I’ve been working with my data for the past few weeks and one of the most painful yet (hopefully) rewarding activities I’ve had to manage is the act of processing the data.  Before you can even being to analyse your data it needs to be in a format that is useable for the purposes of your study.  In most cases researchers can’t begin analysing the data in it’s raw form.  For example, my overall methodological approach is within narrative based research and part of my study involves working with the data from one-to-one interviews.  The question then becomes what do I mean by ‘interview data’.  Generally speaking it’s the interview transcriptions that I’ve had to generate by spending several (soul destroying) hours listening to the interview recordings and typing it all down, which in my case, was pretty much the whole interview.  Now I hate transcribing, I hate it, and yet it’s a necessary step I had to do in order to get the raw data (i.e. the interview recordings) into a format that I could use.

In this sense, processing data is like doing laundry by hand.  You start off and it’s great. It’s new and you’re still getting used to the tools.  The water is hot and everything seems to be as it should. Then the water starts to get cold and your hands are uncomfortable but you’re not done! There’s a spot that you missed and you need to go back.  Then there’s another spot, but the water is too cold. So you need to go away and boil water to add to the cold water and start the process again…but it’s a little different this time.  You’re seeing not only the spots but noticing the more minute details of the item you’re washing – the weave of the cloth, the colours…

Courtesy of spike55151 via Flickr…then the water gets cold. Again.

Repeat.

I’ve been reassured from other colleagues (post-viva and almost there) that the effort is well worth it and that it could even start to be fun (?).  I can see their point, because as I continue to process my data into a useable format I’m learning about how I would like to go about analysing the data, what works and what is unlikely to be effective.  Until the bulk of my data is processed, for the moment, I suppose I have to endure.

(Many thanks to Paul Breen and Magdalena de Stefani for inspiring this post)

One thought on “The hard, cold slog of processing data

  1. Hi Eljee, I really understand where you’re coming from with this. I spent a long time trying to do anything other than sitting down and starting the laundry, even though everybody from Mark Twain to Simone de Beauvoir tells us that we should never put off until tomorrow what we can do today. Then I think there comes a point when you’ve shopped around so many books and journals that the cupboards are full and there’s nothing else to shop for. You’ve now got to tackle that blooming laundry basket, blooming in the sense of the way posh people say polite things when they want to swear, and blooming in the sense of being like a great big apple tree that’s standing in the way of everything else. There’s no more getting around it and you’ve got to sit down and prune it because if you don’t nothing’s ever going to grow from it. The leaves are going to smother the apple buds so you’ve got to get the secateurs. But – unlike the apple tree – you can’t just throw the dead leaves in the green wheelie bin. You’ve got to store them, keep them alive in a fridge or something, and then when you’re doing the laundry you go back and say ‘wait, I need to bring that leaf back to life.’ No wonder it’s so almost maddeningly frustrating. Worse than that it feels like sometimes the light you’re sitting in changes the way you see things, so if you’re in direct sunlight it seems one colour and then moonlight seems another, and then sitting in half-darkness at midnight seems like something else. Or worst of all you’re lying in bed and you’re in half sleep and you think to yourself you finally understand the fabric in the same way as Van Gogh once said that the best pictures we paint in life are those we dream of when we’re smoking a pipe in bed. I guess these days with health and safety that’s not allowed, even for artists, but that’s when it gets really frustrating. You think you’ve found the thing holding together the central fabric and you get up to tackle the laundry again and it’s just a loose thread that’s dangerously close to unravelling all your ideas so far. But it does get fun. Right now I am at a place in the analysis where the interaction between people is really interesting. I was also recently informed that when British people use words such as ‘quite’ they could actually mean it in a negative way but the Scots, the Irish and the Americans really do mean something’s quite good or quite interesting when they say that. Finally, perhaps there’s a further angle on your laundry analogy. If data analysis is doing laundry by hand, is CAQDAS the washing machine or the tumble dryer?

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