Identification of Falsifications in Survey Data

在项目第一阶段的基础上，我们从三个方面扩展了我们的分析。第一，考虑到部分伪造的情况，即面试人员仅伪造了部分面试和/或部分面试。分析了现有方法在这种情况下仍然有用的程度。其次，对面试官动机的研究进行了扩展，同时考虑到心理学的动机理论。基于这一理论背景，本文提出了部分证伪的类型和程度的假设。我们将使用一个实验来测试这些假设，并得出进一步的指标集中在部分证伪。第三，在存在部分伪造的情况下，使用确定性聚类方法似乎是不够的。相反，概率聚类方法被认为是允许描述的数据为一个采访者作为两个分布的混合物。为了克服这些方法的数值问题，使用优化算法。此外，建模方法被扩展到两个以上的集群，允许不同类型的伪造取决于动机或经验等因素。所有结果都用于改进问卷和调查方法，以通过提高访谈者的积极性和增加事后发现的机会来阻止伪造。

Building on the first phase of the project, we extend our analysis in three aspects. First, the case of partial falsifications is taken into account, i.e. that interviewers falsify only some interviews and/or only parts of interviews. It is analyzed to what extent the existent methods are still useful in this setting. Second, the study of interviewers’ motivation is extended taking into account also theories of motivation from psychology. Based on this theoretical background, hypothesis about the type and extent of partial falsifications are derived. We will use an experiment to test these hypotheses and to derive further indicators focusing on partial falsifications. Third, in the presence of partial falsifications, the use of deterministic clustering methods does not appear adequate. Instead, probabilistic clustering methods are considered which allow describing the data for one interviewer as a mixture of two distributions. To overcome numerical problems of these methods, optimization heuristics are used. Furthermore, the modelling approach is extended to more than two clusters, allowing for different types of falsifications depending on factors such as motivation or experience. All results are used to improve questionnaires and survey methodology in order to deter falsifications by enhancing interviewers’ motivation and increasing the chance of ex post detection.