A post for National Poetry Day.
It is pretty common for research methods courses and books to suggest that qualitative researchers read through their data – such as interview transcripts – several times. Reading through happens before you get down to the ‘real work’ of ‘actual analysis’. The idea is that you get familiar with what’s been said, as a whole, before you begin to deconstruct. It’s perhaps a bit like having to understand what a cheesecake is before you begin the cheffy separation into crumbs and mousse.
There are various reasons for comprehensive data reading at and as the start of analysis. Narrative researchers might suggest that by reading the lot you start to apprehend the overall story that is being told, and you often identify any sub-stories that are lurking in the text. Grounded theorists might argue that you start to pick up the themes as you read – they either leap out at you or gradually come into view. Arts-based researchers might say that reading through allows an early hearing of the literary qualities of speech – the metaphors, repeated words, pauses and stumbles that often get missed in coding and thematising processes.
I’m always interested in early wholistic reads as a way to think about, say, how a person being interviewed makes meaning. Of course to do this kind of activity, you have to try to do the impossible and get into the other person’s head… And of course this really is completely impossible. You can however have a go. Having some questions in mind can help in this process of mind-reading. What sense does the interviewee make of my questions? What do they do with the topics I’ve directed them to? What resources do they call on to put their point of view? What experiences do they reference, what relationships and networks do they choose to make visible, what histories and contexts are fore-grounded? What seems to be the logic of what they are saying? Or, in the case of field notes, what on earth was I thinking then? (That’s a joke, Joyce.)
But as well as some orienting questions, I reckon it also helps to be open to qualitative data as a mess, a muddle, as profoundly not logical and reasoned.
I’ve been vaguely interested in Keats’ notion of ‘negative capability’ for a while. I’ve wondered what it might have to say to very early readings of interview transcripts and other qualitative data. I’ve recently returned to the idea more seriously. This revisit came about as I was faced with the refusal of some data to make nice, neat sense. The words and images just wouldn’t let themselves be packaged up into definite know-able and name-able clumps. And negative capability came to mind.
Now, if you didn’t get Keats’ notion of negative capability offered to you in your undergraduate years, then it might help to know that it’s generally attributed to a mere couple of lines in a letter written in 1817. Keats says…
…I mean Negative Capability, that is, when a man (sic) is capable of being in uncertainties, mysteries, doubts, without any irritable reaching after fact and reason.
I was taught in my undergraduate English degree that this fragment meant that Keats thought it was foolish to go searching for an ultimate truth. He thought that this was particularly so for poets and other artists who ought, he thought, not to bother about essential truths at all. Rather, they ought to be open and receptive to the unknowable. In other snatches of writing Keats also talked about the necessity of artists being able to empty themselves of what they hold most dear; they ought to let go of their customary ideas and aspirations in order to be open to the ineffable.
When faced with my difficult data, it struck me that something of the spirit of Keats might be appropriate. I’m attracted to Keats’ urging to resist the rush to find truths, to dispel all of the usual thought processes and to embrace the contradictions, vagueness, and elusiveness of speech/life/the world. I like his notion of being uncertain and of the receptiveness to alternative ways of talking/thinking/being that it might offer. And the notion of negative capability might also act as some solace when the analysis just doesn’t do what what it’s supposed to.
But I also thought about his idea more generally. Negative capability would certainly be useful to bring to first readings of interviews and conversations. It would put off the urge to get things sorted quickly, a virtue that methods books and doctoral training often don’t explain sufficiently.
I’m not wanting to argue here for negative capability – read as a resistance to some kind of approximate and contingent attempt at truthfulness – as a permanent research sensibility. I might argue that at some other point in time, but right now I’m just thinking that it might be a pretty helpful notion to insert into some of the highly technical conversations I hear in research methods training. A focus on negative capability as an early stage of meeting-and-greeting-your-data would certainly slow down the leap to decide what the stuff ‘says’. A focus on negative capability might also encourage a little scepticism about the processes that are used to force data into apparently resolute categories called themes and codes.
And of course in my case, negative capability might also legitimate tolerance of mess and ambiguity – which is after all an inevitability for data (words, images and numbers) which rely on human interpretation.