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Following discussions with current PhD students in the research group, we’ve realised that every researcher was using different methodologies, but we were not sharing enough about them. A small group of us decided to start a new short course aimed at PhD students in the section but also from other departments and institutions (that’s very common in Denmark, contrary to the UK where I did my PhD). It was actually a great opportunity to read again about qualitative, quantitative, and mixed methodologies; especially targeted at our field of studies:
PhD course on Research Design and Case Study Methodology in Planning and Landscape Research


We conducted a short survey in the department to get a feeling of what were students’ expectations of a ‘methodology’ course, and divided the course into 5 days:
  • research design, to build solid bases in science inquiries
  • case study design, since that’s often a methodology in planning
  • qualitative data collection, to discuss interviews, focus groups, and so on.
  • quantitative data collection, to discuss surveys
  • analysis of data, to present some tools.
We invited researchers experts in and out of the department to teach a large part of these courses. This was actually a really interesting experience for all of us in the department – we got to hear about philosophy of science, interviews, surveys, and quantitative analysis from people who have years of experience and some of them had already taught them.
I was in charge of the last day, analysis and visualisation of data. It was a great opportunity for me to wrap up the concepts we had visited, starting from the research design, to the data collection, and introducing tools to analyse and visualise data. I’m passionate about data visualisation, so I was really looking forward to presenting that.

Data analysis

The PhD is often the first time most students will be carrying out some ‘full scale’ research. It is can be the first time they need to be very critical of their own data – and therefore before talking about analysis I wanted to emphasise how they should be discussing the quality of their data. I introduced concepts like reliability, validity, generalisability, and credibility. We discussed what it meant for them, and how they would be talking about it in their research.
In a second part, I presented a set of steps that they could follow to analyse their data – preparation (transcribing, cleaning, ..), reduction (finding patterns using different methods; integrated, inductive or deductive), presentation, and conclusion/verification.
I also presented a different analysis: content, interview, and sentiment analysis, as well as useful tools: AntCon, nVivo, Excel, Python, R, ..

Data visualisation

This was my favourite part, and I really enjoyed doing the theoretical research for it. A graph should not be drawn thinking ‘what do I have?’ but instead ‘what do I want to show?’ For that reason, I really liked the following graph:
This is really a complete picture of what type of stories can be told with data. If the data has been correctly reduced, then it is easy to know what trends exist.


This course can actually a little bit challenging; the students who joined the course came from a large range of disciplines – water management, climate adaptation, and urban planning. Some had very quantitative research, while others had very qualitative research. Some had rather theoretic approach, and a few had an applied approach. It was difficult to teach data analysis and visualisation while striking a balance between all these interests. However, I think it was interesting to give an opportunity to students to discuss their research in a systematic way, and to show them the other approaches that exist.