一个不相关随机反应技术与逻辑回归分析的替代方案。

Pub Date : 2016-11-01 Epub Date: 2016-01-25 DOI:10.1007/s10260-016-0351-1
Shu-Hui Hsieh, Shen-Ming Lee, Chin-Shang Li, Su-Hao Tu
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引用次数: 4

摘要

随机反应技术(RRT)是一种重要的工具,通常用于保护受访者的隐私,避免在敏感问题的调查中有偏见的答案。在这项工作中,我们考虑联合使用格林伯格等人的非相关问题RRT (J Am Stat Assoc 64:520- 539,1969)和华纳的相关问题RRT (J Am Stat Assoc 60:63- 69,1965)来处理非相关问题RRT中无害问题的问题。与Greenberg等人(1969)现有的无相关问题RRT不同,该方法可以通过使用Warner(1965)的相关问题RRT提供更多关于无害问题的信息,从而有效提高Scheers和Dayton (J Am Stat Assoc 83:969-974, 1988)的最大似然估计器的效率。然后,我们可以使用逻辑回归来估计敏感特征的普遍性。在这种新设计中,我们提出了转换方法并提供了大样本特性。本文以婚外关系调查和有线电视调查为例,提出了联合条件似然方法。作为本研究的一部分,我们对所提出的方法的相对效率进行了模拟研究。此外,我们利用两项调查研究比较了不同情景下的分析结果。
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An alternative to unrelated randomized response techniques with logistic regression analysis.

The randomized response technique (RRT) is an important tool that is commonly used to protect a respondent's privacy and avoid biased answers in surveys on sensitive issues. In this work, we consider the joint use of the unrelated-question RRT of Greenberg et al. (J Am Stat Assoc 64:520-539, 1969) and the related-question RRT of Warner (J Am Stat Assoc 60:63-69, 1965) dealing with the issue of an innocuous question from the unrelated-question RRT. Unlike the existing unrelated-question RRT of Greenberg et al. (1969), the approach can provide more information on the innocuous question by using the related-question RRT of Warner (1965) to effectively improve the efficiency of the maximum likelihood estimator of Scheers and Dayton (J Am Stat Assoc 83:969-974, 1988). We can then estimate the prevalence of the sensitive characteristic by using logistic regression. In this new design, we propose the transformation method and provide large-sample properties. From the case of two survey studies, an extramarital relationship study and a cable TV study, we develop the joint conditional likelihood method. As part of this research, we conduct a simulation study of the relative efficiencies of the proposed methods. Furthermore, we use the two survey studies to compare the analysis results under different scenarios.

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