ASOK15636U Digital Methods: From Ethnography to Supervised Machine Learning

Volume 2018/2019

MA Research Methodology and Practice (MSc Curriculum 2015)

Course package:

Welfare, Inequality and Mobility
Culture, lifestyle and everyday life
Knowledge, organisation and politics

Creditstudents must be at master level


It is widely recognized that growth in digital data formats enable new relations between quantitative and qualitative methods of inquiry and analysis, thus posing questions as to how such new complementarities are best exploited for social-scientific and practical purposes.

This course will run through a number of ways in which qualitative and quantitative modes of inquiry complement one another. They will compose a research strategy that ensures both qualitative methods strength in valid interpretation of the meaning and consequence of social actions and quantitative methods strength in generalization and structural analysis. 

The course will structured around a mini research project from data collection to analysis. In the data collection part we will combine quantitative sampling theory and qualitative case selection theory to ensure a good sample. For exploring the data we will discuss and use a set of methods meant to map out ones data and display the varying densities and differences in ones data(network analysis, clustering, multidimensional scaling).

These maps work as navigational device to ensure that one digital ethnography gets hold of the relevant variation in ones data. The digital ethnography entails a practice of participant observation in digital spaces (e.g. a Facebook group, a company intranet board) for the purpose of learning the details of the values and motivations characteristic of social interaction in this setting.

The digital ethnography provides the interpretative grounding which insures that the categories and social processes, that in the later stage will be quantitatively assessed, have a hold in the meaning-making practices of the actors themselves. In order to explore, generalize and test large scale patterns we need to translate our ethnographic description into something quantifiable.

We will use supervised machine learning in order to scale our qualitative categorization/coding. The student will learn the pro's and con's of various strategies regarding ones sampling, optimization strategies, the uses of unsupervised methods as input.

Importantly we will pay a lot of attention to biases detection and correction to ensure the reliability and validity of our supervised machine learning model. This categorized dataset can then be used of more or less simple quantitative analysis which together with the digital ethnography will make the mini analysis.     

Learning Outcome


  • Understand and reflect on mixing qualitative and quantitative modes of inquiry in Digital Methods
  • Evaluate pitfalls and potentials of using large scale digital data on social action



  • Use mixed methods research strategies to produce analysis at one's sensitive to actors meaning-making practices and able to capture and analyze large scale social patterns.  
  • Master a set of methods skills(digital ethnography; quantitative mapping, and supervised machine learning)



  • The  students will learn how plan and conduct a mini analyse using digital methods and data from start to finish.
  • The student will be able to handle both the qualitative and quantitative challenges of working with 'found data'.    

Syllabus in Absalon

Forlæsninger og øvelser.
  • Category
  • Hours
  • Course Preparation
  • 47
  • Exercises
  • 125
  • Lectures
  • 28
  • Preparation
  • 6
  • Total
  • 206

I give structured feedback to student presentations, drafts of the final paper and to the final paper. Moreover, students get informal feedback to their ideas and arguments during class discussions.

7,5 ECTS
Type of assessment
Course participation
Active participation
Active participation consists of handing-in 3 small written papers during the semester regarding 3 different subjects
Exam registration requirements

Sociology students must be enrolled under MSc Curriculum 2015 to take this exam.

Credit students must be at master level.

Marking scale
passed/not passed
Censorship form
No external censorship
Exam period

Find more information on your study page at KUnet.

Exchange students and Danish full degree guest students please see the homepage of Sociology; and


At re-exam, the form of examination is the same as ordinary exam.

If the form of examination is ”active participation” the re-examination form is always “free written take-home essay

Criteria for exam assesment

Please see the learning outcome.