The social sciences in the interstices of big data: a practical guide to avoid being conservative and avoid falling into the end of theory.

Abstract

When we began studying the problem of Big data associated with the social sciences, our first reaction as methodology teachers was to think that we are facing a new form of empiricism where the availability of data would prevail more than ever over theory. We were heirs - without knowing it - to the alarms that went off when Wired magazine in 2008 published "The End of Theory: The Data Deluge Makes the Scientific Method Obsolete" by Chris Anderson. 

After a few readings we understood the textures and nuances that this problem entailed and that as social scientists we were facing what was beginning to emerge as a new change in the ways of interpreting and representing the world (Gindin and Busso 2021), a change that not necessarily implies abdicating before the accumulation of data and it does imply adding new methods and techniques to the already traditional ones. In the social sciences, the volume of data, more than a virtue in itself, should be a reason for reflection in relation to quality, access, the contexts  construction of data and the ethical problems associated with the privacy of the people who produce them.