dc.contributor.author | Hill, Craig A. | de |
dc.contributor.author | Biemer, Paul P. | de |
dc.contributor.author | Buskirk, Trent D. | de |
dc.contributor.author | Callegaro, Mario | de |
dc.contributor.author | Córdova Cazar, Ana Lucía | de |
dc.contributor.author | Eck, Adam | de |
dc.contributor.author | Japec, Lilli | de |
dc.contributor.author | Kirchner, Antje | de |
dc.contributor.author | Kolenikov, Stanislav | de |
dc.contributor.author | Lyberg, Lars | de |
dc.contributor.author | Sturgis, Patrick | de |
dc.date.accessioned | 2019-05-17T09:49:19Z | |
dc.date.available | 2019-05-17T09:49:19Z | |
dc.date.issued | 2019 | de |
dc.identifier.issn | 1864-3361 | de |
dc.identifier.uri | https://www.ssoar.info/ssoar/handle/document/62687 | |
dc.description.abstract | Held in October 2018, The Big Data Meets Survey Science conference, also known as "BigSurv18," provided a first-of-its-kind opportunity for survey researchers, statisticians, computer scientists, and data scientists to convene under the same roof. At this conference, scientists from multiple disciplines were able to exchange ideas about their work might influence and enhance the work of others. This was a landmark event, especially for survey researchers and statisticians, whose industry has been buffeted of late by falling response rates and rising costs at the same time as a proliferation of new tools and techniques, coupled with increasing availability of data, has resulted in "Big Data" approaches to describing and modelling human behavior. | de |
dc.language | en | de |
dc.subject.ddc | Sozialwissenschaften, Soziologie | de |
dc.subject.ddc | Social sciences, sociology, anthropology | en |
dc.subject.other | Big Data; machine learning | de |
dc.title | Exploring New Statistical Frontiers at the Intersection of Survey Science and Big Data: Convergence at "BigSurv18" | de |
dc.description.review | begutachtet (peer reviewed) | de |
dc.description.review | peer reviewed | en |
dc.source.journal | Survey Research Methods | |
dc.source.volume | 13 | de |
dc.publisher.country | DEU | |
dc.source.issue | 1 | de |
dc.subject.classoz | Erhebungstechniken und Analysetechniken der Sozialwissenschaften | de |
dc.subject.classoz | Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods | en |
dc.subject.thesoz | Umfrageforschung | de |
dc.subject.thesoz | survey research | en |
dc.subject.thesoz | amtliche Statistik | de |
dc.subject.thesoz | official statistics | en |
dc.subject.thesoz | Datengewinnung | de |
dc.subject.thesoz | data capture | en |
dc.subject.thesoz | Datenqualität | de |
dc.subject.thesoz | data quality | en |
dc.subject.thesoz | künstliche Intelligenz | de |
dc.subject.thesoz | artificial intelligence | en |
dc.subject.thesoz | Informatik | de |
dc.subject.thesoz | computer science | en |
dc.rights.licence | Deposit Licence - Keine Weiterverbreitung, keine Bearbeitung | de |
dc.rights.licence | Deposit Licence - No Redistribution, No Modifications | en |
internal.status | formal und inhaltlich fertig erschlossen | de |
internal.identifier.thesoz | 10040714 | |
internal.identifier.thesoz | 10035431 | |
internal.identifier.thesoz | 10040547 | |
internal.identifier.thesoz | 10055811 | |
internal.identifier.thesoz | 10043031 | |
internal.identifier.thesoz | 10047318 | |
dc.type.stock | article | de |
dc.type.document | Zeitschriftenartikel | de |
dc.type.document | journal article | en |
dc.source.pageinfo | 123-135 | de |
internal.identifier.classoz | 10105 | |
internal.identifier.journal | 674 | |
internal.identifier.document | 32 | |
internal.identifier.ddc | 300 | |
dc.identifier.doi | https://doi.org/10.18148/srm/2019.v1i1.7467 | de |
dc.description.pubstatus | Veröffentlichungsversion | de |
dc.description.pubstatus | Published Version | en |
internal.identifier.licence | 3 | |
internal.identifier.pubstatus | 1 | |
internal.identifier.review | 1 | |
ssoar.urn.registration | false | de |