, students train their qualitative digital method skills by conducting their own project (with some teacher assistance available), i.e. digitally mapping a public issue chosen by themselves (or possibly suggested by the teachers). In-class exercises give priority to providing students first-hand skills in closely combining digital data formats into combined qualitative and quantitative social analyses that mirror realistic use scenarios in a range of contexts where social data analysis is a key component.
The syllabus consists mainly in relevant research articles pertaining to digital methods and the method skills and traditions covered. In addition, Robert V. Kozinets’ Netnography (Sage, 2019) is used as reader.
The syllabus altogether amounts to 600 pages. Of these, student groups self-select 40 pages of relevance to their chosen project theme (corresponding, standardly to 2 research articles).
Students can only register for the exam for Digital Methods if they have passed all compulsory courses on the first semester on the master's programme in social data science.
- 7,5 ECTS
- Type of assessment
Written assignmentOral examination, 40 mins. under invigilation
- Type of assessment details
- Group-based oral exam with a prior written assignment in
groups. The written assignment should contain a description of
method accounts, forms of analyses and formulate and evaluate an
explicit research question. In addition, it may contain
documentation in the shape of code, field-notes, data
visualizations, and so on, as relevant to the project.
The assessment is based on an overall assessment of the students’ ability to formulate and implement a coherent digital social research framework. Specifically, students are evaluated on their ability to give an account of the different parts of the assignment (research question, analysis etc.)
The total length of the written assignment must not exceed:
• For three students: 20 standard pages
• For four students: 25 standard pages
In the standard situation (a 3-person group), the oral part of the exam lasts 40 minutes in total (30 minutes of examination). With extra student in group, 5 minutes are added.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Criteria for exam assessment
The exam will be assessed on the basis of the learning outcome (knowledge, skills and competencies) for the course.
- Project work
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 4
- 70 students.
- Social Data Science
- Social Data Science
- Faculty of Social Sciences
- Anders Blok (3-70717b4f827e723d7a843d737a)
Are you BA- or KA-student?
Courseinformation of students