Mixed-method approaches to social network analysis

Social Network Analysis (SNA) has received growing attention in methodological debates in the social sciences. Recent mathematical developments and user-friendly computer
programmes for visualising and measuring networks have led to significant advances in quantitative SNA. Amidst these developments, however, there have been calls for the revival
of qualitative approaches to social networks, not necessarily to supplant quantitative methods, but to complement them. Quantitative approaches map and measure networks by simplifying social relations into numerical data, where ties are either absent or present. They therefore bracket out questions of crucial importance to understanding the kinds of human interaction networks studied by social scientists. Qualitative approaches, on the other hand, enable analysts to consider issues relating to the construction, reproduction, variability and dynamics
of complex social ties. This paper considers the arguments for adopting a mixed-method approach to network analysis, firstly as they arise out of the existing research literature, and secondly, as they have been highlighted in explicit theoretical debates about combining quantitative and qualitative data and analysis. By unpacking the different ways in which researchers have combined
quantitative and qualitative methods in network projects it also seeks to provide some guidance for others on ‘how to’ mix methods in SNA. In particular, it reviews literature in
which quantitative SNA has been combined with interviews, ethnography and historical archival research and considers the benefits of these strategies. On a theoretical note, the
paper considers suggestions that mixing quantitative and qualitative approaches can enable researchers to explore the structure (or form) of networks from an ‘outsider’s’ view, and the content and processes of networks from an ‘insider’s’ view. It also refers to recent discussions which suggest that SNA offers a particular opportunity for mixing methods because networks are both structure and process at the same time, and therefore evade simple
categorisation as either quantitative or qualitative phenomena.