An Improved Two-stage Randomized Response Model for Estimating the Proportion of Sensitive Attribute

Response Model ◽

Randomized Response Technique ◽

Randomized Response ◽

Two Stage ◽

Sensitive Attribute ◽

Mean And Variance ◽

Efficiency Comparison ◽

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.

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 ◽

Response Model ◽

Randomized Response Technique ◽

Randomized Response ◽

Suggested Model ◽

Two Stage ◽

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.

An efficient two-stage randomized response model under stratified random sampling

Mathematical Population Studies ◽

10.1080/08898480.2016.1222222 ◽

2016 ◽

Vol 23 (4) ◽

pp. 222-238 ◽

Cited By ~ 1

Author(s):

Sally Abdelfatah ◽

Reda Mazloum

Keyword(s):

Random Sampling ◽

Response Model ◽

Randomized Response ◽

Two Stage ◽

Estimation of a rare sensitive attribute in a stratified two-stage unrelated randomized response model by using Poisson probability distribution

Statistics ◽

10.1080/02331888.2018.1472600 ◽

2018 ◽

Vol 52 (4) ◽

pp. 901-918 ◽

Cited By ~ 1

Author(s):

G. N. Singh ◽

Amod Kumar ◽

Gajendra K. Vishwakarma

Keyword(s):

Probability Distribution ◽

Response Model ◽

Randomized Response ◽

Two Stage ◽

Sensitive Attribute ◽

Data Gathering, Analysis and Protection of Privacy Through Randomized Response Techniques: Qualitative and Quantitative Human Traits - Handbook of Statistics ◽

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

10.1016/bs.host.2016.01.008 ◽

2016 ◽

pp. 133-154

Author(s):

S. Abdelfatah ◽

R. Mazloum

Keyword(s):

Random Sampling ◽

Response Model ◽

Randomized Response ◽

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.