A Two Stage Cluster Multiplicative Unrelated Quantitative Attribute Randomized Response Model

2017 ◽  
Vol 19 (4) ◽  
pp. 1897-1906
Author(s):  
Gi-Sung Lee
2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Tanveer A. Tarray ◽  
Housila P. Singh

A stratified randomized response model based on R. Singh, Singh, Mangat, and Tracy (1995) improved two-stage randomized response strategy is proposed. It has an optimal allocation and large gain in precision. Conditions are obtained under which the proposed model is more efficient than R. Singh et al. (1995) and H. P. Singh and Tarray (2015) models. Numerical illustrations are also given in support of the present study.


1975 ◽  
Vol 12 (4) ◽  
pp. 402-407 ◽  
Author(s):  
James E. Reinmuth ◽  
Michael D. Geurts

This article extends the randomized response sampling design to find the intensity of positive action on a sensitive topic among those who have taken a positive action. Sampling properties of the ratio estimate are explored and the model is used to estimate the intensity of shoplifting among shoplifters in a Honolulu shopping center.


2021 ◽  
pp. 004912412110099
Author(s):  
Ghulam Narjis ◽  
Javid Shabbir

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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