An efficient new scrambled response model for estimating sensitive population mean in successive sampling

Narjis, Ghulam; Shabbir, Javid

doi:10.1080/03610918.2021.1986528

Cited by 11 publications

(26 citation statements)

References 28 publications

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“…Khalil et al [ 7 ] conducted a study in the analysis of the influence of observational errors on the mean estimators in sample surveys of sensitive-type variables. An enhanced form of optional quantitative randomized response models was also presented by Narjis and Shabbir [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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A novel randomized scrambling technique for mean estimation of a finite population

Azeem,

Asadullah,

Ijaz

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

“…Azeem and Ali [ 19 ] compared six existing randomized models using various evaluation measures. Azeem et al [ 20 ] developed an efficient modification of the Narjis and Shabbir [ 8 ] technique.…”

Section: Introductionmentioning

confidence: 99%

A novel randomized scrambling technique for mean estimation of a finite population

Azeem,

Asadullah,

Ijaz

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Gupta et al [ 9 ] presented a measure for evaluation of randomized response models by quantifying the respondents’ privacy and efficiency as a single number. Narjis and Shabbir [ 10 ] suggested an efficient variant of the Gjestvang and Singh [ 6 ] technique for data collection on quantitative sensitive variables. Khalil et al [ 11 ] analyzed the influence of measurement errors on the mean estimator of the sensitive quantitative variable.…”

Section: Introductionmentioning

confidence: 99%

A neutral comparative analysis of additive, multiplicative, and mixed quantitative randomized response models

Azeem

Ali

2023

PLoS ONE

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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.

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“…Diana and Perri [4] introduced a technique that was based on additive and multiplicative scrambling variables. A quantitative technique for sensitive surveys was presented by Gjestvang and Singh [5], which has recently been further enhanced by Narjis and Shabbir [6]. A research study conducted by Khalil et al [7] analyzed the effect of measurement errors on mean estimators in sensitive surveys.…”

Section: Introductionmentioning

confidence: 99%

Using the exponential function of scrambling variable in quantitative randomized response models

Azeem

2023

Math Methods in App Sciences

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In sample surveys, refusals and untruthful reporting by the respondents are serious issues often faced by the researchers during the data collection process. Over the past few decades, randomized response models have gained wide popularity among survey statisticians as a tool to reduce the non‐response rate. The existing literature on quantitative randomized response models utilize the scrambling variable either as an additive term or as a multiplicative factor. This paper introduces the novel idea of the use of the exponential function of scrambling variable in quantitative randomized response models. Two new quantitative randomized response models are proposed based on exponential scrambling variable. It is found that the new randomized response models are more efficient than the existing quantitative randomized response models. The mathematical properties of the mean estimator of the sensitive variable under the new proposed methods have been discussed. Moreover, the joint and separate measures of privacy protection and efficiency of the proposed and existing models have also been discussed. Further, to illustrate the practical implementation of the suggested models, an example of data collection is also presented.

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An efficient new scrambled response model for estimating sensitive population mean in successive sampling

Cited by 11 publications

References 28 publications

A novel randomized scrambling technique for mean estimation of a finite population

A novel randomized scrambling technique for mean estimation of a finite population

A neutral comparative analysis of additive, multiplicative, and mixed quantitative randomized response models

Using the exponential function of scrambling variable in quantitative randomized response models

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