ASDK20007U Digital Methods
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.
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, digital ethnography, and content analysis. As such, it supplements the various quantitative techniques taught in other courses on the program, as well as provides tools for mixing qualitative methods with textual and/or visual quantitative data into quali-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.
Knowledge
- Show familiarity with the basic techniques, use scenarios, and validity criteria of computer-assisted qualitative methods, i.e. digital ethnography, content analysis, and exploratory network analysis.
- Account for the procedures, potentials, and pitfalls of combining qualitative and quantitative data sources, including in integrated quali-quantitative ways.
- 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.
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 patterns of networks, ideas, and behaviour characteristic of specific social settings and public issues, using the appropriate qualitative method(s).
- Combine qualitative data with a quantitative data source, thereby integrating heterogeneous digital data formats into comprehensive social analyses.
Competencies
- Evaluate and analyse a social data problem from both qualitative and quantitative perspectives, including determining when to deploy which method designs.
- Design and implement small-scale digital ethnography campaigns, along with exploratory network analysis and content analysis, to obtain insights into social networks, ideas, and behaviour at individual and organizational levels.
- Combine qualitative and quantitative sources of data, as well as forms of narration and visualization, into persuasive quali-quantitative reports on social data problems for a range of organizational use scenarios.
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 must follow Digital Methods concurrently with the Advanced Social Data Science II course on the master's programme in social data science.
- Category
- Hours
- Lectures
- 28
- Preparation
- 60
- Exercises
- 35
- Project work
- 63
- Exam
- 20
- Total
- 206
- Credit
- 7,5 ECTS
- Type of assessment
- Written assignment, The examination may be conducted online in case of governmental or university COVID-19 restrictions.Oral examination, 40 mins. under invigilationGroup-based oral exam with prior written assignment (in the shape of a wiki) in groups of 3-4 students. The group-based wiki should contain text (method accounts, analyses etc.), 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 exam is integrated with Advanced Social Data Science II, with separate assessment and grading.
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. - Exam registration requirements
To be eligible for exam, students must have completed and passed four project-related assignments. The assignments can be submitted individually or in groups of 3 or 4 students and must be approved by the instructor. The length of each assignment must be no longer than 3 standard pages.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Re-exam
The second and third exam attempts will be based solely on a written assignment with a new problem statement, approved by the teacher. It will be possible to do the re-examination individually or in groups of 3 or 4 students.
If done individually, the total length of the re-exam must be no longer than 10 standard pages.
If the student passes either the ASDSII or the Digital Methods course, the re-examination pertains only to the course not passed.
Criteria for exam assesment
The exam will be assessed on the basis of the learning outcome (knowledge, skills and competencies) for the course.
Course information
- Language
- English
- Course code
- ASDK20007U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Spring And Block 4
- Course capacity
- 70 students.
- Study board
- Social Data Science
Contracting department
- Social Data Science
Contracting faculty
- Faculty of Social Sciences
Course Coordinators
- Anders Blok (3-696a74487b776b36737d366c73)