Department of Statistics, University of Malakand, Khyber Pakhtunkhwa, Pakistan.
Department of Statistics, University of Wah, Wah Cantt, Pakistan.
PLoS One. 2023 Oct 27;18(10):e0293628. doi: 10.1371/journal.pone.0293628. eCollection 2023.
In social surveys, the randomized response technique can be considered a popular method for collecting reliable information on sensitive variables. Over the past few decades, it has been a common practice that survey researchers develop new randomized response techniques and show their improvement over previous models. In majority of the available research studies, the authors tend to report only those findings which are favorable to their proposed models. They often tend to hide the situations where their proposed randomized response models perform worse than the already available models. This approach results in biased comparisons between models which may influence the decision of practitioners about the choice of a randomized response technique for real-life problems. We conduct a neutral comparative study of four available quantitative randomized response techniques using separate and combined metrics of respondents' privacy level and model's efficiency. Our findings show that, depending on the particular situation at hand, some models may be better than the other models for a particular choice of values of parameters and constants. However, they become less efficient when a different set of parameter values are considered. The mathematical conditions for efficiency of different models have also been obtained.
在社会调查中，随机响应技术可以被认为是一种收集敏感变量可靠信息的流行方法。在过去的几十年中，调查研究人员开发新的随机响应技术并展示其对以前模型的改进已经成为一种常见做法。在大多数现有研究中，作者往往只报告那些有利于他们提出的模型的发现。他们往往倾向于隐藏那些他们提出的随机响应模型表现不如现有模型的情况。这种方法导致了模型之间的有偏差比较，这可能会影响实践者在实际问题中选择随机响应技术的决策。我们使用受访者隐私水平和模型效率的单独和组合指标，对四种可用的定量随机响应技术进行了中立的比较研究。我们的研究结果表明，根据特定情况，某些模型在特定参数值和常数的选择下可能优于其他模型。然而，当考虑不同的参数值集时，它们的效率就会降低。不同模型效率的数学条件也已经得到了。
