Evidence map›Paper›PMID 38524533›Full record

ArticleHeliyon2024

Aiding the well celebrated Kuk's randomized response technique through auxiliary and prior information.

Zawar Hussain, Ishtiaq Hussain, Salman A Cheema, Kalim Ullah, Sultan Salem, Walid Emam, Yusra Tashkandy

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Zawar HussainDepartment of Statistics, Faculty of Computing, The Islamia University of Bahawalpur, Pakistan.
Ishtiaq HussainDepartment of Applied Mathematics, Chung Yuan Christian University, Taoyuan City, 32023, Taiwan.
Salman A CheemaDepartment of Applied Sciences, National Textile University, Faisalabad, Pakistan.
Kalim UllahFoundation University Medical College, Foundation University School of Health Sciences, Pakistan.
Sultan SalemDepartment of Economics, Birmingham Business School, College of Social Sciences, University of Birmingham, Edgbaston, Birmingham, England, B15 2TT, UK.
Walid EmamDepartment of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia.
Yusra TashkandyDepartment of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Asking direct questions in face to face surveys about sensitive traits is an intricate issue. One of the solutions to this issue is the randomized response technique (RRT). Being the most widely used indirect questioning technique to obtain truthful data on sensitive traits in survey sampling RRT has been applied in a variety of fields including behavioral science, socio-economic, psychological, epidemiology, biomedical, criminology, data masking, public health engineering, conservation studies, ecological studies and many others. This paper aims at exploring the methods to subsidize the randomized response technique through additional information relevant to the parameter of interest. Specifically, we plan to contribute by proposing more efficient hybrid estimators compared to existing estimator based on (Kuk, 1990) [31] family of randomized response models. The proposed estimators are based on the methodology of incorporating the pertinent information, available on the basis of either historical records or expert opinion. Specifically, in case of availability of auxiliary information, the regression-cum-ratio estimator is found to be the best to further enhance the estimation through (Kuk, 1990) [31] model while the (Thompson, 1968) [49] shrinkage estimation is observed to be yielding more precise and accurate estimator of sensitive proportion. The findings in this study signify the importance of the proposed methodology. Additionally, to support the mathematical findings, a detailed numerical investigation to evaluate the comparative performances is also conducted. Based on performance analysis, overwhelming evidences are witnessed in the favor of proposed strategies.

Indexed as

Auxiliary variableKuk designRandomized response techniquesSensitive attributeShrinkage estimation procedures

Identifiers

PMID38524533
PMCPMC10958699

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.