Author: Helen Marshall

The data

The data in the NVivo project sometimes offered a detailed picture of a complaint, and sometimes allowed only a glimpse of the matter. It consisted of summaries and partial transcripts made by Sara of all complaints received by the Office of Equal Opportunities Victoria (EOCV http://www.humanrightscommission.vic.gov.au/Home.asp), over a three month period.

Material on the type of complaint, the outcome, the gender of the parties, and very general characteristics of the workplace, was recorded by the EOCV as a matter of course in 2004. Other characteristics of the parties and complaint matter were collected from the files. The amount of detail available for a particular complaint varied. Sara used SPSS to record the 27 demographic variables in the Complaints project.

Some of these demographic variables were put into the NVivo casebook (a table where the researcher can store material on the characteristics of the cases) by Sara when she set up the project. Others were imported as the re-analysis began. They came in from SPSS via Excel. Sara saved a copy of her SPSS database as an Excel workbook and emailed it me (I did not have a copy of SPSS on my computer). I used the process that Pat Bazeley suggests (Bazeley 2007 pp140-2), to import the extra variables. And I discovered that Pat's advice about the importance of making sure that the names of cases are absolutely consistent between your original data base and NVivo is crucial. (You can get the same good advice from Lyn Richards' tutorials on using NVivo, from the NVivo help files, and from any trainer. I had removed or renamed some sources in the project during the tidying up but forgot to have the same names for sources in the Excel workbook, and it took several hours' work and a lot of swearing to discover and fix the mess! The lesson I learned here is to take it slowly when preparing any material to import. I'd already learned that it is easier to use Word than NVivo for formatting or altering sources (e.g. when substituting pseudonyms for real names in transcripts). From now on, I'll take the same care with all items that I might want to bring in. The quantitative data was easy to record and comprehend (once I heeded the advice on consistency).

The qualitative material that is the core of the complaints project is much more complex. First, the sources are not 'raw' in the same way as encoded interview transcripts. As noted in the comments on setting up the project, the material in EOCV files is highly confidential. Sara could not simply take copies of the material; she had to work at the EOCV offices, and return the files as she read them. So the sources in the NVivo project are Sara's short quotes and paraphrases of the documents in each file.

With limited time for the initial project, Sara found it easier to 'talk' the data than type it. She used Dragon Dictate voice recognition software that she had already trained. (http://voicerecognition.com/dragon/d_personal.html) to transcribe her vocal notes. This worked well. While there are certainly still some computer-generated mistakes in the sources, they seem to be minor ones and I had no difficulty understanding the sources.

Second, within every complaint that Sara's literal voice has turned into a source, there are the metaphorical 'voices' of at least three actors - the complainant, the respondent and the commission. Sometimes the 'voice' of the complainant or respondent was being relayed by an advocate (such as union official or a lawyer). Sometimes the voice was responding to another voice- as when a complainant made a rejoinder to a respondent's reaction to the original complaint. This is a project full of voices! In the description of working with data you can read about how the plethora of voices was eventually reduced to three 'speakers' the complainant, the respondent and the commission.

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