Application of the Randomized Response Technique in Obtaining Quantitative Data

Greenberg, Bernard; Kuebler, Roy R.; Abernathy, James R.; Horvitz, Daniel G.

doi:10.2307/2283916

Cited by 61 publications

(66 citation statements)

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“…Second, regardless of whether response symmetry is being used or not, the use of the present CDM is restricted to the assessment of dichotomous attributes only. Even though an extension of the current approach to allow for an assessment of quantitative attributes (Greenberg, Abernathy, & Horvitz, 1969) is not impossible in principle, it is not a straightforward exercise and beyond the scope of the current study. Third, the present RRT models do not allow for the assessment of single individuals; they can only be applied to analyze group means.…”

Section: Discussionmentioning

confidence: 99%

Assessing Sensitive Attributes Using the Randomized Response Technique: Evidence for the Importance of Response Symmetry

Ostapczuk

Moshagen

Zhao

et al. 2009

Journal of Educational and Behavioral Statistics

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Randomized response techniques (RRTs) aim to reduce social desirability bias in the assessment of sensitive attributes but differ regarding privacy protection. The less protection a design offers, the more likely respondents cheat by disobeying the instructions. In asymmetric RRT designs, respondents can play safe by giving a response that is never associated with the sensitive attribute. Symmetric RRT designs avoid the incentive to cheat by not allowing such responses. We tested whether a symmetric variant of a cheating detection model (CDM) increases compliance with the instructions in a survey of academic dishonesty among 2,254 Chinese students. As we observed more noncompliance in the asymmetric than symmetric variant, we recommend the use of symmetric CDMs, which can easily be tested within multinomial models.

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Section: Discussionmentioning

confidence: 99%

Assessing Sensitive Attributes Using the Randomized Response Technique: Evidence for the Importance of Response Symmetry

Ostapczuk

Moshagen

Zhao

et al. 2009

Journal of Educational and Behavioral Statistics

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“…In 1971 Greenberg introduced the unrelated question RRT model, which allows the interviewer to ask questions requiring a quantitative response [21]. We employed the RRT in a general population survey.…”

Section: Methodsmentioning

confidence: 99%

Estimating the Sizes of Populations at High Risk for HIV: A Comparison Study

Jing

et al. 2014

PLoS ONE

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ObjectivesBehavioral interventions are effective strategies for HIV/AIDS prevention and control. However, implementation of such strategies relies heavily on the accurate estimation of the high-risk population size. The multiplier method and generalized network scale-up method were recommended to estimate the population size of those at high risk for HIV by UNAIDS/WHO in 2003 and 2010, respectively. This study aims to assess and compare the two methods for estimating the size of populations at high risk for HIV, and to provide practical guidelines and suggestions for implementing the two methods.MethodsStudies of the multiplier method used to estimate the population prevalence of men who have sex with men in China published between July 1, 2003 and July 1, 2013 were reviewed. The generalized network scale-up method was applied to estimate the population prevalence of men who have sex with men in the urban district of Taiyuan, China.ResultsThe median of studies using the multiplier method to estimate the population prevalence of men who have sex with men in China was 4–8 times lower than the national level estimate. Meanwhile, the estimate of the generalized network scale-up method fell within the range of national level estimate.ConclusionsWhen high-quality existing data are not readily available, the multiplier method frequently yields underestimated results. We thus suggest that the generalized network scale-up method is preferred when sampling frames for the general population and accurate demographic information are available.

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“…Greenberg et al [2] extended the Warner's [1] work to collect the data on quantitative stigmatizing variables. Since then, several authors have worked on quantitative randomized response models including, Eichhorn and Hayre [3], Gupta and Shabbir [4], Gupta and Shabbir [5], Bar-Lev et al [6], Gupta et al [7], Hussain and Shabbir [8], Saha [9], Chaudhuri [10], Hussain and Shabbir [11] and references therein.…”

Section: Introductionmentioning

confidence: 99%

“…The optional randomized response from respondent in the subsample is given by:The expectation of sample response from sample is given by:since . Huang [2] proposed the following estimators of and . where and are the observed means from the two subsamples. The variances of estimators in (12) and (13) are given by: where …”

Section: Introductionmentioning

confidence: 99%

Additive and Subtractive Scrambling in Optional Randomized Response Modeling

2014

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This article considers unbiased estimation of mean, variance and sensitivity level of a sensitive variable via scrambled response modeling. In particular, we focus on estimation of the mean. The idea of using additive and subtractive scrambling has been suggested under a recent scrambled response model. Whether it is estimation of mean, variance or sensitivity level, the proposed scheme of estimation is shown relatively more efficient than that recent model. As far as the estimation of mean is concerned, the proposed estimators perform relatively better than the estimators based on recent additive scrambling models. Relative efficiency comparisons are also made in order to highlight the performance of proposed estimators under suggested scrambling technique.

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Application of the Randomized Response Technique in Obtaining Quantitative Data

Cited by 61 publications

References 0 publications

Assessing Sensitive Attributes Using the Randomized Response Technique: Evidence for the Importance of Response Symmetry

Assessing Sensitive Attributes Using the Randomized Response Technique: Evidence for the Importance of Response Symmetry

Estimating the Sizes of Populations at High Risk for HIV: A Comparison Study

Additive and Subtractive Scrambling in Optional Randomized Response Modeling

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