I think this was one of the most helpful chapters on analysing data that I read for this project. However, when I’d first read it for our third workshop, I was at such an early stage of my research that I got quite overwhelmed with all the information that this chapter provides. I came back to it once I gathered my data and was getting ready for its analysis, and that was the moment that I was able to process it better.
It intrigued me to learn that analysis can be playful, imaginative flexible and reflexive. I made me think ‘I can do it’. At the same time, it is important to remember that: “Qualitative analysis is ‘intellectual craftsmanship’ – playful but methodical and intellectually competent.” (p.132)
Most important part of this text was the list considerations in the process of analysis. I have put the ones I thought are most relevant to my research into the kind of mind-map to help me internalise the information.

This chapter also talks about multiple perspectives in analysis. It makes a very interesting point that:
“The different views either come together to support your argument or make you question your original research proposition. Both outcomes are valuable in research terms. Obviously, it is satisfying to have arrived at some kind of consensus or broad agreement; however, it is equally interesting to have a range of different and possibly conflicting views. (…) An honest appraisal of the strengths and limitations of the analytical approach and methods used is an important part of a research report or dissertation.” (p.142)
Finally, it describes various tools that you can use to visualise the data. Some of the tools I will not be able to apply to my research, however, it was still good to learn about them. I am drawn to try matrix (depending what data I will have, but I see a scope for using it with the questionnaire results combine with the focus group interview) and mind-maps (one of my go- to tools when learning – Padlet reminds me of them). I quite like dimensional analysis – I want to try that out if I think it can be applied to my data set.