achievement of sustainability is adversely affected by several aspects of the current academic reward system. One is the distinction between ‘pure’ computational research and ‘applied’ software development, with the former bringing rewards for ‘proof of concept’ software innovations but the latter – involving re-building the software to make it robust and efficient – being little rewarded within academia, to the extent that there are few developers to be found even in computer science departments, let alone social science departments. Yet without significant development work most ‘proof of concept’ innovations – such as those emerging from the e-Science programme – are unusable except in the hardware and software context in which the researcher constructed them. Earlier in this chapter, the advantage of software co-production was noted, but this requires collaboration not just between computer scientists and social scientist users, but also the addition of developers to the team, who can re-build innovative tools so that they become project-independent.
There is a similar distinction between both research and development on the one hand and service delivery on the other. The latter requires documentation, online or face-to-face support, FAQs, software maintenance, bug fixes, distribution, porting to new operating systems and so on. Service delivery to support e-Infrastructure is essential for effective and widespread use of e-research resources, but has little place in academia except in a very few specialized units.
Given the co-ordinated efforts of computer scientists, developers and service providers needed to deliver e-Infrastructure that can be readily deployed by users, and the lack of such organizational and human resources in many academic departments, it is not surprising that researchers tend to restrict themselves to the sorts of social science that can be achieved through an unsystematic mix of existing technologies with which they are most familiar.
The next section introduces the materials in the following chapters, which are designed to increase awareness of the opportunities that e-Infrastructure offers to transform social research. We begin with chapters focused on understanding the potential and challenges of new sources of social data for social research, while not forgetting that much can yet be done to enhance the use of more conventional data sources, such as surveys. We then turn to examining innovations where e-Research offers tools that open up new opportunities for social research across a broad range of topics. All of our contributors make clear in their individual chapters that they are aware of the issues around research ethics posed by new sources of social data and more powerful tools for analysis. Such is the importance of this topic that we include a chapter devoted entirely to it. Finally, this book had its genesis, in part, as a response to Savage and Burrow’s widely cited paper, ‘The Coming Crisis in Empirical Sociology’ (2007). We believe that the chapters in this book present plentiful evidence that innovations in digital research methods have the potential to radically transform academic sociology, and we thought it appropriate to let one of the paper’s authors have the last word on whether this transformation represents a crisis or an opportunity to be seized.
1.3 The Chapters
The chapters in this volume have been selected to provide an informative introduction to innovations in social science research methods and tools, along with a review of issues and challenges that remain to be resolved if researchers are to enjoy the full benefits of the innovations.
The chapters reflect the various ways in which social science research has changed under the influence of both new sources of social data and innovations in research infrastructure and tools. The social sciences are known for diversity of methods, and their quite different ideas about how to study and make sense of the social world. One fundamental distinction is what is often referred to as the quantitative-qualitative divide and another is between the use of primary and secondary data. Innovative digital tools have the capacity to blur both distinctions, as several chapters reveal.
Chapter 2: The Changing Social Science Data Landscape
This chapter reviews the new sources of social data being made available by a combination of new data services and changes in government policy on access to administrative records. It also notes the rapid expansion of born digital and big social data – of which social media comprise but one, admittedly high profile, example. In the chapter, Purdam and Elliot examine how access to the new data opens up new opportunities for social researchers and, drawing on an eight-point typology of new kinds of social data, they present a series of real world examples to illustrate how the social sciences can benefit from them. They also discuss some of the potential challenges for social researchers of using these new sources of social data, such as variable data quality, questionable generalizability and representativeness, and restrictions on free access to some kinds of social media data, and they explore the implications of these and other challenges for the practice of social science research.
Purdam and Elliot argue that the almost effortless capacity to collect new kinds of social data poses the risk that researchers will neglect theory in favour of more data-driven methods. They also speculate on how access to social data in real time (‘datastreams’) might lead to a blurring of the boundaries between research and policy intervention. Finally, in what is a recurring theme throughout this book, they examine some of the ethical issues that accompany the use of new forms of social data in research.
Chapter 3: Exploiting New Sources of Data
In this chapter, Elliot and Purdam take up the methodological challenges, outlined in Chapter 2, that researchers face if they are to make effective use of new sources of digital social data. They employ a series of case studies of research, including election campaigns, civil unrest, migration and mobility, and health and well-being, to illustrate how methodological innovations, such as crowd-sourcing, may be mobilized to meet the challenges.
Opinions on the value of new forms of social data have divided academic social researchers, with some taking the view that the discipline is on the threshold of a renaissance, including opportunities to study the social world in real time. Others dismiss such claims as naïve at best and – at worst – sacrificing methodological robustness and validity for convenience. Regarding the latter, numerous critics have raised concerns that the sheer volume of new forms of social data will make computational methods increasingly attractive to researchers and lead them to ignore the risks of relying on computer power to drive their analyses. One such risk is that posed to the verification and repeatability of results, which arises from using complex, and sometimes proprietary, algorithms that lack transparency; for example, the operationalization of statistical formulae in the packages researchers use are hidden from them. Another risk is that posed to meaningful understanding of social phenomena by the lure of spurious correlations thrown up by over-reliance on inductive methods.
Mindful of these problems, Elliot and Purdam argue that the solution is a middle course, combining new and conventional sources of data in a robust, mixed methods approach that bridges data- and hypothesis-driven traditions. There are, of course, obstacles to be overcome. New sources of data such as social media may increase threats to privacy, and Purdam and Elliot call for more research into ways of countering these threats through improved methods for data anonymization and a new ethical framework. In relation to the latter, they note that it is time that citizens realized the value (economic and social) of their own data and, equally importantly, they argue that commercial interests must not be allowed to constrain researchers’ access to new forms of social data.
Chapter 4: Survey Methods: Challenges and Opportunities
In this chapter, Murphy considers the future for data collection and using survey methods in the context of new sources of digital social data and technical innovations in research methods and tools. He sets the scene by discussing current challenges for
