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Analyzing Qualitative Data
July 18, 2014
I recently read a great paper by Rettie et al. (2008) which, although based on a small sample size, found that only 9% of UK market research organisations doing qualitative research were using software to help with qualitative analysis.
At first this sounds very low, but it holds true with my own limited experiences with market research firms, and also with academic researchers. The first formal training courses I attended on Qualitative Analysis were conducted by the excellent Health Experiences Research Group at Oxford University, a team I would actually work with later in my career. As an aforementioned computer geek, it was surprising for me to hear Professor Sue Ziebland convincingly argue for a method they defined as the One Sheet of Paper technique, immortalised as OSOP. This is essentially a way to develop a grounded theory or analytical approach by reducing the key themes to a diagram that can be physically summarised on a single piece of paper, a method that is still widely cited to this day.
However, the day also contained a series of what felt like ‘confessions’ about how much of people’s Qualitative analysis was paper based: printing out whole transcripts of interviews, highlighting sections, physically cutting and gluing text into flipcharts, and dozens and dozens of multi-coloured Post-it notes! Personally, I think this is a fine method of analysis, as it keeps researchers close to the data and, assuming you have a large enough workspace, it lets you keep dozens of interviews and themes to hand. It’s also very good for team work, as the physicality gets everyone involved in reviewing codes and extracts.
In the last project I worked on, looking at evidence use for health decision making we did most of the analysis in Excel, which was actually easier for the whole team to work with than any of the dedicated qualitative analysis software packages. However, we still relied heavily on paper: printing out the interviews and Excel spreadsheets, and using flip-chart paper, post-its and marker pens in group analysis sessions. Believe me, I felt a pang of guilt for all the paper we used in each of these sessions, rainforests be damned! But it kept us inspired, engaged, close to the data and let us work together.
So I can quite understand why so many academics and market research organisations choose not to use software packages: at the moment they don’t have the visual connection to the data that paper annotations allow, it’s often difficult to see the different stages of the coding process, and it’s hard to produce reports and outputs that communicate properly.
The problem with this approach is the literal paper-trail – how you turn all these iterations of coding schemes and analysis sessions into something you can write up to share with others in order to justify how you made the decisions that led to your conclusions. So I had to file all these flip-charts and annotated sheets, often taking photos of them so they could be shared with colleagues at other universities. It was a slow and time consuming process, but it kept us close to the data.
When designing Quirkos, I have tried in some ways to replicate the paper-based analysis process. There’s a touch interface, reports that show all the highlighting in a Word document, and views that keep you close to the data. But I also want to combine this with all the advantages you get from a software package, not least the ability to search, shuffle dozens of documents, have more colours than a whole rainbow of Post-it notes, and the invaluable Undo button!
Software can also help keep track of many more topics and sources than most people (especially myself) can remember, and if there are a lot of different themes you want to explore from the data, software is really good at keeping them all in one place and making them easy to find. Working as part of a team, especially if some researchers work remotely or in a different organisation can be much easier with software. E-mailing a file is much easier than sending a huge folder of annotated paper, and combining and comparing analysis can be done at any stage of the project.
Qualitative analysis software also lets you take different slices through the data, so you can compare responses grouped by any caracteristics for the sources you have. So it's easy to look at all the comments from people in one location, or between a certain age range. Certainly this is possible to do with qualitative data on paper as well, but the software can remove the need of a lot of paper shuffling, especially when you have a large number of respondents.
But most importantly, I think software can allow more experimentation - you can try different themes, easily combine or break them apart, or even start from scratch again, knowing that the old analysis approach you tried is just a few clicks away. I think that the magic undo button also gives researchers more confidence in trying something out, and makes it easier for people to change their mind.
Many people I’ve spoken to have asked what the ‘competition’ for Quirkos is like, meaning, what do the other software packages do. But for me the real competitor is the tangible approach and the challenge is to try and have something that is the best of both worlds: a tool that not only apes the paper realm in a virtual space, but acknowledges the need to print out and connect with physical workflows. I often want to review a coded project on paper, printing off and reading in the cafe, and Quirkos makes sure that all your coding can be visually displayed and shared in this way.
Everyone has a workflow for qualitative analysis that works for them, their team, and the needs of their project. I think the key is flexibility, and to think about a set of tools that can include paper and software solutions, rather than one approach that is a jack of all trades, and master of none.