dc.contributor.author | Daikeler, Jessica | de |
dc.contributor.author | Gauly, Britta | de |
dc.contributor.author | Rosenthal, Matthias | de |
dc.contributor.editor | Maehler, Débora B. | de |
dc.contributor.editor | Rammstedt, Beatrice | de |
dc.date.accessioned | 2020-10-13T11:30:42Z | |
dc.date.available | 2020-10-13T11:30:42Z | |
dc.date.issued | 2020 | de |
dc.identifier.isbn | 978-3-030-47515-4 | de |
dc.identifier.issn | 2367-1718 | de |
dc.identifier.uri | https://www.ssoar.info/ssoar/handle/document/70070 | |
dc.description.abstract | Linking survey data to administrative data offers researchers many opportunities. In particular, it enables them to enrich survey data with additional information without increasing the burden on respondents. German PIAAC data on individual skills, for example, can be combined with administrative data on individual employment histories. However, as the linkage of survey data with administrative data records requires the consent of respondents, there may be bias in the linked dataset if only a subsample of respondents - for example, high-educated individuals - give their consent. The present chapter provides an overview of the pilot project about linking the German PIAAC data with individual administrative data. In a first step, we illustrate characteristics of the linkable datasets and describe the linkage process and its methodological challenges. In a second step, we provide an illustrative example of the use of the linked data and investigate how the skills assessed in PIAAC are associated with the linkage decision. | de |
dc.language | en | de |
dc.publisher | Springer | de |
dc.relation.ispartof | 69624 | |
dc.subject.ddc | Sozialwissenschaften, Soziologie | de |
dc.subject.ddc | Social sciences, sociology, anthropology | en |
dc.subject.other | PIAAC; PIAAC-L; administrative data; data linkage; consent to linkage | de |
dc.title | Linking PIAAC Data to Individual Administrative Data: Insights from a German Pilot Project | de |
dc.description.review | begutachtet | de |
dc.description.review | reviewed | en |
dc.source.collection | Large-Scale Cognitive Assessment: Analyzing PIAAC Data | de |
dc.publisher.country | DEU | |
dc.publisher.city | Cham | de |
dc.source.series | Methodology of Educational Measurement and Assessment | |
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 | Datengewinnung | de |
dc.subject.thesoz | data capture | en |
dc.subject.thesoz | Datenaufbereitung | de |
dc.subject.thesoz | data preparation | en |
dc.subject.thesoz | Datenqualität | de |
dc.subject.thesoz | data quality | en |
dc.rights.licence | Creative Commons - Namensnennung 4.0 | de |
dc.rights.licence | Creative Commons - Attribution 4.0 | en |
ssoar.contributor.institution | GESIS | de |
internal.status | formal und inhaltlich fertig erschlossen | de |
internal.identifier.thesoz | 10040714 | |
internal.identifier.thesoz | 10040547 | |
internal.identifier.thesoz | 10040524 | |
internal.identifier.thesoz | 10055811 | |
dc.type.stock | incollection | de |
dc.type.document | Sammelwerksbeitrag | de |
dc.type.document | collection article | en |
dc.source.pageinfo | 271-290 | de |
internal.identifier.classoz | 10105 | |
internal.identifier.document | 25 | |
internal.identifier.ddc | 300 | |
dc.identifier.doi | https://doi.org/10.1007/978-3-030-47515-4_11 | de |
dc.description.pubstatus | Veröffentlichungsversion | de |
dc.description.pubstatus | Published Version | en |
internal.identifier.licence | 16 | |
internal.identifier.pubstatus | 1 | |
internal.identifier.review | 2 | |
internal.identifier.series | 1686 | |
ssoar.wgl.collection | true | de |
internal.pdf.wellformed | true | |
internal.pdf.encrypted | false | |
ssoar.urn.registration | false | de |