Transitioning from QUAL to mixed methods: Teething Problems and Pragmatic Solutions in Researching Public-Sector Data Sharing in the UK

Socio-legal studies are today more methodologically diverse than ever: not only do socio-legal researchers confidently employ multiple methods in their work, but we also start to see the rise of a distinct strand of scholarship on socio-legal methodology, either qualitative, quantitative or mixed-methods. Yet, there is a particular process which receives less attention in the scholarship, i.e. the process of transition from one methodology to another. Much unlike the polished outcome of a research project, this process can be fuzzy, messy and tangled.

In this presentation, I reflect on the challenges encountered in my effort to transition from a qualitative to a mixed-methods research design within my doctoral research into data sharing decision-making in the UK public sector. First, I provide some background on my research project and its methodological underpinnings. I explain my motivations behind transitioning to a mixed-methods design and the process of designing a pilot survey instrument based on my qualitative findings. Second, I elaborate on the teething problems I faced in this process: difficulties with constructing a valid and reliable quantitative measure, problems related to the population and the sampling frame, as well as low response to the survey. Third, I discuss the pragmatic solutions I devised in response to the problems: using my pilot survey as a bespoke mechanism of member checking to enhance the validity of my qualitative findings. I explain the role and place of member checking in qualitative research and highlight the advantages of using a survey instrument for member checking as opposed to alternative approaches.

Far from demonising the aspiration to move from one methodology to another in a socio-legal project, the narrative offered here indicates that much-needed experimentation shall be accompanied by reflectiveness and modesty about the correspondence between theory and data.