The challenges of data management and analysis on a large longitudinal qualitative research project
Computer aided qualitative data analysis has the potential to revolutionise both the scale of research and possible analysis techniques. Yet, the software itself still imposes limits that hinder and prevent this full potential from being realised. This post looks at the large and complex dataset created as part of the Welfare Conditionality research project, the analytical approach adopted, and the challenges QDAS faces.
The Welfare Conditionality project has two broad research questions in setting out to consider the issues surrounding sanctions, support, and behaviour change. Firstly, is conditionality ‘effective’ – and if so for whom, under what conditions, and by what definition of effective. And, secondly, whether welfare conditionality is ‘ethical’ – how do people justify or criticise its use and for what reasons. To answer these questions, we have undertaken the ambitious task of collecting a trove of qualitative data on conditional forms of welfare. Our work across nine policy areas, each of which has a dedicated ‘policy team’ that is responsible for the research. The policy areas are: unemployed people, Universal Credit claimants, lone parents, disabled people, social tenants, homeless people, individuals/families subject to antisocial behaviour orders or family intervention projects, (ex-)offenders, and migrants. Research has consisted of 45 interviews with policy stakeholders (MPs, civil servants, heads of charities), 27 focus groups with service providers, and three waves of repeat qualitative interviews with 481 welfare service users across 10 interview locations in England and Scotland.