You’ve read hundreds of books. You’ve waded through archival material. You’ve got mountains of surveys, folders full of transcripts, notebooks stuffed with barely legible field notes, and rather more photographs than you initially intended. Now what? How is it going to be possible to convert all of this material into something sensible? Where do you start? What is it you don’t know about data analysis ?
It’s not at all uncommon to feel deeply worried about getting started on analysing all your material. Thinking about what it might take to make something out of the pantechnicon of paper and digital documents produces deep chasms of doubt, a fug of anxiety and/or a crisis in self-belief.
Getting through this stage can be really, really tough. They don’t often say that in the methods books. It all looks rather simple and straightforward on the written page. Well, I’m here to tell you that too many of the books gloss over the messy reality that is beginning to make something out of your research stuff. It’s daunting.
Some people find the sheer volume of data produces a kind of paralysis. Not quite knowing where to start means nothing gets done at all. Other people start doing something, anything, in the way that they interpret the research methods texts to suggest – code, code, code. Others turn to a bit of software for support. Many read their data over and over, hoping that it will speak to them. Sometimes of course this does happen – but this is actually because the task of making sense of the material has occurred subconsciously rather than through a more explicit process. While I’m all in favour of letting the subconscious do the work, it is a bit of a risk on time-limited projects – it may take a very long time to produce anything. You usually have to try to hurry the process up.
I tend to think about data analysis in the same way as I think about inventing a new recipe. You have to be systematic. So at the start you work small, and try several things out one after the other to see what seems to taste good. So, let’s imagine you have a base ingredient, say potato, but then you have to sort out how to cook it and what to put with it. Potatoes stewed with walnuts? Disaster- nil points. Potato mashed with parsley – sort of OK but not really what you were after. Steamed potato dressed with bacon, spring onion and chilli oil? Now we’re starting to get somewhere.
So it is with potatoes and with data. Start small, try things out one after the other to see what does which. Find the tasty combination.
I think it’s a really good idea to take a small clump of data and then see what you can do with it. Say you have a set of interview transcripts and you’ve asked the participants the kinds of questions so you’ve covered some common ground… now take just one of those common areas. Put all of the various answers from the transcripts together, numbering them carefully so you know where they came from originally. You can do this physically or you can cut and paste from digital files using a word search. Then try a few things out.
- Take one of the transcript sections and read each separate sentence very carefully – how might each one be understood? If you think a sentence might mean a particular thing. See if you can find any indication that your interpretation is justified. What else is there that relates to this idea? If so, What might be going on?
- Are there any common themes (messy, blurred) that cut across all of the data?
- What metaphors are used?
- Are there phrases in common across the data – are these indicative of some kind of shared framework or discourse?
- What little narratives are there?
- Look for reasonings – Who is doing what to whom and why?
- What categories are used and what is included and excluded through their use?
Or you might take a section, a coherent cluster of answers, in a survey and look to see the various ways in which the numbers might be interpreted, represented and cross tablulated.
Or you might take one transcript only and work through it very carefully, before writing a sketch of the person, and their story/ies.
This is not a complete list of possibilities, of course. The point I want to make is that you have to start somehow, but this doesn’t mean starting anywhere. You do have to be very brave and plunge in, but you may very well not automatically know what and how to do the analysis first off. If you do, that’s great and you will just get on with it. But if you don’t, then you need to work out a way to proceed. You must generate a beginning analytic strategy so you can take a bite sized piece of stuff… this will then help you understand how to tackle the entirety.
Data analysis… As the saying goes – how to eat an elephant? One mouthful at a time. The trick with data analysis to take that first bite – and it’s got to be big enough so you get to experience what it will be like to eat the lot, and not so big that you choke.