Abstract

In social and behavioral surveys, researchers often have interest in obtaining separate estimates for the parameters of the study variable in specific domains, categorized according to some attribute(s) such as political affiliation, income status, residency, and ethnical affiliations etc. When complete information about such attributes are not available in advance, surveyors need to include questions in interviews which provide information about such attributes. The truthful response about these attributes can easily be collected when it is nonsensitive to the respondents, but not so for sensitive attributes. The respondents may either refuse to answer the question or provide evasive response about the sensitive attributes. In such cases, reliable estimates for the mean or total of even a nonsensitive quantitative variable in specific domains are quite challenging as there is no truthful information about the domain membership. Consequently, either we are left with unreliable estimates for small area parameters due to nonresponse, or get biased estimates following false responses from respondents. This article emphasizes on obtaining reliable estimates for subpopulation totals using Warner’s randomized response technique. The suggested method enhances response rate by protecting confidentiality of the respondents in exposing their domain membership. A real-world data set from Pakistan Demographic Health Survey (2017–2018) is used to see the applicability of the proposed method followed by a simulation study.

摘要

在社会和行为调查中，研究人员通常有兴趣获得特定领域研究变量参数的单独估计值，这些参数根据某些属性（如政治派别、收入状况、居住地和种族派别等）进行分类。完成后事先无法获得有关此类属性的信息，因此调查员需要在访谈中包含提供有关此类属性信息的问题。当对受访者不敏感时，可以很容易地收集关于这些属性的真实回答，但对于敏感属性则不然。受访者可能拒绝回答问题或对敏感属性提供回避回答。在这种情况下，对特定领域中甚至不敏感的定量变量的平均值或总和的可靠估计都非常具有挑战性，因为没有关于领域成员的真实信息。因此，要么由于无响应而导致我们对小区域参数的估计不可靠，要么在受访者的错误响应后得到有偏差的估计。本文强调使用 Warner 的随机响应技术获得子总体总数的可靠估计。建议的方法通过保护受访者在公开其域成员身份时的机密性来提高响应率。来自巴基斯坦人口健康调查（2017-2018 年）的真实世界数据集用于查看所提出方法的适用性，然后进行模拟研究。