Respondent-specific randomized response technique to estimate sensitive proportion

Dipika Patra; Sanghamitra Pal; Arijit Chaudhuri

doi:https://doi.org/10.59170/stattrans-2023-051

Respondent-specific randomized response technique to estimate sensitive proportion

Dipika Patra Department of Statistics, Seth Anandram Jaipuria College, Kolkata, India ORCID:https://orcid.org/0000-0003-4318-1123 , Sanghamitra Pal Corresponding author. Department of Statistics, West Bengal State University, India ORCID:https://orcid.org/0000-0002-5752-8282 , Arijit Chaudhuri Applied Statistics Unit, Indian Statistical Institute, Kolkata, India ORCID:https://orcid.org/0000-0002-4305-7686 Statistics in Transition new series, vol. 24, 2023, 4, pages: 53-70 Published online: 8 September 2023 https://doi.org/10.59170/stattrans-2023-051 Citation: Patra, D., Pal, S., Chaudhuri, A., 2023. Respondent-specific randomized response technique to estimate sensitive proportion. Statistics in Transition new series, 24(4), pp. 53-70. https://doi.org/10.59170/stattrans-2023-051

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ABSTRACT

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

KEYWORDS

protection of privacy, randomized response, sensitive issues, varying probability sampling

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