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Article dans une revue

Small Big Data: Using multiple data-sets to explore unfolding social and economic change

Abstract : Bold approaches to data collection and large-scale quantitative advances have long been a preoccupation for social science researchers. In this commentary we further debate over the use of large-scale survey data and official statistics with ‘Big Data’ methodologists, and emphasise the ability of these resources to incorporate the essential social and cultural heredity that is intrinsic to the human sciences. In doing so, we introduce a series of new data-sets that integrate approximately 30 years of survey data on victimisation, fear of crime and disorder and social attitudes with indicators of socio-economic conditions and policy outcomes in Britain. The data-sets that we outline below do not conform to typical conceptions of ‘Big Data’. But, we would contend, they are ‘big’ in terms of the volume, variety and complexity of data which has been collated (and to which additional data can be linked) and ‘big’ also in that they allow us to explore key questions pertaining to how social and economic policy change at the national level alters the attitudes and experiences of citizens. Importantly, they are also ‘small’ in the sense that the task of rendering the data usable, linking it and decoding it, required both manual processing and tacit knowledge of the context of the data and intentions of its creators.
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https://hal-sciencespo.archives-ouvertes.fr/hal-02186396
Contributeur : Spire Sciences Po Institutional Repository <>
Soumis le : mercredi 17 juillet 2019 - 12:07:35
Dernière modification le : vendredi 2 juillet 2021 - 13:59:53

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Stephen Farrall, Emily Gray, Colin Hay, Will Jennings. Small Big Data: Using multiple data-sets to explore unfolding social and economic change. Big Data & Society, 2015, pp.En ligne. ⟨hal-02186396⟩

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