dc.contributor.author | Cornesse, Carina | de |
dc.date.accessioned | 2020-07-10T09:09:31Z | |
dc.date.available | 2020-07-10T09:09:31Z | |
dc.date.issued | 2020 | de |
dc.identifier.issn | 2296-4754 | de |
dc.identifier.uri | https://www.ssoar.info/ssoar/handle/document/68356 | |
dc.description.abstract | Auxiliary data are becoming more important as nonresponse rates increase and new fieldwork monitoring and respondent targeting strategies develop.
In many cases, auxiliary data are collected or linked to the gross sample to predict survey response. If the auxiliary data have high predictive power,
the response models can meaningfully inform survey operations as well as post-survey adjustment procedures. In this paper, I examine the utility of
different sources of auxiliary data (sampling frame data, interviewer observations, and micro-geographic area data) for modeling survey response in a
probability-based online panel in Germany. I find that the utility of each of these data sources is challenged by a number of concerns (scarcity, missing
data, transparency issues, and high levels of aggregation) and that none of the auxiliary data are associated with survey response to any substantial
degree. | de |
dc.language | en | de |
dc.subject.ddc | Sozialwissenschaften, Soziologie | de |
dc.subject.ddc | Social sciences, sociology, anthropology | en |
dc.subject.other | auxiliary data; INKAR data; interviewer observations; Microm data; Nonresponse; online panel recruitment; response propensity; German Internet Panel; GIP | de |
dc.title | The utility of auxiliary data for survey response modeling: Evidence from the German Internet Panel | de |
dc.description.review | begutachtet (peer reviewed) | de |
dc.description.review | peer reviewed | en |
dc.source.journal | Survey Methods: Insights from the Field | |
dc.publisher.country | DEU | |
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 | Bundesrepublik Deutschland | de |
dc.subject.thesoz | Federal Republic of Germany | en |
dc.subject.thesoz | Umfrageforschung | de |
dc.subject.thesoz | survey research | en |
dc.subject.thesoz | Antwortverhalten | de |
dc.subject.thesoz | response behavior | en |
dc.subject.thesoz | Datengewinnung | de |
dc.subject.thesoz | data capture | 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 | noch nicht fertig erschlossen | de |
internal.identifier.thesoz | 10037571 | |
internal.identifier.thesoz | 10040714 | |
internal.identifier.thesoz | 10035808 | |
internal.identifier.thesoz | 10040547 | |
internal.identifier.thesoz | 10055811 | |
dc.type.stock | article | de |
dc.type.document | Zeitschriftenartikel | de |
dc.type.document | journal article | en |
dc.source.pageinfo | 1-9 | de |
internal.identifier.classoz | 10105 | |
internal.identifier.journal | 472 | |
internal.identifier.document | 32 | |
internal.identifier.ddc | 300 | |
dc.source.issuetopic | Fieldwork Monitoring Strategies for Interviewer-Administered Surveys | de |
dc.identifier.doi | https://doi.org/10.13094/SMIF-2020-00008 | de |
dc.description.pubstatus | Veröffentlichungsversion | de |
dc.description.pubstatus | Published Version | en |
internal.identifier.licence | 16 | |
internal.identifier.pubstatus | 1 | |
internal.identifier.review | 1 | |
ssoar.wgl.collection | true | de |
internal.pdf.wellformed | true | |
internal.pdf.encrypted | false | |
ssoar.urn.registration | false | de |