Digital Methods

Course content

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.

Education

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.

Learning outcome

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.

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).

Written
Oral
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
ECTS
7,5 ECTS
Type of assessment
Home assignment
Oral examination, 40 mins. under invigilation
Type of assessment details
Group.

The home assignment of the reexamination can be written either in groups or individually.
Examination prerequisites

To be eligible for the exam in Digital Methods, it is a requirement that students have completed and passed four project-related assignments. The assignments can be submitted individually or in groups and must be approved by a member of the teaching team. The length of each assignment must be no longer than 3 standard pages.

Aid
All aids allowed

ChatGPT and other large language model tools are permitted as a dedicated source, meaning text copied verbatim needs to be quoted, the tool cited, and generally the specific use made of them needs to be described in the submitted exam.

Marking scale
7-point grading scale
Censorship form
No external censorship
Exam period

Exam information:

The examination date can be found in the exam schedule    here

The exact time and place will be available in Digital Exam from the middle of the semester. 

Re-exam

A witten take-home assignment, written either in a group, or individually, on a subject pertaining to the course content and prescribed literature. The subject must be pre-approved by the course lecturer(s).

Reexam info:

The reexamination date/period can be found in the reexam schedule    here

Criteria for exam assessment

Students are assessed on the extent to which they master the learning outcome for the course.

 

To obtain the top grade “12”, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.

 

To obtain the passing grade “02”, the student must in a satisfactory way be able to demonstrate a minimal acceptable level of the knowledge, skills and competencies listed in the learning outcomes.

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 60
  • Exercises
  • 35
  • Project work
  • 63
  • Exam
  • 20
  • English
  • 206

Kursusinformation

Language
English
Course number
ASDK20007U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 4
Studyboard
Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Hjalmar Alexander Bang Carlsen   (2-6d684578746833707a336970)
Saved on the 01-05-2025

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