Dr Emily Stapley contributes today’s guest post. Emily is a Qualitative Research Fellow in the Evidence Based Practice Unit (EBPU) at the Anna Freud National Centre for Children and Families and UCL. EBPU is a child and youth mental health research and innovation unit.
The blog focuses on some of the ways in which Emily and her colleagues are approaching the analysis of interview data from a five-year qualitative longitudinal (QLR) study. The work is part of the evaluation of HeadStart; a five-year, £56 million National Lottery funded programme set up by The National Lottery Community Fund to explore and test new ways to improve the mental health and wellbeing of young people aged 10 to 16 and prevent serious mental health issues from developing. Six local-authority-led partnerships in Blackpool, Cornwall, Hull, Kent, Newham and Wolverhampton are working with local young people, schools, families, charities, and community and public services to make young people’s mental health and wellbeing everybody’s business.
Analysing young people’s experiences of coping with problems, difficult situations and feelings: An evolving approach to analysing qualitative longitudinal evaluation data
The aim of our study is to explore young people’s experiences of coping with and receiving support for problems and difficult feelings or situations over a five-year period. The young people invited to take part in our study were those who were already receiving support from HeadStart or those who might do so in the future. Participants were in Years 5 or 7 at school (age 9 to 12) at the start of the study and (we hope!) will continue to be involved until they are in Years 9 or 11 (age 14 to 16). Working with two colleagues in the EBPU and at the University of Manchester (both of whom are PhD students), we are conducting semi-structured interviews once a year with approximately 80 young people (10 to 15 at each HeadStart partnership).
I decided to conduct a cross-sectional thematic analysis of the interviews in the first year, drawing on Braun and Clarke’s (2006) methodology. This decision was made in the context of the fact that:
- We were working with such a large dataset (82 interviews);
- We had always intended to present the themes arising across the dataset in the first year of the project, as a baseline report for the study as a whole (see Stapley and Deighton, 2018).
We took a team approach, using the qualitative data analysis software package NVivo (v11) to facilitate our analysis of the wave 1 dataset. As part of this process, I initially developed a thematic framework relating to our research questions by coding 80% of the interview transcripts. This involved giving brief labels to the extracts of the interview transcripts that related to our research questions, which described the content of the extracts, and then grouping all extracts with similar labels or codes together to form themes. The other two members of our team then each coded the remaining 20% of the transcripts using my thematic framework. This resulted in refinements and additions being made where necessary to the thematic framework.
At the outset of the study, we made a pragmatic decision to analyse the data drawing on the interviews across the HeadStart partnerships, rather than to conduct individual pieces of partnership-specific analysis. This speaks to our remit as the HeadStart Learning Team responsible for the national evaluation of the programme, whereas site-specific qualitative data collection and analysis is being conducted locally by the individual partnerships. However, we did explore which themes from our analysis described above could be seen specifically in the interviews from each partnership (i.e. across all of the interviews in a given partnership, which themes from our thematic framework were present and which were not?). There was relatively little variation between the partnerships, in terms of the themes from our thematic framework that could be seen specifically in their interviews. Ultimately, any decision to bring together the national and locally-collected qualitative datasets will be influenced by the degree of heterogeneity in our aims/research questions, our capacity, and the instigation of appropriate data sharing agreements.
Following our initial analysis of the wave 1 dataset, we had a decision to make in the second year about how to conduct diachronic analysis across waves 1 and 2. Sources such as Grossoehme and Lipstein (2016) have been helpful in thinking about this. We are currently planning to use typology methods, such as ideal-type analysis, to explore the patterns or ‘types’ evident in the young people’s experiences and perspectives, and the potential shift in this across the two years. For instance, do the young people (individually and in general across the sample) exhibit different patterns of coping behaviour and support use in the second year of the study, as compared to the first year, and why? What are the mechanisms or factors behind changes in the young people’s wellbeing across the first and second years of the study? The ideal-type analysis process typically begins by the researcher(s) writing a ‘case reconstruction’ of each interview, in our case a summary of the content of each transcript. These case reconstructions are then systematically compared with each other by the researcher(s), which leads to the formation of a number of broadly similar groups of case reconstructions or, in other words, interviews representing similar types of experience (e.g. Stapley et al., 2017).
We are now about to go into wave 3, our third year of data collection, and are really looking forward to seeing our participants again, as they grow older and have new experiences, opinions and perspectives. The growing size of the dataset as we accumulate more interviews each year means that establishing clear baselines in our analysis at the outset of the study will be important to direct our focus over the course of the study. At this early stage, I would envisage our analytic approach evolving over time, depending on the findings from our analysis at each wave and the topics raised by the young people during data collection.
Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77-101.
Grossoehme, D. and Lipstein, E. (2016). Analyzing longitudinal qualitative data: The application of trajectory and recurrent cross-sectional approaches. BMC Research Notes, 9, 1-5.
Stapley, E. and Deighton, J. (2018). HeadStart Year 1: National Qualitative Evaluation Findings – Young People’s Perspectives. London: CAMHS Press.
Stapley, E., Target, M., and Midgley, N. (2017). The journey through and beyond mental health services in the United Kingdom: A typology of parents’ ways of managing the crisis of their teenage child’s depression. Journal of Clinical Psychology, 73, 1429-1441.