Identification of Falsifications in Survey Data

调查数据可能会被访问者篡改。基于对这种伪造动机的分析，我们开发、测试和应用多元统计方法，该方法可用于识别调查数据中的伪造。这些方法建立在伪造采访的特定属性上，例如，关于未回答问题的数量或数字的分布，以及它们的相互依赖性。在应用聚类方法时，使用这些标准对实际访谈数据进行分组。我们还考虑启发式优化算法，以获得最佳的集群为不同的标准。此外，如果事前信息的伪造，判别分析是用来识别的典型属性的虚假采访，在平行的统计方法的分析，我们开发的设计特点的问卷，这增加了事后检测的机会伪造。因此，调查数据的质量预计将得到改善，因为事后发现了伪造的访谈，并增加了威慑作用。问卷设计和统计方法进行了测试，在实验设置。

Survey data might be subject to falsifications by interviewers. Based on an analysis of the motivation for such falsifications we develop, test and apply multivariate statistical methods, which can be used to identify falsifications in survey data. The methods build on specific properties of falsified interviews, e.g., with regard to the number of unanswered questions or the distributions of digits, and their interdependence. The grouping of actual interview data is performed using these criteria when applying clustering methods. We also consider heuristic optimisation algorithms for obtaining the best possible clusters for different criteria. Furthermore, if ex ante information on falsifications is available, discriminant analysis is used to identify typical properties of false interviews.In parallel to the analysis of the statistical methods, we develop design features of questionnaires, which increase the chance of ex post detection of falsifications. Thereby, the quality of survey data is expected to improve both due to the ex post identification of faked interviews and an increased deterrence effect. Questionnaire designs and statistical methodology are tested in an experimental setting.