Little data: negotiating the 'new normal' with idiosyncratic and incomplete datasets.

In this paper we make a case for 'Little Data', which is real-time, self-collected, idiosyncratic datasets maintained by individuals about themselves on myriad topics. We develop and offer a methodology for combining these messy, highly personal insights, to make deductive observations about collective practices. In testing this approach, we use the case study of the 2020–21 stay-at-home orders imposed in the U.S.A., U.K., and Western Europe during the Coronavirus pandemic to operationalise and demonstrate the applicability of this method. Our main finding is to show that whilst stay-at-home orders did have a significant impact on habits during the COVID-19 pandemic, these changes were often counterintuitive, of an insightful nature on topics that would otherwise not be investigated, and always short-lived. Our main contribution is to present Little Data, despite and because of its fragmented and disparate nature, as a viable and useful tool to understand personal habits at finite junctures. [ABSTRACT FROM AUTHOR]