Estimation for sensitive characteristics using optional randomized response technique

Huang, Kuo-Chung

doi:10.1007/s11135-007-9082-6

Cited by 25 publications

(7 citation statements)

References 17 publications

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“…In the method proposed by [7], the interviewee was asked to choose, by means of a randomization device, from two questions; one concerned the sensitive variable and the other was unrelated (both were of the same order of magnitude). Other important papers in this regard include [8][9][10][11][12][13][14][15][16][17][18][19][20][21], together with the contributions compiled by [22][23][24][25][26]. When dealing with quantitative sensitive variables, the idea is that respondents should not disclose the true value of the sensitive variable but rather provide a scrambled value, which is obtained by algebraically perturbing the true response.…”

Section: Introductionmentioning

confidence: 99%

Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

2021

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Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.

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Section: Introductionmentioning

confidence: 99%

Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

2021

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“…A large number of developments following Chaudhuri and Mukerjee (1985) approach were proposed by Gupta (2001), Gupta et al (2002), Pal (2008) and many others. Subsequent developments are due to Arnab (2004), Chaudhuri and Saha (2005), Saha (2007), Huang (2008), Arnab and Rueda (2016) among others with slight differences in approaches. As we see, in ORR a sampled person is offered an option either to (i) report directly whether he/she bears a stigmatizing feature, say A ( which may mean alcoholism or testing HIV positive, etc.)…”

Section: Introductionmentioning

confidence: 99%

How privacy may be protected in optional randomized response surveys

Pal

Chaudhuri

Patra

2020

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There are materials in literature about how privacy on stigmatizing features like alcoholism, history of tax-evasion, or testing positive in AIDS-related testing may be partially protected by a proper application of randomized response techniques (RRT). The paper demonstrates what amendments are necessary for this approach while applying optional RRTs covering qualitative characteristics, permitting a sampled respondent either to directly reveal sensitive data or choose a randomized response respectively with complementary probabilities. Only a few standard RRTs are illustrated in the text.

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“…Gjestvang and Singh [9] present a forced quantitative randomized RRM. Huang [10] present his model to estimate mean and sensitivity level of stigmatizing variables. Gjestvang and Singh [11] present a different type of additive model in which selected respondents scramble their responses for both assertions.…”

Section: Introductionmentioning

confidence: 99%

Improved Randomized Response in Optional Scrambling Models

Hussain¹,

Shahid²

2019

JSTA

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In the present study, we discuss the issue of increasing the respondents cooperation in sensitive surveys. When the question is highly sensitive then the cooperation from the respondents is decreased. We propose two optional randomized response models (ORRMs) to increase the respondents cooperation. A comparison of proposed ORRMs with [1] two-and three-stage models have been made. It is found that, in estimating mean and sensitivity level, the proposed strategies perform better than [1] twoand three-stage models. A comparison is also made among the [1] two-and three-stage models and proposed ORRMs to identify the best one. Numerical illustration are also given in favor of the algebraic results.

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Estimation for sensitive characteristics using optional randomized response technique

Abstract: Percent relative efficiency, Privacy protection, Optional randomized response, Randomization device, Sampling survey,

Cited by 25 publications

References 17 publications

Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

How privacy may be protected in optional randomized response surveys

Improved Randomized Response in Optional Scrambling Models

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