Event details

How do I manage a large amount of data? How can I deal with conversational (e.g. from interviews), textual (e.g. newspaper articles) or visual data (e.g. observations) in a systematic way? What techniques are there for reducing, systematizing and interpreting data? Are there programs that support me in this?

In order to manage the complexity of everyday life, we are constantly categorizing, i.e. we classify things, statements or actions, find names for them and implicitly define criteria with the help of which we systematize all these impressions. A simple example: If something has four wheels, drives on the road, etc., we categorize it as a car; if it exceeds a certain size, it falls into the category of truck, bus or similar for us. It becomes more challenging when we are not dealing with a single object, but with complex texts or images, for example, which we try to understand and categorize. Qualitative content analysis (QIA) is a method for evaluating different types of data that builds on this everyday form of gaining knowledge and uses it systematically to understand the manifest/explicit and (within certain limits) latent/implicit content of the data material. Category systems are the essential instrument for systematization.

The workshop introduces the basics of qualitative content analysis and also tests and reflects on various qualitative content analysis techniques of category formation.

In particular, the following topics will be covered:

• Basics of qualitative content analysis
• Classification of qualitative content analysis in the qualitative research landscape
• Category formation and types of categories
• Techniques for developing content analysis categories
• Requirements for category systems

Previous knowledge in the field of qualitative content analysis is not required. It is expected that the preparation package will be completed before the classroom session.

Learning objectives:

At the end of the course

• be able to name central features of qualitative content analysis,
• be able to classify qualitative content analysis in the qualitative content analysis research landscape,
• have become familiar with the prototypical process of a content-structuring qualitative content analysis,
• can apply central qualitative content analysis strategies and techniques, in particular category formation, and
• are familiar with the possibilities of software-supported evaluation and can classify these with regard to their own research and work process

Workload for ECTS:

1 ECTS: active participation in the workshop, working through a small preparation package (reading, instructional videos, exercise)

Lecturer:

Christoph Stamann is a research assistant at Magdeburg-Stendal University of Applied Sciences, where he develops digitally enriched teaching/learning arrangements for qualitative social research as part of the h²d² project (didactically and digitally competent teaching and learning). His methodological focus includes qualitative content analysis, on which he regularly offers workshops and in 2019/2020 co-edited a double issue in the journal Forum Qualitative Sozialforschung / Forum: Qualitative Social Research in 2019/2020.