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On the Bayesian Estimation of Synthesized Randomized Response Techniques for Obtaining Sensitive Information

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  • Olusegun S. Ewemooje
  • Isaac O. Adeniyi
  • Femi B. Adebola
  • Wilford B. Molefe

Abstract

The reduction of response bias in survey research is crucial ensuring that the collected data accurately represents the target population. In this study, the Bayesian Estimation of the Synthesized Random Response Technique (BESRRT) estimators are proposed as an effective method for minimizing response bias. The BESRRT estimators are being expressed using different priors, such as the Kumaraswamy, Generalized Beta, and Beta‐Nakagami distributions. The study employs numerical data investigation and preanalyzed data to compare the performance of the proposed estimators with other conventional models and assess the efficiency of the proposed technique. The results indicate that the BESRRT estimators, particularly the Beta‐Nakagami Distribution prior estimators, outperform other estimators and can potentially improve the accuracy of survey data for informed decision‐making. Consequently, the study concludes that the proposed method is more effective in reducing response bias in surveys involving sensitive information.

Suggested Citation

  • Olusegun S. Ewemooje & Isaac O. Adeniyi & Femi B. Adebola & Wilford B. Molefe, 2025. "On the Bayesian Estimation of Synthesized Randomized Response Techniques for Obtaining Sensitive Information," International Journal of Mathematics and Mathematical Sciences, John Wiley & Sons, vol. 2025(1).
    • Handle: RePEc:wly:jijmms:v:2025:y:2025:i:1:n:5589512
    DOI: 10.1155/ijmm/5589512
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    References listed on IDEAS

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    1. Adetola Adedamola Adediran & Femi Barnabas Adebola & Olusegun Sunday Ewemooje, 2020. "Unbiased estimator modeling in unrelated dichotomous randomized response," Statistics in Transition New Series, Polish Statistical Association, vol. 21(5), pages 119-132, December.
    2. Pier Francesco Perri & Elvira Pelle & Manuela Stranges, 2016. "Estimating Induced Abortion and Foreign Irregular Presence Using the Randomized Response Crossed Model," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 129(2), pages 601-618, November.
    3. Hua Xin & Jianping Zhu & Tzong-Ru Tsai & Chieh-Yi Hung, 2021. "Hierarchical Bayesian Modeling and Randomized Response Method for Inferring the Sensitive-Nature Proportion," Mathematics, MDPI, vol. 9(19), pages 1-12, October.
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