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

2016 ◽  
pp. 133-154
Author(s):  
S. Abdelfatah ◽  
R. Mazloum
Keyword(s):  
Random Sampling ◽  
Stratified Random Sampling ◽  
Response Model ◽  
Randomized Response ◽  
Randomized Response Model

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

2021 ◽  
pp. 004912412110099
Author(s):  
Ghulam Narjis ◽  
Javid Shabbir
Keyword(s):  
Random Sampling ◽  
Stratified Random Sampling ◽  
Response Model ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Two Stage ◽  
Sensitive Attribute ◽  
Mean And Variance ◽  
Efficiency Comparison ◽  
Randomized Response Model

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

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.

scholarly journals A Modified Forced Randomized Response Model

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.

scholarly journals An alternative approach for construction of strata using quantified sensitivity level

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.   

scholarly journals An Additive Stratified Quantitative Attribute Randomized Response Model

2014 ◽  
Vol 27 (2) ◽  
pp. 239-247
Author(s):  
Gi-Sung Lee ◽  
Seung-Chul Ahn ◽  
Ki-Hak Hong ◽  
Chang-Kyoon Son
Keyword(s):  
Response Model ◽  
Randomized Response ◽  
Randomized Response Model
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