By Kristy Revell, Agent and PhD student, University College London

I recently decided to use a qualitative, data-analysis software package called Atlas.ti, which allows researchers to methodically uncover observed phenomena in their data. I attended a training course at Surrey University as part of my work with the Agents network, and have since found that the software saved time, identified new relationships in my codes and confirmed my suspicions about the theory that is emerging from my data.

As part of my research, I have been conducting semi-structured interviews with (environmental) sustainability officers who work for local authorities. The interviews aimed to collect data on the sustainability projects being delivered by local authorities, and the actions that these local authorities are taking to help residents to transition to a more sustainable lifestyle.

In November, I began looking for a way to analyse the data that I had collected. I've not analysed interview data before, so I started by familiarising myself with the literature and the different approaches I could adopt. I decided to use a grounded theory approach to the analysis, which involves coding the data and theory building. In this case, coding is the "means of categorising segments of data with a short name that simultaneously summarises and accounts for each piece of your data" [Charmaz, 2006]. The main reason that I chose the grounded theory approach is that it "does not begin with prior assumptions about hypotheses, research questions or what literature should underpin the study" [Gray, 2004]. As a result, I felt that this approach was particularly suitable for my area of research, which has an identifiable gap in knowledge, but lacks a clear route to how we can better understand the phenomena.

I used Atlas.ti to assist the coding of the interviews. After the initial coding phase, which involves working through the transcripts or notes from each interview, and applying appropriate codes to the text, I continued to comb the data until my codes fitted all of my data sets. This iterative process lead to the redefining of some of the codes, the creation of some new codes and the removal of some others. Luckily, changes are easy to make with Atlas.ti because you can delete, add, move, change and rename the codes with ease. This is a real time saver, and it is this that makes coding software like Atlas.ti so useful. It was certainly easier than the old-fashioned way of coding using pen and paper - a method that is still commonly employed.

Another great thing about Atlas.ti is the co-occurrence table (or explorer) lets you see which codes are co-occurring and at what frequency they co-occur. You can also get an indication of how significant these co-occurrences are from the generated co-occurrence coefficient. For me, the co-occurrence table identified relationships between my codes which I hadn’t necessarily noticed myself; it also confirmed some of my own suspicions about the theory emerging from the data.

Overall I found Atlas.ti to be really useful software that can help you make sense of your data in a systematic way, whilst uncovering hidden relationships in your data. It also makes it very easy to revisit codes and quickly re-locate them as you formulate more complex hypotheses from the data.

References:

Gray, D.E., (2004) Doing research in the real world. London: Sage.

Charmaz, K., (2006) Constructing grounded theory. A practical guide through qualitative analysis. London: Sage.