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dc.contributor.authorJenkins, J. Craig
dc.contributor.authorMaher, Thomas V.
dc.date.accessioned2016-12-02T23:15:39Z
dc.date.available2016-12-02T23:15:39Z
dc.date.issued2016-03-08
dc.identifier.citationWhat Should We Do about Source Selection in Event Data? Challenges, Progress, and Possible Solutions 2016, 46 (1):42 International Journal of Sociologyen
dc.identifier.issn0020-7659
dc.identifier.issn1557-9336
dc.identifier.doi10.1080/00207659.2016.1130419
dc.identifier.urihttp://hdl.handle.net/10150/621502
dc.description.abstractThe prospect of using the Internet and other Big Data methods to construct event data promises to transform the field but is stymied by the lack of a coherent strategy for addressing the problem of selection. Past studies have shown that event data have significant selection problems. In terms of conventional standards of representativeness, all event data have some unknown level of selection no matter how many sources are included. We summarize recent studies of news selection and outline a strategy for reducing the risks of possible selection bias, including techniques for generating multisource event inventories, estimating larger populations, and controlling for nonrandomness. These build on a relativistic strategy for addressing event selection and the recognition that no event data set can ever be declared completely free of selection bias.
dc.language.isoenen
dc.publisherROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTDen
dc.relation.urlhttp://www.tandfonline.com/doi/full/10.1080/00207659.2016.1130419en
dc.rightsCopyright © Taylor & Francis Group, LLC.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectanalytical strategiesen
dc.subjectevent data methodsen
dc.subjectselection biasen
dc.titleWhat Should We Do about Source Selection in Event Data? Challenges, Progress, and Possible Solutionsen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Sociolen
dc.identifier.journalInternational Journal of Sociologyen
dc.description.notePublished online: 08 Mar 2016; 18 Month Embargo.en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal accepted manuscripten
refterms.dateFOA2017-09-09T00:00:00Z
html.description.abstractThe prospect of using the Internet and other Big Data methods to construct event data promises to transform the field but is stymied by the lack of a coherent strategy for addressing the problem of selection. Past studies have shown that event data have significant selection problems. In terms of conventional standards of representativeness, all event data have some unknown level of selection no matter how many sources are included. We summarize recent studies of news selection and outline a strategy for reducing the risks of possible selection bias, including techniques for generating multisource event inventories, estimating larger populations, and controlling for nonrandomness. These build on a relativistic strategy for addressing event selection and the recognition that no event data set can ever be declared completely free of selection bias.


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