In the last blog post article, I talked about the benefits of visualising qualitative data, not just in the communication and dissemination stage, but also during data analysis. For newcomers to the world of qualitative research, the analysis process often seems intimidating and mysterious. How is the coding framework created? How are decisions made about categorising quotes? How do I know I have taken the right approach? When do I know when I am finished?
But as qualitative researchers will often tell you, the analysis process is so much more than a step-by-step regulated process. It is often like an exploratory journey, during which connections will be discovered, hypotheses challenged, and new unexpected paths will be taken. Personally I feel that qualitative analysis is a very creative process, something that Patton (2014) describes as drawing from “both critical and creative thinking – both the science and the art of analysis”.
Play is often the best way to work with qualitative data. Somehow it often feels more enlightening to explore themes and codes with Post-it notes, and manipulate them in a physical way – moving them around as part of a discussion and experimentation in grouping. The thematic bubbles in Quirkos can be manipulated in the same way, grouped and ungrouped by pulling them around, endlessly rearranged or grouped by colour.
I use the term play deliberately, as informal experimentation with data can uncover new things, and encourages the user to try different approaches. I really wish to encourage this during qualitative analysis, because it is so often productive. The graphical and interactive interface in Quirkos is key to this, as is the instant undo and redo buttons: it’s important to be always able to go back, because play so often leads nowhere, but occasionally somewhere very valuable.
Because of this, I have long believed that visualisation should be an integrated part of the analysis process, because it is inherently iterative and interactive. I know I am not alone on this: Bradley, Curry, Devers (2007) state that “there is general agreement that analysis is an ongoing, iterative process”. This applies to developing a coding structure, as well as the act of categorising text. For that reason, any approach that not only keeps researchers close to their data, but allows them to play with it, I find inherently beneficial.
The limitation with most software approaches is that they treat these exploratory steps as distinct events and phases, not as part of the process. So if you want to see connections in a data – run a report. If you want to see the text under a code - generate a summary. To visualise a topic, generate a graph of a particular node: all through a series of button clicks and separate windows. To me, this interrupts the discovery process in the same way that a scratched CD track stops the flow of music.
We designed Quirkos to help people play with their data. To make visualisations live and interactive, and to make it touch-friendly, colourful and forgiving. We try not to limit how researchers can sort and group their themes, but make flexible tools that people can adapt to their own preferred ways of working. And of course, to let people share their work with others and extend that creative process to teamwork, because the discussion and challenge that comes from engaging with other researchers is often as valuable as hours (or days!) of solitary coding.
Most of all though, we wanted software that freed the imagination to take more creative approaches, rather than tying researchers down to literally thinking inside a box or spreadsheet. Rather than a node-tree, the primary interface in Quirkos is called the canvas, and for a deliberate reason. It’s a sandbox for creative play, and space for that ingenious combination of art and science.
*Melamed, L. (1987). ‘The role of play in adult learning’, in Boud, D. & Griffin, V.(eds.), Appreciating adults learning: from the learner’s perspective, London: Kogan Page, pp13–24