Randomized response models (RRMs) aim at increasing the validity of measuring sensitive attributes by eliciting more honest responses through anonymity protection of respondents. This anonymity protection is achieved by implementing randomization in the questioning procedure. On the other hand, this randomization increases the sampling variance and, therefore, increases sample size requirements. The present work aims at countering this drawback by combining RRMs with curtailed sampling, a sequential sampling design in which sampling is terminated as soon as sufficient information to decide on a hypothesis is collected. In contrast to nontruncated sequential designs, the curtailed sampling plan includes the definition of a maximum sample size and subsequent prevalence estimation is easy to conduct. Using this approach, resources can be saved such that the application of RRMs becomes more feasible. An R Shiny web application is provided for simplified application of the proposed procedures. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

中文翻译：

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使用随机响应模型提高调查效率：基于缩减抽样的顺序方法。

随机响应模型 (RRM) 旨在通过对受访者的匿名保护引发更诚实的响应，从而提高测量敏感属性的有效性。这种匿名保护是通过在提问过程中实施随机化来实现的。另一方面，这种随机化增加了抽样方差，因此增加了对样本量的要求。目前的工作旨在通过将 RRM 与缩减抽样相结合来克服这一缺点，缩减抽样是一种顺序抽样设计，其中一旦收集到足够的信息来决定假设，抽样就会终止。与非截断序列设计相比，缩减抽样计划包括最大样本量的定义，随后的流行率估计很容易进行。使用这种方法，可以节省资源，使 RRM 的应用变得更加可行。提供了一个 R Shiny Web 应用程序，用于简化建议程序的应用。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）