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dc.contributor.authorDalla Valle, L
dc.contributor.authorKenett, RS
dc.date.accessioned2017-03-28T12:01:08Z
dc.date.available2017-03-28T12:01:08Z
dc.date.issued2015-11
dc.identifier.issn0748-8017
dc.identifier.issn1099-1638
dc.identifier.other7
dc.identifier.urihttp://hdl.handle.net/10026.1/8703
dc.description.abstract

<jats:p>This work is about integrated analysis of data collected as official statistics with administrative data from operational systems in order to increase the quality of information. <jats:italic>Information quality</jats:italic>, or <jats:italic>InfoQ</jats:italic>, is ‘the potential of a data set to achieve a specific goal by using a given empirical analysis method’. <jats:italic>InfoQ</jats:italic> is based on the identification of four interacting components: the analysis goal, the data, the data analysis and the utility, and it is assessed through eight dimensions: data resolution, data structure, data integration, temporal relevance, generalizability, chronology of data and goal, construct operationalization and communication. The paper illustrates, through case studies, a novel strategy to increase <jats:italic>InfoQ</jats:italic> based on the integration of official statistics with administrative data using copulas and Bayesian Networks. Official statistics are extraordinary sources of information. However, because of temporal relevance and chronology of data and goals, these fundamental sources of information are often not properly leveraged resulting in a poor level of <jats:italic>InfoQ</jats:italic> in the use of official statistics. This leads to low valued statistical analyses and to the lack of sufficiently informative results. By improving temporal relevance and chronology of data and goals, the use of Bayesian Networks allows us to calibrate official with administrative data, thus strengthening the quality of the information derived from official surveys, and, overall, enhancing <jats:italic>InfoQ</jats:italic>. We show, with examples, how to design and implement such a calibration strategy. Copyright © 2015 John Wiley &amp; Sons, Ltd.</jats:p>

dc.format.extent1281-1300
dc.languageen
dc.language.isoen
dc.publisherWiley
dc.subjectinformation quality (InfoQ)
dc.subjectdata integration
dc.subjectBayesian networks
dc.subjectadministrative data
dc.subjectofficial statistics
dc.titleOfficial Statistics Data Integration for Enhanced Information Quality
dc.typejournal-article
dc.typeArticle
dc.typeProceedings Paper
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000363876000016&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue7
plymouth.volume31
plymouth.publisher-urlhttp://dx.doi.org/10.1002/qre.1859
plymouth.publication-statusPublished
plymouth.journalQuality and Reliability Engineering International
dc.identifier.doi10.1002/qre.1859
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2015-07-17
dc.identifier.eissn1099-1638
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1002/qre.1859
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2015-11
rioxxterms.typeJournal Article/Review


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