Regular blog readers will know I’ve been procrastinating just a bit on really digging into my data coding process. Why?
Coding qualitative data can be tedious….and it’s summer, it's really nice outside right now! So…I’ve locked myself in a windowless room in my basement today to get down to business.
Everyone seems to have their own spin on coding qualitative data but there are some general guidelines. It starts with a careful reading of the text and highlighting key words or phrases and making notes in the margins. Ideas are then grouped into categories, then into bigger themes. Throughout the process the research question should be kept top of mind. Working inductively, patterns will emerge from the data. Conversely, a researcher might enter the process with a particular theory or hypothesis and then work deductively to see how that theory is supported or refuted by the data. All of this is highly subjective, as you might imagine, which isn’t necessarily a bad thing, but it needs to be acknowledged.
I decided to build a colourful data visualization while coding to “see” the data in a new way.
To inspire myself and keep things interesting, I assigned a different coloured post-it note to each participant. I went through the interview data with post-it notes in hand, jotting down interesting phrases, words and ideas that I had highlighted. I grouped these into categories, roughly assigned through my interview guide questions. During the process, certain observations and questions started to come up so I jotted those down on different post-it notes.
I’ve now finished section one and the wall is starting to look…pretty! At this point, I get a good feeling and take a well-deserved dance break.
I’ll be moving on to other sections following this same type of process with all the interview data throughout the rest of the weekend.
Some early observations and questions, some of which may ultimately fall outside of my project parameters…
How will the use of AI in medicine impact or shape medical practices?
What gets missed when we take a data-driven approach to problems?
Participants tend to talk about ethics in ways that are distant or abstract rather than seeing ethics from a personal level.
Issues around funding and its influence in agenda setting are rarely direct or overt in academia. However, funding agendas, particularly as set by governments via grants, do shape institutional funding which in term shapes what research is done.
Now that I’m over the hurdle of getting started, I hope the rest of it will be easier.