{"id":40169,"date":"2017-08-17T02:42:12","date_gmt":"2017-08-17T09:42:12","guid":{"rendered":"http:\/\/world.edu\/?p=40169"},"modified":"2017-08-17T02:42:12","modified_gmt":"2017-08-17T09:42:12","slug":"play-with-your-data","status":"publish","type":"post","link":"https:\/\/world.edu\/play-with-your-data\/","title":{"rendered":"Play with your data"},"content":{"rendered":"

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Data analysis can be pretty scary. \u00a0That moment when you realise that making sense of the stuff you\u2019ve so painstakingly generated comes down to you \u2013 just you. \u00a0Well, relax. It\u2019s not just you that has to leap into the unknown. We all put ourselves on the line when we make decisions about definitions, scales, codes, themes \u2013 and when we saywhat the analysis eventually means. But that realisation can be alarming \u2013 maybe it\u2019s even paralysing.<\/p>\n

You might think that qualitative researchers suffer most from nervousness about interpretation \u2013 \u00a0facing up to those mountains of transcripts and\/or field-notes and\/or photographs can be really daunting. \u00a0But of course surveys also often contain open questions too so \u00a0mixed methods and quants researchers also often face lots of words, not to mention the task of actually converting numbers into something convincing and believable.<\/p>\n

Doctoral researchers often get the worries at the beginning stages of data analysis. What if I am just being stupid? What if I am barking up the wrong tree altogether? What kind of criticisms might be levelled at the choices I have made?<\/p>\n

So it\u2019s not at all surprising that in the face of masses of data that doctoral researchers opt for a technique-led approach to analysis. After all, regardless of whether we are doctoral or highly experienced researchers, we all<\/em>\u00a0have to demonstrate that we have approached the processes of data analysis systematically, and consistently.<\/p>\n

But sometimes the need to be technically proficient is more like clutching at a life raft. If I just do the analysis \u2018the right way\u2019 then that\u2019s OK. Showing I can follow a set of \u2018rules\u2019 is all that matters. I\u2019m not vulnerable because my exemplary technical prowess is all that counts.<\/p>\n

We researchers quite rightly try so hard to get rid of anything that might seem dodgy. For example, one of the strategies that people employ to guard against idiosyncratic individual judgements is \u2018inter-rater reliability\u2019. This is where two people code the same transcript. An interpretation is reliable if both people agree \u2013 if they \u2018see\u2019 the same thing.<\/p>\n

But isn\u2019t that also a worry? If we are looking at two people with the same sets of understandings because they are in the same disciplines and have had the same kind of research training, then something else might be going on. In finding a common account of and for their data, they might be just acting really predictably and conventionally. They might be simply finding what they expect to find.\u00a0Processes of choice and interpretation of data are precisely where taken-for-granted ways of thinking can really get in the way. You see this pattern, you opt for this direction because it just \u2018seems right\u2019 or \u2018obvious\u2019 or \u2018expected\u2019.<\/p>\n

But what if it\u2019s not so straightforward?<\/p>\n