Friday, 16 May 2014

Let's be pragmatic: one approach to qualitative data analysis

Today, Hanna, one of my MSc students, has been asking interesting questions about doing a qualitative data analysis. Not the theory (there's plenty about that), but the basic practicalities.

I often point people at the Braun & Clarke (2006) paper on thematic analysis: it’s certainly a very good place to start. The Charmaz book on Grounded Theory (GT) is also a great resource about coding and analysis, even if you’re not doing a full GT. And I've written about Semi-Structured Qualitative Studies. For smallish projects (e.g. up to 20 hours of transcripts), computer-based tools such as  Atlas ti, nVivo and Dodoose tend to force the analyst to focus on the tool and on details rather than on themes.

I personally like improvised tools such as coloured pens and lots of notebooks, and/or simple Word files where I can do a first pass of approximate coding (either using the annotation feature or simply in a multi-column table). At that stage, I don’t worry about consistency of codes: I’m just trying to see what’s in the data: what seem to be the common patterns and themes, what are the surprises that might be worth looking at in more detail.

I then do a second pass through all the data looking systematically for the themes that seem most interesting / promising for analysis. At this stage, I usually copy-and-paste relevant chunks of text into a separate document organised according to the themes, without worrying about connections between the themes (just annotating each chunk with which participant it came from so that I don’t completely lose the context for each quotation).

Step 3 is to build a narrative within each of the themes; at this point, I will often realise that there’s other data that also relates to the theme that I hadn’t noticed on the previous passes, so the themes and the narrative get adapted. This requires looking through the data repeatedly, to spot omissions. While doing this, it's really important to look for contradictory evidence, which is generally an indication that the story isn't right: that there are nuances that haven't been captured. Such contradictions force a review of the themes. They may also highlight a need to gather more data to resolve ambiguities.

The fourth step is to develop a meta-narrative that links the themes together into an overall story. At this point, some themes will get ditched; maybe I’ll realise that there’s another theme in the data that should be part of this bigger narrative, so I go back to stage 2, or even stage 1.  Repeat until done!

At some point, you relate the themes to the literature. In some cases, the literature review (or a theory) will have guided all the data gathering and analysis. In other cases, you get to stage 4, realise that someone has already written exactly that paper, utter a few expletives, and review what alternative narratives there might be in your data that are equally well founded but more novel. Usually, it’s somewhere between these extremes.

This sounds ad-hoc, but done properly it’s both exploratory and systematic, and doesn’t have to be constrained by the features of a particular tool.