Earlier this week, I had an appointment cancelled, which free’d up Wednesday. This mattered because an NVivo training course was scheduled for that day, and I’d been unable to attend because of my earlier commitment. As luck would have it, there was one place left, which i immediately snapped up … what luck! But at that stage, I didn’t realise quite how lucky.
The session was delivered by Ben Meehan of QDA Training, an experienced NVivo trainer, who promised us a rather different training experience to those we were perhaps familiar with. Although not quite as radical as we might have been led to believe, there were definitely some distinct differences between this session and conventionally-structured computer application training sessions. It’s not often you’re on such a course lasting only a day, where you don’t even touch the keyboard until after lunch! Nevertheless, that proved, for me, to be exactly the right strategy.
I wasn’t attending the conference as a complete novice. Given the approach I’ve taken to my research, at some stage I was going to need to collect, manage, analyse and interpret a wide range of qualitative data. With multiple methods (often within digital domains) generating several types of data, it made sense to me to seek computer assistance. Since the package to which the University subscribes is NVivo, I decided quite early on to begin to get to grips with it. There is plenty of helpful literature out there1, and a good number of YouTube videos; unsurprisingly some are better than others. I found this one particularly helpful:
Together, these resources have helped me become familiar with the the structure, features and a range of uses of NVivo; I could doubtless bring it to bear on my research and make (fairly) effective use of it. Although I’ve given it a good test drive whilst researching the literature and as part of my pilot methods study, I kept hitting snags and was convinced I wasn’t getting the most from it. More worryingly, I was concerned that I might set up a poor structure that would later require me to dismantle it and thereby increase my workload unnecessarily. The question was though, whether a course titled ‘Introduction to NVivo’ might be aimed at people with less experience. I needn’t have worried.
Structure of the day.
With its full title of ‘Introduction to NVivo: Building your database’ we can see how important this course will be in addressing the fundamental aspects of using NVivo. It was with this in mind that Ben devoted the initial session to discuss why we might want to use a computer to assist our qualitative analysis in the first place, and what the implications of that choice might be. He then asked to consider what data we were likely to be collecting and how they might be thought of as ‘cases’ so that we would be in a position to design a structure to organise them within NVivo. Time invested in this initial planning would pay dividends in later stages when we needed to conduct and test different levels of analysis across and through our data. Another crucial element in the design and build of our database is the analytical strategy we intend to adopt. This is where I became a little unstuck, as this isn’t an area I’ve yet devoted sufficient thought to, beyond using open coding, developing categories and themes, and synthesising the emergent concepts. I used constructivist grounded theory in an earlier study, but hadn’t really moved on from there. This session above all else forced me to confront my choices and whether framework analysis, content analysis, discourse analysis, interpretative phenomenological analysis, or indeed grounded theory are consistent with my epistemology. At the moment, I see virtues in several approaches and have to resolve whether it’s possible to draw what might be useful from each, or whether they are completely incompatible. More thinking still to do.
Having collected, analysed and interpreted our data, the next stage will be to report our findings. Within that reporting, in addition to the insights we developed, we will need to present and justify the decisions we made; once more NVivo can help with that process and help us to construct a more robust and credible thesis. By annotating, memoing and reliably entering pertinent metadata, NVivo can help us maintain a transparent audit trail connecting the findings we present back through the decisions we took, to the data where those seeds were sown.
We finally launched NVivo after lunch, when we were given the opportunity to put into practice much of what we’d discussed earlier. Using the data from an actual piece of research, Ben introduced us to all of the features and capabilities within NVivo which would allow us to implement the ideas we had developed in the morning. With the ‘why’ in place, the ‘how’ now made far more sense.
What I came away with.
Although my awareness of the NVivo environment and the tools within it didn’t advance far, instead I gained something of far greater value. I now have a much better appreciation of what my database could look like and how to begin setting that up. I know that my planning will be key and that that will have to be informed by the analytical strategies I choose.
As one might expect, we were provided with a set of resources we can refer back to; a workbook (printed and pdf) and data we can use to practice our technique. Less usual was the offer of access to ongoing support for the duration of our projects. We can get additional personal advice and assistance when setting up our projects or if we encounter any problems, either by email or when necessary through Skype and remote desktop sharing. Incredibly generous, a potential lifesaver, but precisely the kind of safety net many of us will need as our research unfolds.
What other people thought.
It was clear from the initial introductions that people were at a range of different places in both their research and their capability with NVivo. That’s very difficult for the person at the front to cater for, and yet I got the impression from those I talked with and from the gratitude people expressed at the close of the course, that most people, like me, had gained a great deal. Ben obviously knew his stuff and was able to draw from a wide range of experiences when answering queries from the audience. He never once sugar-coated things, nor over-evangelised the virtues of NVivo, but what he did do was set out the questions we need to answer and a path to follow when we have those answers.
1BAZELEY, Patricia, and JACKSON, Kristi, (2013). Qualitative data analysis with NVivo. Second edition / Pat Bazeley and Kristi Jackson.. ed., London, SAGE.
BAZELEY, Patricia, et al. (2000). The NVivo qualitative project book. London, SAGE Publications; SAGE.
GIBBS, Graham (2002). Qualitative data analysis: Explorations with NVivo (Understanding social research). Buckingham: Open University Press.
RICHARDS, Lyn (1999). Using NVivo in qualitative research. Sage.