scholarly journals The Three-Stage Cluster Randomized Response Model for Obtaining Sensitive Information

2003 ◽  
Vol 10 (1) ◽  
pp. 247-256
Author(s):  
Gi Sung Lee ◽  
Ki Hak Hong ◽  
Chang Kyoon Son ◽  
Young Mee Jung
Keyword(s):  
Sensitive Information ◽  
Response Model ◽  
Randomized Response ◽  
Cluster Randomized ◽  
Randomized Response Model

The Collection of Sensitive Information Using a Two-Stage, Randomized Response Model

1975 ◽  
Vol 12 (4) ◽  
pp. 402-407 ◽  
Author(s):  
James E. Reinmuth ◽  
Michael D. Geurts
Keyword(s):  
Sampling Design ◽  
Shopping Center ◽  
Sensitive Information ◽  
Response Model ◽  
Randomized Response ◽  
Two Stage ◽  
Ratio Estimate ◽  
Positive Action ◽  
Randomized Response Model

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.

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

scholarly journals A Remark on Gupta, Gupta and Singh Optional Randomized Response Model

2019 ◽  
Vol 15 (1) ◽  
pp. 43-73
Author(s):  
H. P. Singh ◽  
S. M. Gorey
Keyword(s):  
Response Model ◽  
Randomized Response ◽  
Numerical Illustration ◽  
The Mean ◽  
Randomized Response Model

Abstract Gupta et al (2002) suggested an optional randomized response model under the assumption that the mean of the scrambling variable S is ‘unity’ [i.e. µs = 1]. This assumption limits the use of Gupta et al’s (2002) randomized response model. Keeping this in view we have suggested a modified optional randomized response model which can be used in practice without any supposition and restriction over the mean (µs) of the scrambling variables S. It has been shown that the estimator of the mean of the stigmatized variable based on the proposed optional randomized response sampling is more efficient than the Eicchorn and Hayre (1983) procedure and Gupta et al’s (2002) optional randomized technique when the mean of the scrambling S is larger than unity [i.e. µs > 1]. A numerical illustration is given in support of the present study.

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