My colleagues and I worked on the ‘TBI experiences’ study1 – qualitative longitudinal research (QLR) about recovery and adaptation after traumatic brain injury (TBI). Led by Kathryn McPherson and Alice Theadom, we came to QLR as qualitative researchers who saw a need to capture how recovery and adaptation shifted and changed over time, in order to better inform rehabilitation services and support.
For QLR, our data collection period of 48 months was relatively short. Our focus was on understanding what helped or hindered recovery and adaptation for people with TBI, and the significant others in their lives. However, with 52 participants (plus their significant others), the volume of data was significant. We interviewed participants at 6, 12, 24 and 48 months after a TBI, and at 48 months we had a subset of participants with diverse experiences. The focus for our analytical approach was a type of thematic anaylsis based on Kathy Charmanz’s writing on grounded theory. The purpose of our research was to build a picture of what recovery and adaptation looks like for a cohort of people over time. While we did do some analysis of ‘case sets’ (the series of interviews relating to a particular person), the focus of the analysis was more on looking at patterns across the participant group rather than individuals.
Making sense of a large amount of rich data is always challenging, but the added dimension of change over time is something we spent a lot of time pondering. One of the biggest challenges was to find strategies to make the changes we were interested in visible in our coding structure, so we could easily see what was happening in our data over time. We chose to set up an extensive code structure during analysis at the first time-point and work with this set of codes throughout, adapting and adding to them at further time-points. We reasoned this would enable us to track both similarities and differences in the ways people were talking about their experiences over the various timepoints. Doing this made it possible to map the set of codes themselves as a way of seeing changes over time.
We used detailed titles for the codes and comprehensive code descriptions that included examples from the data. At each time-point, the code descriptions were added to and consideration was given to which codes were outdated or had shifted. For example, a code we labelled at 6 months as: ‘allowing me to change what I normally do to manage symptoms and recover’ needed extensions to the code description at 12 months to reflect subtle changes. Beyond that, although data still fitted with the essence of the code that had been developing over time, we began to question the appropriateness of the code title, as the later data related to the same idea but was no longer about managing symptoms, rather navigating the need to do things
differently than before the injury to cope with changes.
This way of working with the code enabled us to reflect on the experience for participants. At the 24 month point, the original code was ‘in transition’ – not quite a new code yet, but different enough to be an uncomfortable fit with the original title. The description now included this query to help us reconsider it in light of new data in the future.
When analysing interviews at 48 months, the data related to this idea had changed and no longer fitted the existing code title or description. We needed to consider introducing a new code, one that had a key relationship with the existing one but captured the essence of our findings more clearly. Essentially, the idea of ‘changing what I normally do’ had expired, because there was less tendency to refer to pre-injury activities as ‘what I normally do’. However, negotiating having to do things differently in order to manage life was still an issue for participants experiencing ongoing effects. The changes in codes over time and the relationships between ‘old’ and ‘new’ code were very visible using this system.
The extensive code descriptions helped orientate us to the interview extracts that were most influential in shaping the code, and the database we set up to record our coding allowed us to create reports of every extract coded here, so we could review and debate changes with reference to key data and the ‘feel’ of what was coded. Another key strategy we used to help us explore data over time was the use of data visualisation software. We used QlikSense, which is designed for exploring patterns in data and then directly drilling down into the relevant detail to look at what’s going on (as opposed to seeing an overview, which we did on paper). One example is where codes and groups of codes varied in their prominence (e.g. code density or number of participants who contributed to the code) across different timepoints. Seeing these differences prompted us to look at the code descriptions and the data coded there to consider if this pattern added to our understanding of how people’s experiences were changing over time. We provide some more detailed examples of the patterns we explored in a paper published in Nursing Inquiry in 20172.
At the start of our study, we had limited understanding about the challenges ahead because of the nature of QLR, but in working it out by doing it, we saw the value of such an approach – so much so, that some of the other authors have since been involved in other QLR projects.
References
1 Fadyl, J. K., Theadom, A., Channon, A., & McPherson, K. M. (2017). Recovery and Adaptation after Traumatic Brain Injury in
New Zealand: Longitudinal qualitative findings over the first two years. Neuropsychological Rehabilitation (open access)
2 Fadyl, J. K., Channon, A., Theadom, A., & McPherson, K. M. (2017). Optimising Qualitative Longitudinal Analysis: Insights from a Study of Traumatic Brain Injury Recovery and Adaptation. Nursing Inquiry, 24(2).