An Efficient Randomized Response Model Using Two Decks of Cards Under Simple and Stratified Random Sampling

The randomized response technique (RRT) is an effective method designed to obtain the stigmatized information from respondents while assuring the privacy. In this study, we propose a new two-stage RRT model to estimate the prevalence of sensitive attribute ([Formula: see text]). A simulation study shows that the empirical mean and variance of proposed estimator are close to corresponding theoretical values. The utility of proposed two-stage RRT model under stratification is also explored. An efficiency comparison between proposed two-stage RRT model and some existing RRT models is carried out numerically under simple and stratified random sampling.

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An Improved Two-stage Stratified Randomized Response Model for Estimating Sensitive Proportion

Sociological Methods & Research ◽

10.1177/0049124119875963 ◽

2019 ◽

pp. 004912411987596

Author(s):

Zawar Hussain ◽

Salman Arif Cheema ◽

Ishtiaq Hussain

Keyword(s):

Random Sampling ◽

Numerical Study ◽

Stratified Sampling ◽

Stratified Random Sampling ◽

Response Model ◽

Randomized Response Technique ◽

Randomized Response ◽

Suggested Model ◽

Two Stage ◽

Randomized Response Model

This article is about making correction in Tarray, Singh, and Zaizai model and further improving it when stratified random sampling is necessary. This is done by using optional randomized response technique in stratified sampling using a combination of Mangat and Singh, Mangat, and Greenberg et al. models. The suggested model has been studied assuming proportional and Neyman allocation schemes. Numerical results show larger gains in efficiency. Through a detailed numerical study, it is established that the suggested model is relatively more efficient than the Kim and Warde model and the models mentioned above.

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A Modiﬁed Forced Randomized Response Model

Journal of the Nigerian Society of Physical Sciences ◽

10.46481/jnsps.2020.30 ◽

2020 ◽

pp. 36-50

Author(s):

Adefemi Adeniran ◽

A. A. Sodipo ◽

C. G. Udomboso

Keyword(s):

Random Sampling ◽

Simple Random Sampling ◽

Sampling Techniques ◽

Response Model ◽

Randomized Response ◽

Numerical Illustration ◽

Proposed Model ◽

Efficiency Comparison ◽

Randomized Response Model ◽

Sensitive Group

In this paper, we proposed a new Randomized Response Model (RRM) that estimate proportion of people in a population (P) belonging to a sensitive group (S) under study. Simple random sampling with replacement and stratiﬁed simple random sampling scheme were adopted. Maximum likelihood and Bayesian estimation procedures of the proposed model were developed and compared. The sampling distribution (expectation and variance) of the proposed estimator under the two sampling techniques, eﬃciency comparison of the proposed model with some existing models, and numerical illustration of all the compared models were also explored. We found that the proposed model outperformed other existing RRMs in terms of eﬃciency and it proved to be more protective in designing survey for sensitive related issues.

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An alternative approach for construction of strata using quantified sensitivity level

Journal of Applied and Natural Science ◽

10.31018/jans.v13i3.2679 ◽

2021 ◽

Vol 13 (3) ◽

pp. 807-814

Author(s):

Dejen Agegnehu ◽

P. K. Mahajan ◽

R. K. Gupta

Keyword(s):

Exponential Distribution ◽

Random Sampling ◽

Sampling Design ◽

Random Variable ◽

Simple Random Sampling ◽

Response Model ◽

Randomized Response ◽

Alternative Approach ◽

Sensitivity Level ◽

Randomized Response Model

The study is investigated on an alternative method for the construction of strata using sensitivity level when the samples are selected with simple random sampling with replacement (SRSWR) and the data are collected by scrambled optional randomization technique on the sensitive characters. Thus, the optional randomized response model , where k is a random variable having value 1 if the response is scrambled and 0 otherwise, was considered for finding out Approximate Optimum Strata Boundaries by minimizing the variance of the estimator . The cum. was proposed for finding out Approximate Optimum Strata Boundary in Neyman allocation for the optional scrambled response. This is applicable for wider classes of sampling design and estimators in stratification. The proposed rule on optional scrambled randomized response is efficient and can be used effectively for the construction of optimum strata boundary via Rectangular, Right triangular and Exponential distribution.

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