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Improved randomized response in additive scrambling models

Author

Listed:
  • Zawar Hussain
  • Mashail M. Al-Sobhi
  • Bander Al-Zahrani
  • Housila P. Singh
  • Tanveer A. Tarray

Abstract

Randomized response models deal with stigmatizing variables appearing in health surveys. Additive and subtractive scrambling in split sample and double response yield unbiased mean and sensitivity estimators of high precision. The split sample method is protective of privacy. The double response method is as protective only conditionally. To achieve the maximum efficiency, the scrambling variables must be similar to each other and the probability of obtaining a true response must be as large as possible. The randomized response procedures yield more efficient estimates of the average total number of classes missed by university students.

Suggested Citation

  • Zawar Hussain & Mashail M. Al-Sobhi & Bander Al-Zahrani & Housila P. Singh & Tanveer A. Tarray, 2016. "Improved randomized response in additive scrambling models," Mathematical Population Studies, Taylor & Francis Journals, vol. 23(4), pages 205-221, October.
    • Handle: RePEc:taf:mpopst:v:23:y:2016:i:4:p:205-221
    DOI: 10.1080/08898480.2015.1087773
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    References listed on IDEAS

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    1. Shaul K. Bar-Lev & Elizabeta Bobovitch & Benzion Boukai, 2004. "A note on randomized response models for quantitative data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 60(3), pages 255-260, November.
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