This post originally appeared on MethodSpace here and was reposted with permission
By Michael Todd, MethodSpace Editor
The era of analyzing big data to research issues in social science is among us. Given that the era only really arrived with the advent of the data itself, and those pools of data – sources such as social media, unstructured text, digital sensors, financial and administrative transactions — have only recently become widely available as commodities, it’s reasonable to think that big data research is a young academics game.
In fact, based on findings reported in a new white paper, that’s not necessarily so. There was no difference in career stage among those doing – or not doing – big data research among respondents to a survey. In short, the idea that early-career researchers were somehow more likely to be digital natives and therefore more apt to conduct computational social science than those who PhDs were issued more than a decade ago was not sustained.
SAGE Publishing surveyed social scientists around the world to learn more about who engages in research using so-called ‘big data,’ and what challenges they face as well as the barriers facing those who are interested in conducting computational social science going forward. Summarizing those results, SAGE has published a white paper, Who Is Doing Computational Social Science? Trends in Big Data Research, authored by Katie Metzler, publisher for SAGE Research Methods; David A. Kim, in the Department of Emergency Medicine at Stanford University; Nick Allum, a professor of sociology and research methodology at the University of Essex; and Angella Denman of the University of Essex.
“The findings from this survey reveal that there is an appetite to engage with data at an accelerated rate among social scientists,” noted SAGE’s director of global publishing, Ziyad Marar, “but that unique challenges persist related to such issues as interdisciplinary, research design training, and access.” (Marar addressed some of these same issues in an essay he wrote responding to the annual Edge question for this year.)
Here at MethodSpace, we’ll be unpacking the findings of that survey in three posts. Today we’ll look at who is doing computational social science. The next two posts will examine what is being used for computational research, and the last post will discuss the challenges the survey respondents identified.
The survey team initially contacted more than a half million social science contacts, of whom 9,412 fully completed the survey. The vast majority, 7,933, described themselves as from academe, with the next largest sector, government, providing 527 answers. A plurality of answers — 3,302– came from the United States, followed by the United Kingdom (728), Indiana (405), and Canada (353), although the responses were genuinely global, with 35 counties each supplying at least 50 completed surveys.
Disciplines of respondents were also all over the map, with education, psychology and health sciences each providing more than a thousand respondents. Nonetheless, fields as diverse as the law, nursing, marketing and history joined more traditional social science disciplines such as political science, demographics, criminology and sociology in supplying respondents.
Of respondents, one third self-identified as having been involved in big data research of some kind, with one of four of them reporting that all or most of their research involved big data or data science methods. Some 60 percent of researchers reporting big data work said they had conducted their big data research within the last 12 months.
Predictably, who is doing the most big data research is in large part explained by type of research associated with the respondent’s discipline. And so, the most common disciplines reporting any big data research were social statistics and research methods, where almost three out of five respondents had been involved in big data research at some point, economics (about half), demography, population studies, and human geography (slightly less than half), and health sciences (slightly less than two out of five).
“Overall,” wrote the white paper’s authors, “these percentages seem very high (especially in the case of history and anthropology, which are not typically disciplines associated with big data), and this further suggests that researchers who are very interested in big data and who are already engaged in big data research were more likely to complete the survey. It may also indicate ambiguity about what people understand by the terms big data and data science.”
Of the remaining two thirds of respondents, those who have not yet engaged in big data research, half of them (3057 respondents) said that they are either “definitely planning on doing so in the future” or “might do so in the future.” That means that a substantial number of respondents don’t expect to do any big data work period, and while it might seem difficult to escape some brush with big data, some 1,083 of respondents said they definitively are not planning on doing it.