Information of all kinds is now being produced, collected, and analyzed at unprecedented speed, breadth, depth, and scale. The capacity to collect and analyze massive data sets has already transformed fields such as biology, astronomy, and physics, but the social sciences have been comparatively slower to adapt, and the path forward is less certain. For many, the big data revolution promises to ask, and answer, fundamental questions about individuals and collectives, but large data sets alone will not solve major social or scientific problems. New paradigms being developed by the emerging field of “computational social science” will be needed not only for research methodology, but also for study design and interpretation, cross-disciplinary collaboration, data curation and dissemination, visualization, replication, and research ethics (Lazer et al., 2009).

Sage conducted a survey with social scientists around the world to learn more about researchers engaged in big data research and the challenges they face, as well as the barriers to entry for those looking to engage in this kind of research in the future. We were also interested in the challenges of teaching computational social science methods to students. The survey was fully completed by 9412 respondents, indicating strong interest in this topic among our social science contacts. Of respondents, 33 percent had been involved in big data research of some kind and, of those who have not yet engaged in big data research, 49 percent (3057 respondents) said that they are either “definitely planning on doing so in the future” or “might do so in the future.”

Find out more by reading the full white paper, written by Katie Metzler, Publisher for Sage Research Methods, David A. Kim, from Stanford University’s Department of Medicine, Nick Allum, Professor of Sociology and Research Methodology at the University of Essex, and Angella Denman, also at the University of Essex.