Proficient randomized response model based on blank card strategy to estimate the sensitive parameter under negative binomial distribution

Singh, G. N.; Singh, Chandraketu

doi:10.1016/j.asej.2021.10.006

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…The work of Narjis and Shabbir [16] and Hsieh et al [17] is also to be noted in the case of the use of two-stage RR models in order to find the commonness of a sensitive characteristic. Singh and Singh [18] offered a technique for finding the population fraction of a stigmatized characteristic, making use of very well-known distribution that is negative binomial distribution for his work. Singh et al [19] projected a three-stage randomized response model, making use of poisson distribution.…”

Section: Survey Sampling and Randomized Responsementioning

confidence: 99%

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Estimation of Hidden Logits Using Several Randomized Response Techniques

Halim,

Arshad,

Alomair

et al. 2023

Symmetry

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In survey sampling, we aspire to obtain sound and consistent responses, which are not achieved while dealing with sensitive issues. Frequently, respondents give elusive responses to sensitive questions, so we employ randomized response techniques that facilitate finding an appropriate proportion of socially sensitive characteristics. In the present study, we proposed a hidden logit estimation method using Huang, Warner, and Mangat’s randomized response techniques. This study depicts that the estimates become closer to the standard logits as the values of p increase. We found that the hidden logit estimates obtained by the Huang randomized response technique were nearer to the parametric values, in contrast to the other existing techniques, and we demonstrate an increase in accuracy as well. The simulation-based AIC and SIC values are used to assess model performance. We found that the Huang model is the best model for the proposed hidden logit method. This paper contributes towards the application of logistic models in the case of sensitive or socially stigmatized issues.

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