Dr Åsa Audulv, lecturer in the Department of Nursing Science, Mid Sweden University, Sweden and School of Occupational Therapy, Dalhousie University, Canada has written today’s guest post. Åsa has conducted qualitative longitudinal research (QLR) into self-management among people with long-term health conditions. With colleagues she is currently working on a literature review of QLR methods and in today’s post she draws on their preliminary findings to highlight the lack of transparency around approaches to QLR analysis in health research publications.
Be transparent (and proud) – How can we better describe the practice of qualitative longitudinal analysis?
About 12 years ago I started a QLR project as part of my PhD work. At that time, I knew little about the traps, tricks and rewards of longitudinal analysis. I basically had the idea that our phenomenon of interest – the self-management of long-term health conditions – changed through an individual’s illness trajectory and, because existing research heavily relied on one-time interviews, I thought a longitudinal design would provide more insight. In short, data collection with four interviews per participant, spanning over two years seemed like a design that could contribute new knowledge. I understood that this design could result in around 70 interview transcripts to analyze and, at that time, I only had vague ideas about how that analysis might be conducted.
Over the past year I have been working with colleagues on a literature review concerning different methodological approaches to QLR analysis. Our inclusion criteria have been articles within the field of health research, collecting qualitative data at several time-points. After reading 52 articles one thing that surprised us was how little was conveyed about the longitudinal aspects of the analysis. In total, 57.6% (30 articles) did not mention how they had managed the longitudinal aspects. For example, they did not say anything about time point, change, or comparison over/through time-points in their analysis section. Since, the body of QLR work is small in comparison to qualitative studies it is possible that many authors were more used to describing approaches to analyzing one-time data-collection studies and therefore did not really know how to outline the longitudinal aspect in their analysis. The limited amount of methodological literature might also add to this uncertainty. Further, it is possible that most peer-reviewers of QLR papers are experts in the substantive focus of the work, rather than on QLR methodology, so they might not spot this aspect during the peer-review process. There might also be pragmatic reasons, like limited space available. However, the fact that QLR studies are complicated and relatively unusual would add to the importance of explicit analysis descriptions regarding how such analysis was conducted.
In our review, 22 articles (42.3%) had some description of how they analyzed the longitudinal aspect. However, the clarity and depth of the descriptions varied. Some described the longitudinal aspect as an integrated part of their whole analysis. These projects were often centered around investigating change. They typically described their analysis in several steps where the longitudinal aspects were included in almost every step. For instance, Johansen and colleagues (2013) conducted a study about addicted individuals’ social motivations and non-professional support. In their description of the analysis the longitudinal aspect was well integrated (the bold indicates the longitudinal aspects):
“…, we first conducted an open coding of the data from phase 1 and phase 2. Next, we used the framework analysis method to track changes over time , and facilitate axial coding and constant comparison. Relationships between the codes were explored throughout all three phases of the study and individual changes were covaried with dyadic events and events involving relationships with other people representing network support for either using or non-using. As such, narrative analyses were conducted for all dyads to capture details about the support process and its consequence for recovery. In this way, we were able to describe the support dynamics of each dyad, explore how the support was influenced by characteristics of the individual members and support arrangement, and theorize about the ways this affected recovery. In addition to the tracking of thematic changes, we also utilized proportions as indicators of change ” (Johansen, 2013, p.233)
Other studies described the longitudinal aspects as one isolated step, often at the end of the analysis description. This suggests that the first part of the analysis had been conducted with a focus on the phenomena with the longitudinal aspects brought in at a later stage to deepen the understanding and/or add another perspective. For example, do Mackintosh-Franklin et al (2014) describe:
“Findings from each interview stage were analyzed separately, and only after separate analysis had taken place were both data sets combined for final analysis. Findings reported below are from the two final stages of this analytical process, using separate and combined interview sets.” (Mackintosh-Franklin, 2014, p.202)
Some articles mentioned a longitudinal dimension to the analysis, but were not specific about how that analysis was conducted. For example, Salter et al (2014, p.2) describe: “Iteration between both data sets and the research literature helped inform the analysis at the explanatory level.” Several studies described the use of tools and/or analysis strategies that are often employed for analyzing longitudinal aspects. For example, matrices, flow charts, and/or comparing across parts or interviews. Some described these tools and analysis strategies clearly. However, it is more common that they are mentioned in passing and the reason and outcomes of using these practices remain unclear. For example, one article mentioned the use of matrices but did not describe if those matrices were compared to time-points, cases or both.
In conclusion, as the other blogs in this collection have shown, there are different ways to analyze QLR data, and thus different ways of describing the qualitative longitudinal aspects of analysis. First, we need to be clear about what aspects of a project are longitudinal and how we are going to analyze them. Secondly, by being transparent in our description of how we conduct the analysis we can make our approach and our justification for that approach clearer. In turn, that will make it easier for our readers to evaluate the quality of our work. In our review, 57.6% of the articles lacked a description of how they analyzed time in their QLR. I would argue that would be 30 articles too many. A third reason to clearly describe the longitudinal aspects of an analysis is to raise awareness of our work. We should be proud of the approach we use. QLR opens up a wide range of possibilities. It can help us better describe our phenomena of interest, and collect richer data. By writing a succinct analysis section we are giving an example of how it can be done, teaching others about QLR, and showing the merits of such approaches. My longitudinal data collection lasted for two and a half years and included 81 interviews that generated 726 single-spaced transcribed pages. Eventually, it was presented in two research papers (Audulv, Asplund, Norbergh, 2012; Audulv, 2013) and I still think it was a rather cool project.
Audulv, Å., Asplund, K. and Norbergh, K-G. (2012) The process of self-management integration. Qualitative Health Research. 22(3), 332-345
Audulv, Å. (2013). The over time development of chronic illness self-management patterns: a longitudinal qualitative study. BMC Public Health, 13:452
Johansen, A.B., Brendryen, H., Darnell F.J. and Wennesland, D.K. (2013). Practical support aids addiction recovery: the positive identity model of change. BMC Psychiatry 13:201
Mackintosh-Franklin C. (2014). The Impact of Experience on Undergraduate Preregistration Student Nurses’ Responses to Patients in Pain: A 2-Year Qualitative Longitudinal Study. Pain Management Nursing, 15, (1): 199-207
Salter C, McDaid L, Bhattacharya D, Holland R, Marshall T, et al. (2014) Abandoned Acid? Understanding Adherence to Bisphosphonate Medications for the Prevention of Osteoporosis among Older Women: A Qualitative Longitudinal Study. PLoS ONE 9(1): e83552.