Georgia Philip, a Senior Research Associate in the School of Social Work, at the University of East Anglia, writes today’s insightful post. Georgia has expertise in the areas of: fathers, gender and care, qualitative and feminist research, the feminist ethics of care, parenting interventions and family policy.
In this post, Georgia reflects on the challenges of managing the volume and depth of data generated in a qualitative longitudinal analysis of men’s experiences of the UK child protection system. The study was conducted with colleagues John Clifton and Marian Brandon.
Working with qualitative longitudinal data
For the past two years I have worked with colleagues John Clifton & Marian Brandon on a qualitative longitudinal (QL) study of men’s experiences of the UK child protection system.
Alongside the twists and turns of the research relationships developed with our participants and the conceptual work involved in presenting their accounts, we have also encountered practical challenges of managing the volume and depth of data generated. This post briefly identifies some of these challenges, and our responses to them.
Our QL study involved 35 men who were fathers or father figures to a child with a newly made child protection plan, recruited between April and August 2015, and taking part for a period of 12 months. The study consisted of two in-depth interviews, at the start and end of the study period, and (approximately) monthly phone contacts with each man. Twenty-eight men participated for the full 12 months. We took a holistic approach, looking back at men’s histories, relationships, fathering experiences and any past encounters with welfare agencies, and then accompanying them forward, into the current encounter with child protection and its impact on their lives.
Our overall approach to the analysis was inductive and iterative, drawing on existing QL methodological literature (Neale, Henwood & Holland, 2012). It also engaged us in thinking about ‘time’ in theoretical and methodological terms: as a concept, that shapes how lives are lived, narrated and imagined, and as a resource for examining a significant local authority process. Our practical approach to the management of the high volume of data was a combination of pre-emptive and responsive strategies. Three challenges we encountered were, how to analyse across and within our sample; how to facilitate data sharing across the research team; how to combine analysis of men’s lives, and of the child protection system, in coherent way.
Early on, we decided to use NVivo Frameworks as a mechanism for managing the data (NatCen 2014, Ritchie et al 2014), and we constructed a matrix to record aspects of men’s lives, and of the unfolding child protection process. This enabled us to collate and analyse data from the outset rather than separating (and delaying) analysis from data collection. It also established a process for organising the data using the ‘case and wave’ approach adopted in other QL studies (Hughes and Emmel, 2012; Thomson, 2007) to look across the sample by time wave (we divided our 12 months into four three-month periods), and within it, at each man’s individual ‘case’ However, whilst NVivo allowed us to develop a way of structuring our analysis, it did not, in practice, facilitate a reliable way of collaborating across the research team.
As the researchers, John and I had a group of men and an accumulating data set that we ‘knew’ better. This meant we needed to develop ways of sharing cases and checking our developing analysis, to build an integrated and credible understanding of the sample as a whole. We found that working independently on, and then trying to merge, copies of our NVivo project just wasn’t viable, and the project files were unstable. Therefore we had to devise, or revert back, to other strategies for managing this. We continued using our original matrix, to summarise data over the four time waves, and to help compile the individual case studies, but did this using Word and sharing via a secure drive on the University network. We met monthly as a full team to discuss and compare our analysis, understand the developing cumulative picture, and review the ongoing process of data gathering. We also came to make extensive use of memo writing as a particularly useful means of condensing data, exploring pertinent issues within it, and discussing these with each other. We then took the decision that John and I each take the lead in analysing one of the two main domains of the data: men’s encounter with the child protection process and their wider lives as fathers. This ensured that we both had to fully consider all participants’ data and actively collaborate on integrating our work as part of the later, conceptual stages of the analysis.
This project has been intensely demanding and satisfying, at every stage. Finding ways of coping with rich, accumulating data, generated with increasing momentum as research relationships develop, has been just one of these demands. Being committed to an inductive approach, which does justice to the men’s own accounts, whilst also generating a coherent conceptual explanation and meaningful practice messages for social workers, is another. What we have offered here is a tiny glimpse into some of the practical strategies for meeting such multiple demands, which we hope may be useful for other researchers new to QL research.
Our full report will be available from the Centre for Research on Children and Families, from September 2017.
NatCen (2014) Frameworks in NVIVO manual- Step by step guide to setting up Framework matrices in NVIVO. London: NatCen Social Research.
Neale, B, Henwood, K & Holland, J (2012) Researching Lives Through Time: an introduction to the Timescapes approach, Qualitative Research, 12 (1) 4-15
Ritchie, J Lewis, J McNaughton Nicholls, C Ormston, R (2014) Qualitative research practice: A guide for social science students and researchers London: Sage.
Thomson R (2007). The qualitative longitudinal case history: practical, methodological and ethical reflections. Social Policy and Society 6(4): 571–582.