Introduction

This volume contributes both to a better understanding of contemporary fathers and fatherhood, and of how to conduct social research. This chapter does so by considering the challenges and value of engaging with ‘big data’ for fatherhood researchers. It argues that the arrival of new forms of data presents genuinely exciting possibilities for tackling important questions that have been either inadequately addressed, or sidelined, in previous work. The discussion therefore contributes to setting the agenda for near-future empirical studies of fatherhood through assessing the potential of newly available methods and, by implication, the limitations of those currently used. To do so, it looks at the value for fatherhood research of three forms of data that are commonly lumped together as ‘big’: administrative data; large-scale textual data; and data produced through mobile applications. Necessarily, the question of methods cannot be divorced from that of substantive lines of enquiry. So, in setting researchers the challenge of pursuing the possibilities of big data, attention is directed to particular aspects of fatherhood that deserve academic scrutiny. There need not then be a ‘crisis’ in empirical sociology prompted by the arrival of big data as initially suggested by Savage and Burrows (2007), but there is a need to engage with data generated through technological innovations. The challenge of big data is more profound than proposing a shift to include different methods of data collection; it requires social scientists to rethink the questions about fathers and fatherhood we are able to answer and work with other disciplines to do so.

‘Go large’: studying fathers and fatherhood

Family sociology has been inventive in developing qualitative approaches, for example, adapting qualitative interviewing methods to include emotion maps (Gabb, 2008) or moving away from interviewer-led data with participant produced videos (Muir and Mason, 2012), as well using multiple methods within a single research project (for example, Eldén, 2012: Gabb and Fink, 2015). In other words, researchers are increasingly ‘creative’ about the methods that are used. However, thinking has been less imaginative when it comes to the possibilities offered by large-scale data. This matters, given that it has been widely claimed that computational social science is what is going to really challenge and change the social sciences in the 21st century (see, for example, Christakis 2012).