Iterativity in data analysis: part 1

This post is a 2-parter and follows on from last week’s post about generating data.

The one thing I did not know, at all, during my PhD was that qualitative data analysis is a lot more complex, messy and difficult than it looks. I had never done a study of this magnitude or duration before, so I had never worked with this much data before. I had written papers, and done some analysis of much smaller and less messy data sets, so I was not a c0mplete novice, but I must say I was quite taken aback by the mountain of data I found I had once the data generation was complete. What to do now? Where to start? Help!

The first thing I did, on my supervisor’s advice, was get a license for Nvivo10 and uploaded all my documents, interview and video recordings and field notes into its clever little software brain so that I could organise the data into folders, and so that I could start reading and coding it. This was invaluable. Software that enables you to store, organise and code your data is a must, I think, for a study as large and long as a PhD. This is not an advert for Nvivo so I won’t get into all its features, and I am sure that other free and paid-for qualitative data analysis packages like Atlas Tii or the Coding Analysis Toolkit from UMass would do the job just as well. However, I will say that being able to keep everything in one place, and being able to put similar chunks of text into different folders without mixing koki colours or scribbling all over paper to the point of confusion was really useful. I felt organised, and that made a big difference to my mental ability to cope with the data analysis and sense-making process.

The second thing I did was keep very detailed notes in my research journal on my process as it unfolded. This was essential as I needed to narrate my analysis process to my readers in as much detail as possible in my methodology chapter. If a researcher cannot tell you how they ended up with the insights and conclusions they did, it is much harder to trust their research or believe what they are asking you to. I wanted to be believable and convincing – I think all researchers do. Bernstein (2000) wrote about needed two ‘languages of description (LoD)’ in research: the internal (InLoD) which is essentially where you create a theoretical framework for your study that coheres and explains how you are going to understand your problem in a more abstract way; and the external (ExLoD) where you analyse and explain the data using that framework, outlining clearly the process of bringing theory to data and discovering answers to your questions. The stronger and clearer the InLod and ExLoD, the greater chance other researchers then have of using, adapting, learning from your study, and building on it in their own work. When too much of your process of organising, coding, recoding, reading, analysing, connecting the data is hidden from the reader, or tacit in your writing about it, there is a real risk that your research can become isolated. By this I mean that no one will be able to replicate your study, or adapt your tools or framework to their own study while referencing yours, and therefore your research cannot be readily be built on or incorporated into a greater understanding of the problems you are interested in solving (and the possible solutions).

This was the first reason for keeping detailed notes. The second was to trace what I was doing, and what worked and what did not so that I could learn from mistakes and refine my process for future research projects. As I had never worked with a data set this large or varied before, I really didn’t know what to do, and the couple of qualitative research ‘textbooks’ I looked at were quite mechanical or overly instrumental in their approach, which didn’t make complete sense to me. I wanted a more ‘ground-up’ process, which I felt would increase the validity and reliability of my eventual claims. I also wanted to be surprised by my data, as much as I wanted to find what I thought I was looking for. The theory I was using further required that I not just ‘apply’ theory to data (which really can limit your analysis and even lead to erroneous conclusions), but rather engage in an open, multiple and iterative reading of the data in successive stages. Detailed notes were key in keeping track of what I was doing, what confused me, what made sense and so on. Doing this consciously has made me feel more confident in taking on similarly sized research projects in future, and I feel I can keep building and learning from this foundation.

This post is a more conceptual musing about the nature of qualitative data analysis and lays the groundwork for next week’s post, where I’ll get into some of the ‘tools’ or approaches I took in actually doing my analysis. Stay tuned… 🙂



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