A stratified unrelated question randomized response model

Kim, Jong Min; Elam, Matthew E.

doi:10.1007/s00362-006-0327-6

Cited by 44 publications

(23 citation statements)

References 4 publications

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“…A stratified unrelated question RR model using Singh et al (1994) procedure, an improved unrelated question RR model for completely truthful reporting has been proposed. It has been shown that for the prior information given, the proposed stratified unrelated question RR model using Neyman allocation is more efficient in terms of variance than Kim and Elam (2007) unrelated question stratified RR model. A notable point in this study is that the proposed model is better than the one earlier considered by Kim and Elam (2007).…”

Section: Resultsmentioning

confidence: 99%

“…1. Since all the PRE values in Table 1 are greater than 100, our stratified unrelated question RR model using Neyman allocation is more efficient in terms of variance than Kim and Elam (2007) stratified RR model under the assumptions given and the prior information used. …”

Section: Theoremmentioning

confidence: 99%

“…In the subsection 2.2, we demonstrate the findings of empirical studies. The empirical studies show that, for the prior information given, the proposed model is more efficient in terms of variance than Kim and Elam's (2007) model. In Section 3 we offer some concluding remarks.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we consider the use of stratified random sampling with Neyman allocation to Singh et al (1994) Kim and Elam's (2007) model. Numerical illustrations and graphs are also given in support of the present study.…”

Section: Introductionmentioning

confidence: 99%

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An Adroit Stratified Unrelated Question Randomized Response Model using Neyman Allocation

Singh

Tarray

2014

Sri Lankan J App Stats

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In this paper we consider the use of stratified random sampling with Neyman allocation to Singh et al. (1994) Kim and Elam's (2007) model. Numerical illustrations and graphs are also given in support of the present study. unrelated question randomized response strategy for completely truthful reporting. It has been shown that for the prior information given, our new model is more efficient in terms of variance (in the case of completely truthful reporting) than

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Section: Resultsmentioning

confidence: 99%

Section: Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Adroit Stratified Unrelated Question Randomized Response Model using Neyman Allocation

Singh

Tarray

2014

Sri Lankan J App Stats

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“…Then randomized response (RR) technique is applied to each stratum. Many researchers have extended RR models to stratification such as [1,8,10,11,12,13,20,22]. In the following sub section we also extend proposed models to stratified sampling framework.…”

Section: Proposed Models Under Stratified Random Samplingmentioning

confidence: 99%

Two Mixed Randomized Response Models under Simple and Stratified Random Sampling with Replacement Schemes

Fatima¹,

Fatima²,

Hussain³

2016

HJMS

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We have proposed two mixed randomized response models for surveying sensitive issues. The properties of proposed estimators are derived under the simple and stratified random sampling schemes while considering completely and less than completely truthful reporting cases. We proved that the proposed models are unconditionally efficient than [13,15,16,20]. In order to get the idea of gain in efficiency and model stability numerical and graphical efficiency comparisons are done for the two models under two mentioned cases.

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Variance jackknife estimation for randomized response surveys: A simulation study and an application to explore cheating in exams and bullying

Arnab

Cobo

2020

Comp and Math Methods

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The aim of the randomized response (RR) technique is to decrease social desirability bias, thus guaranteeing confidentiality, improving respondent cooperation, and obtaining reliable estimates. This technique obtains stronger estimates of sensitive characteristics, compared to direct questioning, by reducing respondents motivation to report falsely their attitudes. In this paper, we have considered the problem of estimation of variance of an estimator based on an RR survey data obtained from a complex survey design by using resampling techniques. The performance of the proposed jackknife modified estimator is tested by simulation studies. The simulation studies reveal that there is a decrease in the relative bias and the relative mean square error for all RR schemes. An RR survey was conducted to investigate sensitive behaviors such as cheating in exams and bullying. The application of the new jackknife confidence intervals revealed substantial increase in high‐tech cheating and bullying in the university campuses.

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A stratified unrelated question randomized response model

Abstract: Randomized response, Stratified random sampling, Sensitive characteristics, Dichotomous population, Estimation of proportion,

Cited by 44 publications

References 4 publications

An Adroit Stratified Unrelated Question Randomized Response Model using Neyman Allocation

An Adroit Stratified Unrelated Question Randomized Response Model using Neyman Allocation

Two Mixed Randomized Response Models under Simple and Stratified Random Sampling with Replacement Schemes

Variance jackknife estimation for randomized response surveys: A simulation study and an application to explore cheating in exams and bullying

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