This post links to a videos that form a series for people grappling with the many challenges of analysing qualitative data. To me analysis is by far the hardest but also most rewarding part of research. One thing I have learned over the many years is that there are no short cuts! Good analysis has to take time. Lots of time. Time with your data. So you won’t find tips and strategies for a quick outcome or cutting corners. I focus on ideas that are relatively simple to grasp but profound in their implications, while avoiding areas that are covered extensively in textbooks and methods literature. The videos are relatively short, and I will embed pictures of any slides that appear in the videos here.
You can use these links to skip directly to the videos:
Video 1: The messy history of working with concepts or theory in qualitative analysis Click QDA1
Video 2: What are you trying to achieve in qualitative data analysis? The, any, or an interpretation? Click QDA2
Video 3: Getting to the heart of qualitative analysis through three simple but profound questions. Click QDA3
Video 4: To interpret qualitative data – 10 ways to be wrong. Click QDA4
You can view qual analysis video 1 on youtube.
I chose this one first because it sets the scene for a process that is focused on you, the analyst, and how concepts or theoretical ideas can change what you can see in and do with your data. It doesn’t get into technical ideas about processes of applying theory. Instead it talks through what I have experienced many times now in the hard slog of coming to understand a concept, what it can do for you and what you can do with it in a particular project. Here is the slide:
This is another foundational video (video 2 on youtube), asking you to step back and think about what you’re doing in a fundamental way. Are you looking for the analysis – something objective, independent of who is doing it? Are you looking for any analysis – one of a multitude of possible subjective outcomes? Or something in between – a qualified, subjective, researcher-driven and researcher-enhanced approach (not researcher-proof)? At the end I refer to a video I made about Shenton’s article on trustworthiness in qualitative analysis.
The third in this video series is based on a paper I published with Prachi Srivastava in 2009. That paper is #OpenAccess so you can download it for free through the link I’ve provided here. We present a practical, iterative framework for qualitative data analysis based on three questions:
- What are the data telling me?
- What do I want to know?
- What is the dialectical relationship between what the data are telling me and what I want to know?
This framework (and I do say so myself!) embodies a parsmionious quality: it is simple, but deceptively so. Only three questions, but as the paper suggests, these strike at the heart of a number of important issues. These include questions relating to whether you are seeking an objective researcher-proof analysis (we assume not, hence the ‘me’ and ‘I’ in the questions), and the relative balance between grounded and theoretically informed approaches.
The paper has been cited over 180 times (you can see the details through google scholar) by people from around the world, and in a wide range of disciplines. So hopefully there is something useful in it for many readers of this blog too! The accompanying video gives a quick introduction, but the meat is really in the paper.
This video is inspired by one of my favourite academic books of all time: To interpret the earth – ten ways to be wrong by Stanley A Schumm. That book is about geomorphology (describing physical landscapes and the processes that shape them). I read it as a undergraduate in geography.
What’s good about it is it urges caution, it prevents accidental arrogance or misplaced confidence in the claims we make about the world. I think the same is of value to those of us trying to make sense of the social world, particularly through qualitative data.
In brief the 10 ways to be wrong are in three groups:
To do with scale and place: Time, Space, Location
To do with cause and effect: Convergence, Divergence, Efficiency, Multiplicity
To do with system response: Singularity, Sensitivity and Complexity.