A composite class of estimators using scrambled response mechanism for sensitive population mean in successive sampling

Kumari, Puja; Trisandhya, Pidugu

doi:10.1080/03610926.2017.1422762

Cited by 5 publications

(6 citation statements)

References 19 publications

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“…ther Quantitative RRT model based on an entirely asking each respondent the sensitive question and ir response, Greenberg suggested that each respondtions-either the sensitive quantitative question or antitative question with a known probability distrient unknown to the researcher. While the researcher onses, the respondents' confidentiality would none searcher would not know which question each indier's and Greenberg's models were made over the oposed a means of estimating the sensitivity level of nt kinds of scrambling techniques were explored by (2018), and Priyanka and Trisandhya (2019) [8][9][10]. g optionality to RRT models (respondents may opt hether they personally find the "sensitive" question d model efficiency [11].…”

Section: Methodsmentioning

confidence: 99%

“…We will use the same metrics to compare the characteristics of the MOET model to those of the OET model. [8][9][10]. d that adding optionality to RRT models (respondents may opt cording to whether they personally find the "sensitive" question ly improved model efficiency [11].…”

Section: Methodsmentioning

confidence: 99%

“…ral advancements to Warner's and Greenberg's models were made over the tha, and Aggarwal (2018) proposed a means of estimating the sensitivity level of binary question [7]. Different kinds of scrambling techniques were explored by Perri (2011), Singh et al ( 2018), and Priyanka and Trisandhya (2019) [8][9][10]. al.…”

Section: Moet: Privacy Measurementioning

confidence: 99%

“…Metha, and Aggarwal (2018) proposed a means of estimating the sensitivity level of a sensitive binary question [7]. Different kinds of scrambling techniques were explored by Diana and Perri (2011), Singh et al (2018), and Priyanka and Trisandhya (2019) [8][9][10]. Gupta et al (2002) showed that adding optionality to RRT models (respondents may opt in or opt out of the RRT according to whether they personally find the "sensitive" question to be sensitive) significantly improved model efficiency [11].…”

Section: Introductionmentioning

confidence: 99%

“…ts to Warner's and Greenberg's models were made over the al (2018) proposed a means of estimating the sensitivity level of[7]. Different kinds of scrambling techniques were explored by ingh et al (2018), andPriyanka and Trisandhya (2019)…”

mentioning

confidence: 99%

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A Mixture Quantitative Randomized Response Model That Improves Trust in RRT Methodology

Parker,

Gupta,

Khalil

2023

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The Quantitative Randomized Response Technique (RRT) can be used by researchers to obtain honest answers to questions that, due to their sensitive (socially undesirable, dangerous, or even illegal) nature, might otherwise invoke partially or completely falsified responses. Over the years, Quantitative RRT models, sometimes called Scrambling models, have been developed to incorporate such advancements as mixture, optionality and enhanced trust, each of which has important benefits. However, no single model incorporates all of these features. In this study, we propose just such a unified model, which we call the Mixture Optional Enhanced Trust (MOET) model. After developing methodologies to assess MOET based on standard approaches and using them to explore the key characteristics of the new model, we show that MOET has superior efficiency compared to the Quantitative Optional Enhanced Trust (OET) model. We also show that use of the model’s mixture capability allows practitioners to optimally balance the model’s efficiency with its privacy, making the model adaptable to a wide variety of research scenarios.

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