dc.contributor.author | West, Brady T. | de |
dc.contributor.author | Sakshaug, Joseph W. | de |
dc.date.accessioned | 2018-03-06T12:03:09Z | |
dc.date.available | 2018-03-06T12:03:09Z | |
dc.date.issued | 2018 | de |
dc.identifier.issn | 2296-4754 | de |
dc.identifier.uri | https://www.ssoar.info/ssoar/handle/document/56265 | |
dc.description.abstract | The importance of correctly accounting for complex sampling features when generating finite population inferences based on complex sample survey data sets has now been clearly established in a variety of fields, including those in both statistical and non-statistical domains. Unfortunately, recent studies of analytic error have suggested that many secondary analysts of survey data do not ultimately account for these sampling features when analyzing their data, for a variety of possible reasons (e.g., poor documentation, or a data producer may not provide the information in a public-use data set). The research in this area has focused exclusively on analyses of household survey data, and individual respondents. No research to date has considered how analysts are approaching the data collected in establishment surveys, and whether published articles advancing science based on analyses of establishment behaviors and outcomes are correctly accounting for complex sampling features. This article presents alternative analyses of real data from the 2013 Business Research and Development and Innovation Survey (BRDIS), and shows that a failure to account for the complex design features of the sample underlying these data can lead to substantial differences in inferences about the target population of establishments for the BRDIS. | en |
dc.language | en | |
dc.subject.ddc | Sozialwissenschaften, Soziologie | de |
dc.subject.ddc | Social sciences, sociology, anthropology | en |
dc.subject.other | Complex Sample Surveys; Design-Based Inference; Establishment Surveys; Survey Data Analysis; Weighted Estimation; BRDIS; Business Research and Development and Innovation Survey | de |
dc.title | The Need to Account for Complex Sampling Features when Analyzing Establishment Survey Data: An Illustration using the 2013 Business Research and Development and Innovation Survey (BRDIS) | 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 | technical literature | en |
dc.subject.thesoz | Stichprobenfehler | de |
dc.subject.thesoz | Datengewinnung | de |
dc.subject.thesoz | sample | en |
dc.subject.thesoz | secondary analysis | en |
dc.subject.thesoz | weighting | en |
dc.subject.thesoz | sampling error | en |
dc.subject.thesoz | data | en |
dc.subject.thesoz | Schätzung | de |
dc.subject.thesoz | analysis | en |
dc.subject.thesoz | Fachliteratur | de |
dc.subject.thesoz | periodical | en |
dc.subject.thesoz | Stichprobe | de |
dc.subject.thesoz | Sekundäranalyse | de |
dc.subject.thesoz | Gewichtung | de |
dc.subject.thesoz | data capture | en |
dc.subject.thesoz | estimation | en |
dc.subject.thesoz | Analyse | de |
dc.subject.thesoz | Daten | de |
dc.subject.thesoz | Zeitschrift | de |
dc.rights.licence | Creative Commons - Attribution 4.0 | en |
dc.rights.licence | Creative Commons - Namensnennung 4.0 | de |
internal.status | noch nicht fertig erschlossen | de |
internal.identifier.thesoz | 10040547 | |
internal.identifier.thesoz | 10034712 | |
internal.identifier.thesoz | 10037472 | |
internal.identifier.thesoz | 10035502 | |
internal.identifier.thesoz | 10045727 | |
internal.identifier.thesoz | 10039627 | |
internal.identifier.thesoz | 10043145 | |
internal.identifier.thesoz | 10034708 | |
internal.identifier.thesoz | 10059347 | |
internal.identifier.thesoz | 10057146 | |
dc.type.stock | article | de |
dc.type.document | journal article | en |
dc.type.document | Zeitschriftenartikel | de |
dc.source.pageinfo | 1-10 | de |
internal.identifier.classoz | 10105 | |
internal.identifier.journal | 472 | |
internal.identifier.document | 32 | |
internal.identifier.ddc | 300 | |
dc.identifier.doi | https://doi.org/10.13094/SMIF-2018-00001 | de |
dc.subject.methods | empirisch-qualitativ | de |
dc.subject.methods | qualitative empirical | en |
dc.description.pubstatus | Published Version | en |
dc.description.pubstatus | Veröffentlichungsversion | de |
internal.identifier.licence | 16 | |
internal.identifier.methods | 5 | |
internal.identifier.pubstatus | 1 | |
internal.identifier.review | 1 | |
internal.pdf.version | 1.3 | |
internal.pdf.valid | true | |
internal.pdf.wellformed | true | |
internal.check.abstractlanguageharmonizer | CERTAIN | |
internal.check.languageharmonizer | CERTAIN_CHANGED | |