In the analysis of qualitative data, it can be easy to fall in the habit of creating either very descriptive, or very general theoretical codes. It’s often a good idea to take a step back, and examine your coding framework, challenging yourself to look at the data in a fresh way. There are some more suggestions for how to do this in a blog post article about turning coding strategies on their head. But while in Delhi recently to deliver some training on using Quirkos, I was struck by a couple of exhibits at the National Museum which in a roundabout way made me think about coding qualitative data, and getting the balance right between analytical and emotional coding frameworks.
There were several depictions of Hindu deities trampling a dwarf called Apasmāra, who represented ignorance. I loved this focus of minimising ignorance, but it’s important to note that in Hindu mythology, ignorance should not be killed or completely vanquished, lest knowledge become too easy to obtain without effort.
Another sculpture depicted Yogini Vrishanna, a female deity that had taken the bull-head form. It was apparently common for deities to periodically take on an animal head to prevent over-intellectualism, and allow more instinctive, animalistic behaviour!
I was fascinated between this balance being depicted between venerating study and thought, but at the same time warning against over thinking. I think this is a message that we should really take to heart when coding qualitative data. It’s very easy to create coding themes that are often far too simple and descriptive to give much insight in the data: to treat the analysis as purely a categorization exercise. When this happens, students often create codes that are basically an index of low-level themes in a text. While this is often a useful first step, it’s important to go beyond this, and create codes (or better yet, a whole phase of coding) which are more interpretive, and require a little more thinking.
However, it’s also possible to go too far in the opposite direction and over-think your codes. Either this comes from looking at the data too tightly, focusing on very narrow and niche themes, or from the over-intellectualising that Yogini Vrishanna was trying to avoid above. When the researcher has their head deeply in the theory (and lets be honest this is an occupational hazard for those in the humanities and social sciences), there is a tendency to create very complicated high-level themes. Are respondents really talking about ‘social capital’, ‘non-capitalocentric ethics’ or ‘epistemic activism’? Or are these labels which the researcher has imposed on the data?
These might be the times we have to put on our imaginary animal head, and try to be more inductive and spontaneous with our coding. But it also requires coding less from the head, and more from the heart. In most qualitative research we are attempting to understand the world through the perspective of our respondents, and most people are emotional beings, acting not just for rational reasons.
If our interpretations are too close to the academic, and not the lived experiences of our study communities, we risk missing the true picture. Sometimes we need a this theoretical overview to see more complex trends, but they should never be too far from the data in a single qualitative study. Be true to both your head and your heart in equal measure, and don’t be afraid to go back and look at your data again with a different animal head on!
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