When social scientists think about big data, they often think in terms of quantitative number crunching. However, the growing availability of ‘big’ qualitative datasets presents new opportunities for qualitative research. In this post, Lynn Jamieson and Sarah Lewthwaite explore how ‘big qual’ can be deployed as a distinct research methodology to develop new forms of qualitative research and elucidate complex interactions between largescale qualitative datasets.
The words BIG and DATA have featured prominently in discussions about creating and analysing large scale sets of information to answer important research questions. However, social scientists have had a tendency to think in terms of quantitative, rather than qualitative data, when teaching about and conducting large scale secondary data analysis. As large qualitative data sets have become increasingly available, we have been exploring the potential benefits of ‘Big Qual’, to pioneer a ‘breadth-and-depth method’ of research that could lead to new impactful forms of research, teaching and social policymaking.
One serious limitation to qualitative research is the time it takes to undertake. Individuals, or even teams of researchers, have difficulty rapidly dealing with large volumes of qualitative data. Conventional methods of rigorous analysis require a level of immersion in the data that is inevitably slow, even when software assisted. Conversely, responding to funder initiatives, public archives, such as the UK Data Archive, have made available growing quantities of data from multiple studies, to academics, government departments and third sector organisations to analyse.
This has stimulated an interest in Big Data and often a preference for quantitative methods to quickly and easily make sense of large datasets. Terms such as ‘mining’ and other extractive metaphors are often used to describe these processes, but just how much depth and breadth these approaches deliver is unclear. In contrast to this quantitative approach, archives also provide an opportunity to combine the breadth of quantitative methods with the ‘deep digging’ of qualitative research, opening up the exciting prospect of a new approach to qualitative analysis that can combine insights from across different datasets.
What is Big Qual?
Simply put, Big Qual is the analysis of volumes of qualitative data that are much larger than the quantity that would be feasible for a solo researcher or small team to collect and analyse themselves. For the purposes of our research, we used the Timescapes Archive, a fantastically rich and detailed set of qualitative research projects, to develop our breadth-and-depth methodology and to draw out the implications it has for research, teaching and social policymaking.
Applying the breadth-and-depth method
At the heart of the breadth-and-depth methodology we have developed, is an archaeological metaphor that lays out four stages to dealing with big qual that are akin to the process of discovery in archaeology:
- An enquiry-led overview of archived qualitative research: using meta data like an archaeologist using photographs in an aerial survey
- Computer-aided scrutiny across the breadth of selected data collections: to assess what merits closer investigation, like an archaeologist’s ground-based geophysical survey of an area.
- Analysis of multiple small samples of likely data: equivalent to digging shallow ‘test pits’ to find an area worthy of deeper excavation.
- In-depth analysis: of the type familiar to qualitative researchers, like archaeological deep excavation
Teaching and Research
For hard-pressed, time-constrained teachers of research methods, learning a new complex methodology and developing resources is daunting, but there are benefits. Primarily, it offers students and teachers the opportunity to construct new data sets and conduct analysis using existing qualitative data. This can jump start the process of getting hands-on with data, promoting a learning by doing approach, even if developing research methodologies is not conventionally seen as being ‘in the field’. Significantly, working with these datasets also provides a ‘backstage’ view of qualitative research carried out by experienced practitioners, giving insights into qualitative research techniques that are often obscured, or ‘black boxed’, in journal publications.
The breadth-and-depth approach can also function as a modular approach, with each stage prompting students to think differently about the relationship between theory and evidence, research questions and data. Working with existing qualitative data enables students to focus on individual stages, for instance being able to devote greater time to analysis, or thinking deeply about starting from research questions, versus being led by data. The method also promotes team research and allows students to be co-discoverers on wider research projects, as well as developing their own research questions, trialling new software, or even assembling their own datasets.
Training workshops suggest a real appetite for using Big Qual in this way among both teachers and students. However, the full method is probably most suited to PhD students, where it can be used to sharpen a whole set of skills, helping create the next generation of researchers equipped and inspired to tackle some of society’s most pressing problems.
To this end, techniques for working with new assemblages of qualitative data open the door to a wide range of social policy research, including looking at some of the persistent concerns of our time such as poverty or care for older people. New assemblages enable comparative questions, such as How do different groups of people talk about poverty or elder care? If and how has this changed over time? What were/are people’s experiences of poverty or giving, receiving or brokering care, their understanding, their questions and answers? As part of the development of the method, we ourselves are using our new Big Qual Timescapes dataset to look at how we can answer questions about shifts in practices of care and intimacy over time and across the life course.
This article has been produced as part of research on Big Qual Analysis, the Teaching and Learning of Big Qual and the Pedagogy of Methodological Learning funded by the ESRC National Centre for Research Methods at the University of Southampton involving Sarah Lewthwaite, Susie Weller, Lynn Jamieson, Melanie Nind, Ros Edwards and Emma Davidson.
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About the authors
Sarah Lewthwaite is a research fellow at the ESRC National Centre for Research Methods, University of Southampton where she is conducting research into the teaching and learning of advanced research methods. Sarah can be reached on Twitter via @slewth.
Lynn Jamieson is a Professor in Sociology at the University of Edinburgh, Co-Director of the Centre for Research on Families and Relationships and leads the research strand of ESRC National Centre for Research Methods at the University of Edinburgh.