Re-tooling Social Analysis: Behaviors, Networks, Ideas in the digital age (Summer 2019)

Course content

This course equips students with the analytic skills and reflexive capacities needed to engage critically but productively with various new 'device-aware' styles of social analysis assisted by digital and computational means. It does so, firstly, by way of reading paradigmatic analyses of the nature of social behaviors, networks and ideas, focusing both on classical concepts and contemporary research frontiers.

Examples are drawn from across all the social-science disciplines, and core interdisciplinary convergences are identified. Second, the course takes students through all the methodological steps of social research design, analysis and interpretation, tying these steps to practical examples and to students' own projects (from other courses). Here, key initial questions include: what are the implications of working with different data types (static vs. dynamic; broad vs. deep); how to think about and practically handle data biases stemming from digital platforms and devices (noise, bots etc.); how to build ethical considerations in from the start of data harvesting (digital research ethics)?

In a next step, students are introduced to key methodological traditions often underlying the analysis of behaviors, networks and ideas, respectively (causal-experimental; pattern search; meaning-oriented), as well as to ways of working across them using various digital data sources as well as combining with other sources (including both quantitative and qualitative). In a final step, students learn how to think critically about the interpretation of their social data analyses, including issues of internal and external validity, representativeness and generalizability, as well as analytical induction and concept work.

Rounding up, thirdly, students are introduced to frameworks for thinking about the changing place of social research in digital societies, including the possibilities and challenges opened up by greater interdisciplinary collaboration as well as new types of academia-industry-government partnerships.


BA/MA Elective course

MSc 2015:

Welfare, inequality and mobility
Knowledge, organisation and politics
Culture, lifestyle and everyday life




Learning outcome


  • The student will be able to explain and map out the different explanatory forms(causal-experimental; pattern search; meaning-oriented) active in the area of digital social research.
  • Know the affordances of new digital data and how this influences established modes of epistemological and ethical reasoning within the social sciences.
  • The student will known the pro's and con's of these different approaches when it comes to analysis behavior, network and ideas in a digital data setting.


  •  The student will be able to analysis, reflect upon and evaluate digital social research from epistemological, ethical and theoretical criteria
  • The student will be able to use their knowledge of the diversity of approaches within digital social research to think of novel ways of researching into behavior, networks and ideas by digital means.


  • The student will become competent in the logic of digital social inquiry and a qualified analysts of both own and others research.

Forelæsninger og øvelser.

Syllabus in Absalon 

Knowledge of Python and programming is an advantage

Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Written assignment
Free written take-home essays are assignments for which students define and formulate a problem within the parameters of the course and based on an individual exam syllabus. The free written take-home essay must be no longer than 10 pages. For group assignments, an extra 5 pages is added per additional student. Further details for this exam form can be found in the Curriculum and in the General Guide to Examinations at KUnet.
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

Please see the learning outcome.

  • Category
  • Hours
  • Class Instruction
  • 28
  • Course Preparation
  • 53
  • Exercises
  • 125
  • English
  • 206