Getting beyond WEIRD with Big
Data
Social
science has a weird problem, or the WEIRD problem, as Joseph Henrich and his
colleagues called it in their 2010 critique of sampling biases in behavioral
and social science research. They note
that upwards of 90% of behavioral science research is conducted using
participants from Western, Educated, Industrialized, Rich and Democratic
(WEIRD) societies, and that undergraduate students attending American
universities are 4,000 times more
likely to be sampled into social science research than the average person. This presents some obvious challenges for the
external validity of social science results. You would be hard pressed to find
a scientist who would hold steadfast the idea that we should really be basing
our fundamental understandings about human behavior on the performance of college
undergraduates. But, any social scientist – especially an early career social
scientist trying to establish a research program on a limited budget and under
great time pressure – will tell you that sometimes, external validity is the
price of efficiency. To get anything done at all, we have to use the resources
that are most readily available to us. And the one resource university faculty
have in abundance is a pool of willing, often free, undergraduate research
participants.
Big
Data are poised to change the resource equation. Although far from
comprehensive, most Big Data datasets are less WEIRD than the college
undergraduate samples that many social scientists have come to rely on.
Already, researchers are making great use of Big Data to remedy the WEIRD
problem – for example, by conducting cross-cultural research comparing
Eastern and Western online communities, or following the twitter habits of citizens living
in authoritarian states. In my article in the Early Career
Researcher Forum, I encourage social scientists to seek out Big Data generated
by participants who have been historically overlooked, underrepresented or excluded
from social science research, even if that means actively whittling down a Big
Data dataset until it is quite small. By choosing to make Big Data small, we
can make science a little less WEIRD, and a little more inclusive, moving
forward.
About the author
Brooke
Foucault Welles is an Assistant Professor in the Department of Communication
Studies and on the faculty of Network Science at Northeastern University. Her
research focuses on networked communication, with particular emphasis on how
people use online communication networks to facilitate personal and
organizational goals. You can learn more about Brooke’s research on her website or on Twitter.
