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Respondent-specific randomized response technique to estimate sensitive proportion

100%
Patra D., Pal S., Chaudhuri A.
Statistics in Transition new series
|
2023
|
vol. 24
|
issue 4
53-70
EN
In estimating the proportion of people bearing a stigmatizing characteristic in a community of people, randomized response techniques are plentifully available in the literature. They are implemented essentially using boxes of similar cards of two distinguishable types. In this paper, we propose a more general procedure using five different types of cards. A respondent-specific randomized response technique is also proposed, in which respondents are allowed to build up the boxes according to their own choices. An immediate objective for this change is to enhance, sense of protection of privacy of the respondents. But as by-products, higher efficiency in terms of actual coverage percentages of confidence intervals and related features are demonstrated by a simulation study, and superior jeopardy levels against divulgence of personal secrecy are also reported to be achievable. AMS subject classification: 62D05.
2
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How privacy may be protected in optional randomized response surveys

100%
Pal S., Chaudhuri A., Patra D.
Statistics in Transition new series
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2020
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vol. 21
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issue 2
61-87
EN
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.
3
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Nonrandomized response model for complex survey designs

88%
Arnab R., Shangodoyin D. K., Arcos A.
Statistics in Transition new series
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2019
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vol. 20
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issue 1
67-86
EN
Warner’s randomized response (RR) model is used to collect sensitive information for a broad range of surveys, but it possesses several limitations such as lack of reproducibility, higher costs and it is not feasible for mail questionnaires. To overcome such difficulties, nonrandomized response (NRR) surveys have been proposed. The proposed NRR surveys are limited to simple random sampling with replacement (SRSWR) design. In this paper, NRR procedures are extended to complex survey designs in a unified setup, which is applicable to any sampling design and wider classes of estimators. Existing results for NRR can be derived from the proposed method as special cases.
4
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Bayesian Techniques in Randomized Response

75%
Krzykowski G.
Przegląd Statystyczny
|
2009
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vol. 56
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issue 2
67-86
PL
Zagadnienie prawdziwości odpowiedzi na drażliwe społecznie lub osobiście pytania ankieterów pojawiło się wraz z początkiem badań ankietowych. Często respondenci próbują uniknąć odpowiedzi lub odpowiadają niezgodnie z prawdą. Powstało wiele technik badawczych i analitycznych korygujących wyniki badań ankietowych pozwalających na ocenę rzeczywistej opinii respondentów. W pracy przedstawiono bayesowską analizę techniki Randomize Response. Zaprezentowano argumentację wyboru różnych rozkładów a priori zarówno nieinformujących, jak i rozkładów kompensujących informacje wstępne opisane rozkładami parametrycznymi.
EN
The issue of truthfulness of the answers given to socially or personally sensitive questions appeared at the very beginning of the construction of the surveys. The respondents often try to avoid answering a sensitive question or they do not tell the truth. Various research and analysis techniques have been developed to adjust the surveys results in order to assess the real opinion of the respondents. The work introduces a bayesian analysis of the Randomized Response technique. It presents arguments for choosing various a priori distributions, both non-informative and compensating for the preliminary information described by parametrized distributions.
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