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Introduction: History and Motivation

Hofferth, S. L., Moran, E. F., Entwisle, B., Aber, J. L., Brady, H. E., Conley, D., Cutter, S. L., Eckel, C. C., Hamilton, D. & Hubacek, K., 1-Jan-2017, In : Annals of the American Academy of Political and Social Science. 669, 1, p. 6-17 12 p.

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DOI

  • Sandra L. Hofferth
  • Emilio F. Moran
  • Barbara Entwisle
  • J. Lawrence Aber
  • Henry E. Brady
  • Dalton Conley
  • Susan L. Cutter
  • Catherine C. Eckel
  • Darrick Hamilton
  • Klaus Hubacek

Big data, that is, data that are byproducts of our lives rather than designed for research purposes, are the newest of the information highway innovations. One of the important challenges to social and behavioral science data collection, curation, and dissemination for the foreseeable future is to link diverse forms of data in a way that is cumulative, representative, meaningful, and accessible to a broad range of researchers. It is critical to explore the new questions these data can address and to develop new methods to address them, including linking persons and information about them and their environments across different data platforms while maintaining confidentiality and privacy. Linking a broad array of information—from administrative data (local and state and regional), to social media (Twitter, Facebook), to census and other surveys, to ethnographic data, and data from experiments such as randomized controlled trials—to address how humans and their communities make decisions is challenging. This issue was addressed by papers presented at a conference on New Data Linkages convened by the Social Observatories Coordinating Network in 2016; those articles are brought together in this volume.

Original languageEnglish
Pages (from-to)6-17
Number of pages12
JournalAnnals of the American Academy of Political and Social Science
Volume669
Issue number1
Publication statusPublished - 1-Jan-2017
Externally publishedYes

    Keywords

  • administrative data, big data, data analytics, knowledge generation, social science, survey research, systems science

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