Department of Statistics, University of Malakand, Lower Dir, KP, Pakistan.
PLoS One. 2023 Apr 26;18(4):e0284995. doi: 10.1371/journal.pone.0284995. eCollection 2023.
In survey sampling, the randomized response technique is a useful tool to collect reliable data in many fields including sociology, education, economics, and psychology etc. Over the past few decades, many variants of quantitative randomized response models have been developed by researchers. The existing literature on randomized response models lacks a neutral comparative study of different models to help the practitioners choose the appropriate model for a given practical problem. In most of the existing studies, the authors tend to show only the favorable results by hiding the cases where their suggested models are inferior to the existing models. This approach often leads to biased comparisons which may badly misguide the practitioners when choosing a randomized response model for a practical problem at hand. This paper attempts a neutral comparison of six existing quantitative randomized response models using separate as well as joint measures of respondent-privacy and model-efficiency. The findings suggest that one model may perform better than the other model in terms of efficiency but may perform worse when other metrics of model quality are taken into account. The current study guides practitioners in choosing the right model for a given problem under a particular situation.
在调查抽样中，随机响应技术是一种在社会学、教育学、经济学和心理学等多个领域收集可靠数据的有用工具。在过去的几十年中，研究人员已经开发出了许多定量随机响应模型的变体。现有的随机响应模型文献缺乏对不同模型的中立性比较研究，无法帮助从业者为给定的实际问题选择合适的模型。在大多数现有研究中，作者往往只展示有利的结果，而隐藏了他们建议的模型不如现有模型的情况。这种方法往往会导致有偏差的比较，当从业者为手头的实际问题选择随机响应模型时，可能会产生严重的误导。本文试图使用受访者隐私和模型效率的单独和联合度量标准，对六种现有的定量随机响应模型进行中立比较。研究结果表明，一个模型在效率方面可能优于另一个模型，但在考虑到其他模型质量指标时，可能表现不佳。本研究指导从业者在特定情况下为给定问题选择正确的模型。
