Two - stage stratified partial randomized response strategies

Author(s):  
Housila P. Singh ◽  
Tanveer A. Tarray
2020 ◽  
pp. 004912412091495
Author(s):  
Shu-Hui Hsieh ◽  
Shen-Ming Lee ◽  
Chin-Shang Li

Surveys of income are complicated by the sensitive nature of the topic. The problem researchers face is how to encourage participants to respond and to provide truthful responses in surveys. To correct biases induced by nonresponse or underreporting, we propose a two-stage multilevel randomized response (MRR) technique to investigate the true level of income and to protect personal privacy. For a wide range of applications, we present a proportional odds model for two-stage MRR data and apply inverse probability weighting and multiple imputation methods to deal with covariates on some subjects that are missing at random. A simulation study is conducted to investigate the effects of missing covariates and to evaluate the performance of the proposed methods. The practicality of the proposed methods is illustrated with the regular monthly income data collected in the Taiwan Social Change Survey. Furthermore, we provide an estimate of personal regular monthly mean income.


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.


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