Big data and household water insecurity: Positionings, problematics, and pathways forward

Keywords: Big data; household water insecurity; methodology; epistemology
Session Type: Virtual Paper Abstract
Day: Monday
Session Start / End Time: 2/28/2022 08:00 AM (Eastern Time (US & Canada)) - 2/28/2022 09:20 AM (Eastern Time (US & Canada))
Room: Virtual 8

Abstract

Household water insecurity is studied using a diversity of interdisciplinary methodological and analytical approaches. This increasingly includes methods in “big data” science. While volume, velocity (speed), and variety define big data as a concept, the volume of large quantitative datasets is arguably its most commonly ascribed characteristic. In particular, big data reifies the “p-value problem”, whereby results may often be “significant (p < 0.05)” owing to a high statistical power. Simultaneously, science reform advocates have forcefully argued the epistemological primacy of “significance” should be re-considered. Thus, while big data is conveyed as a major advance in data precision, dominant approaches for interpreting quantitative results, ironically, have the potential to eschew the very nuance and context promised by big data. This presentation critically positions big data in the household water security literature and discusses emerging concerns associated with its use, in conversation with qualitative research approaches. Namely, we contribute to existing calls to contextualize and evaluate research results in the era of big data, and demonstrate why science reform movements that advocate for a critical assessment of magnitude, uncertainty, and relevance of particular associations in water insecurity research cannot occur without qualitative methodologies and epistemologies.