As an example of my methodological process, something I had planned to do from the outset was to find out how Dianetics (the “Bible” of Scientology) had appeared, been received, and mutated since it first emerged in the May
nineteen-fifty edition of Astounding Science Fiction (Fig. 17). I looked in the .xlsx file of my word frequency search created by using the R statistical package, and found three hundred and eighty-nine occurrences of Dianetics and Scientology. I now needed to know the context of
these was significant, or my major exposé would be merely an embarrassing wardrobe malfunction. I experimented using the built-in Spotlight search engine on my Mac to find files that contained these words. This works, but I had to open up the files in a text editor and search again to find the words in their original context. For proper analysis it would be necessary to go to the hard copy (if I had it) or find the photographs of the digital copy to see it in the original form. Possible, but laborious. I explained my struggle to the colleague who had originally suggested I do a PhD. She was analysing survey transcripts and suggested I use Nvivo. I concealed my ignorance by nodding sagely, and googled it afterwards, as had become my customary practice. It seems in retrospect that I was, even if more often by accident than design, “allowing the theory to emerge as necessary” in a structured framework, as described by Debbie Richards
127 (Richards, 2009: 2:2). I was learning something from my isolated attempts to build a coherent and effective solution to my research problem.
In common with other CAQDAS applications, NVivo stores files in a database and provides various ways of searching, coding and linking the content of these files. It includes some tools, of varying degrees of complexity and comprehensibility, for visualising the results. NVivo can create word clouds without having to put text into an online system such as Wordle, even if the results are not as pretty. But “pretty” is not often associated with “respectable” in academia, as I had already discovered (Billig, 2013, Sand-Jensen, 2007), and my work might seem more rigorous now that I was not cleaning up my text before analysis. It was about this time that I started using Gephi (Bastian, Heymann and Jacomy, 2009). I stopped using it shortly afterwards, along with all the other CAQDAS I had been experimenting with, for two reasons: one was that I had run out of friends to help me get them working properly, the second was that previous experience suggested I might spend three weeks getting something to work in Gephi that was either completely unnecessary, or could be done in five minutes using a different tool. I was also running out of time, and the university had a licence for NVivo. After downloading try-before-you-buy versions of the alternatives in my search for the perfect CAQDAS, and others which are freeware, I decided I might as well use what was routinely available, and moreover had a modicum of technical support and local knowledge.
I discovered that, although NVivo does a good job of managing a library of resources, and coding them, and enables text-in-context searches and word frequency analysis, it is very slow when you ask it to work with more than a hundred files at once. I used to think it had crashed when I asked it to find, for instance, all the occurrences of the word “robot” in my collection. I recognised that NVivo suffers from the problems of software that falls into the “bloatware” category – including lots of features, at the expense performance. For example, NVivo opens another program (Libre Office) on a Mac each time it opens a text file, and closes it again afterwards. This is like going to the shops to buy a toothbrush and toothpaste every single time you brush your teeth. I became accustomed to setting up a search and letting it run overnight. To someone who grew up with computers in the eighties this should not seem troublesome, but I am now,
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just like everyone else, impatient and have unreasonable expectations.
Fig. 18. What do you want to be when you grow up?
I explored the features of NVivo – word trees, webs that show links between tagged words and other words, codes and other codes – but realised that these were not necessary. I only needed to identify issues that contained relevant text, although I did create some word clouds with NVivo that seemed meaningful (Fig. 18 – the results of a question on preferred profession included as part of my first survey, and perhaps influenced by the number of responses from the Science Fiction and Fantasy Writers of America group). When I asked myself whether my approach was “fit for purpose,” I realised that what I had started out wanting to do – examining the evidence for cultural links between SF content and the real-world context – was strongly grounded in English literature and the social sciences. I had, after all, only been looking for a way to find the text I wanted to read and discuss – and the software wasn’t going to do this for me. This was an epiphany. My obsession with software and the possibilities for the digital
analysis of texts had blinded me to the fact that I only needed the technology to help me do what I had originally intended – a thematic analysis of objectively chosen sources. On the way to this realisation I had learned a great deal about my capacity for
frustration, but the CAQDAS requirements were not as complex as I had tried to make them. I immediately employed this primitive understanding to write a simple (but a little
129 provocative) paper, published in a friendly (but somewhat obscure) journal, to
demonstrate that I had achieved something (Menadue, 2017b).