dc.contributor.author | Murray-Watters, Alexander | de |
dc.contributor.author | Zins, Stefan | de |
dc.contributor.author | Silber, Henning | de |
dc.contributor.author | Gummer, Tobias | de |
dc.contributor.author | Lechner, Clemens | de |
dc.date.accessioned | 2023-01-23T12:14:10Z | |
dc.date.available | 2023-01-23T12:14:10Z | |
dc.date.issued | 2023 | de |
dc.identifier.issn | 2190-4936 | de |
dc.identifier.uri | https://www.ssoar.info/ssoar/handle/document/84586 | |
dc.description.abstract | The ease with which large amounts of data can be collected via the Internet has led to a renewed interest in the use of non-probability samples. To that end, this paper performs a case study, comparing two non-probability datasets - one based on a river-sampling approach, one drawn from an online-access panel - to a reference probability sample. Of particular interest is the single-question river-sampling approach, as the data collected for this study presents an attempt to field a multi-item scale with such a sampling method. Each dataset consists of the same psychometric measures for two of the Big-5 personality traits, which are expected to perform independently of sample composition. To assess the similarity of the three datasets we compare their correlation matrices, apply linear and non-linear dimension reduction techniques, and analyze the distance between the datasets. Our results show that there are important limitations when implementing a multi-item scale via a single-question river sample. We find that, while the correlation between our data sets is similar, the samples are composed of persons with different personality traits. | de |
dc.language | en | de |
dc.subject.ddc | Sozialwissenschaften, Soziologie | de |
dc.subject.ddc | Social sciences, sociology, anthropology | en |
dc.subject.other | River Sample; Non-probability Sample; BIG-5; Non-linear Dimension reduction; Web Survey Research | de |
dc.title | River Sampling - a Fishing Expedition: A Non-Probability Case Study | de |
dc.description.review | begutachtet (peer reviewed) | de |
dc.description.review | peer reviewed | en |
dc.source.journal | Methods, data, analyses : a journal for quantitative methods and survey methodology (mda) | |
dc.source.volume | 17 | de |
dc.publisher.country | DEU | de |
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 | Datengewinnung | de |
dc.subject.thesoz | data capture | en |
dc.subject.thesoz | Internet | de |
dc.subject.thesoz | Internet | en |
dc.subject.thesoz | Stichprobe | de |
dc.subject.thesoz | sample | en |
dc.subject.thesoz | Online-Befragung | de |
dc.subject.thesoz | online survey | en |
dc.subject.thesoz | Umfrageforschung | de |
dc.subject.thesoz | survey research | 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 | 10040547 | |
internal.identifier.thesoz | 10040528 | |
internal.identifier.thesoz | 10037472 | |
internal.identifier.thesoz | 10037911 | |
internal.identifier.thesoz | 10040714 | |
dc.type.stock | article | de |
dc.type.document | Zeitschriftenartikel | de |
dc.type.document | journal article | en |
dc.source.pageinfo | 3-27 | de |
internal.identifier.classoz | 10105 | |
internal.identifier.journal | 614 | |
internal.identifier.document | 32 | |
internal.identifier.ddc | 300 | |
dc.identifier.doi | https://doi.org/10.12758/mda.2022.05 | 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.valid | false | |
internal.pdf.wellformed | true | |
internal.pdf.encrypted | false | |
ssoar.urn.registration | false | de |