Two-stage unrelated randomized response model to estimate the prevalence of a sensitive attribute

Vishwakarma, Gajendra K.; Kumar, Amod; Kumar, Neelesh

doi:10.1007/s00180-023-01326-8

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…One can think of privacy and efficiency [12]. Vishwakarma et al (2023) developed a two-stage unrela randomized response model [13]. Narjis and Shabir (2021) proposed an RRT model estimating rare sensitive attributes using a Poisson distribution [14].…”

Section: Methodsmentioning

confidence: 99%

“…Additionoduced an enhanced trust feature that enabled respondents to mbling if they felt their responses were not being sufficiently bling alone [6]. espondent privacy was formally recognized during this odel attribute, necessary for motivating respondent truthful-) developed a "unified measure" designed to evaluate quantia single statistic that incorporated the competing elements of Vishwakarma et al ( 2023) developed a two-stage unrelated el [13]. Narjis and Shabir (2021) proposed an RRT model for ributes using a Poisson distribution [14].…”

Section: Methodsmentioning

confidence: 99%

“…The importance of respondent privacy was formally recognized during this timeframe as a key RRT model attribute, necessary for motivating respondent truthfulness, and Gupta et al (2018) developed a "unified measure" designed to evaluate quantitative RRT models based on a single statistic that incorporated the competing elements of privacy and efficiency [12]. Vishwakarma et al (2023) developed a two-stage unrelated randomized response model [13]. Narjis and Shabir (2021) proposed an RRT model for estimating rare sensitive attributes using a Poisson distribution [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Parker,

Gupta,

Khalil

2023

Axioms

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Parker,

Gupta,

Khalil

2023

Axioms

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On the Efficiency of the Newly Developed Composite Randomized Response Technique

Dlamini,

Molefe,

Ewemooje

2024

Computational and Mathematical Methods

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In today’s data‐driven decision‐making era, acquiring accurate information is vital. However, survey research faces challenges with sensitive issues. To address this, the Composite Randomized Response Technique (CRRT) was introduced in estimating the proportion of respondents possessing sensitive attributes. This study revealed that as the model captures more and more people involved in the sensitive attributes (πs) from 0.1 to 0.4, the relative efficiency of CRRT increases from 2.2217 to 678.7843. Hence, CRRT was found to be more efficient than the conventional model, making it a robust approach for surveys targeting sensitive attributes, enhancing data accuracy, and supporting effective policy evaluation and resource allocation.

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Randomized Response Techniques: A Systematic Review from the Pioneering Work of Warner (1965) to the Present

Lee

Tran

et al. 2023

Mathematics

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The randomized response technique is one of the most commonly used indirect questioning methods to collect data on sensitive characteristics in survey research covering a wide variety of statistical applications including, e.g., behavioral science, socio-economic, psychological, epidemiology, biomedical, and public health research disciplines. After nearly six decades since the technique was invented, many improvements of the randomized response techniques have appeared in the literature. This work provides several different aspects of improvements of the original randomized response work of Warner, as well as statistical methods used in the RR problems.

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Two-stage unrelated randomized response model to estimate the prevalence of a sensitive attribute

Cited by 3 publications

References 14 publications

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

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

On the Efficiency of the Newly Developed Composite Randomized Response Technique

Randomized Response Techniques: A Systematic Review from the Pioneering Work of Warner (1965) to the Present

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