Wednesday, 8 May 2019

Qualitative coding using software

Thesis Whisper Inger Mewburn posted last year, "Are the robots coming for our (research) jobs?", discussing the use of support systems for research, such as Grammarly, NVivo and Interpris. Given a trial version of Interpris (from QSR, the makers of NVivo), Mewburn was pleasantly surprised at how well the software analysed qualitative themes from a raw data spreadsheet: as she put it, "Interpris had done in less than a minute what would take me at least a day – maybe two" (25 April 2018). 

What Interpris had done was to look for likely codes. It found far too many, and so plenty of weeding and reorganising had to be done. But because of the in-built AI, it aims to know us and to add our complexity to the suggested codes as time goes on. The best thing is that it could - should - refine without our biases. Nothing like isolating a limiter!

In my view, I think anything that helps us do our work more easily, more replicably - and thus with more replicability - has got to be a bonus. I disagree with the purist view, which is that the thinking, structure and planning has to be done before we get to the analysis part, otherwise we get that old management "systems theory": garbage in >> transformation >> garbage out (Von Bertalanffy, 1968). In qualitative research this 'purist view' is the wrong approach to the research anyway: we are supposed to start our coding once we see our data. We can't set out to analyse with too much structure, otherwise it adds to our biases. 

While I love Johnny Saldana's (2009) book, The coding manual for qualitative researchers, I have no problem using a machine leg-up to start to pull out my codes. Interpris's cut would be the first draft though, to be built on, thought about, verified, tested: all those things that researchers do. It should not be taken as an end-point, but as one interpretation of the data. 

You can see what Interpris can do here:

Interpris sounds very exciting. I can't wait to see it in action.



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