IDEAS home Printed from https://ideas.repec.org/a/spr/metrik/v60y2004i3p255-260.html
   My bibliography  Save this article

A note on randomized response models for quantitative data

Author

Listed:
  • Shaul K. Bar-Lev
  • Elizabeta Bobovitch
  • Benzion Boukai

Abstract

Standard randomized response (RR) models deal primarily with surveys which usually require a ‘yes’ or a ‘no’ response to a sensitive question, or a choice for responses from a set of nominal categories. As opposed to that, Eichhorn and Hayre (1983) have considered survey models involving a quantitative response variable and proposed an RR technique for it. Such models are very useful in studies involving a measured response variable which is highly ‘sensitive’ in its nature. Eichhorn and Hayre obtained an unbiased estimate for the expectation of the quantitative response variable of interest. In this note we propose a procedure which uses a design parameter (controlled by the experimenter) that generalizes Eichhorn and Hayre’s results. Such a procedure yields an estimate for the desired expectation which has a uniformly smaller variance. Copyright Springer-Verlag 2004

Suggested Citation

  • 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.
    • Handle: RePEc:spr:metrik:v:60:y:2004:i:3:p:255-260
    DOI: 10.1007/s001840300308
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s001840300308
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s001840300308?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Giancarlo Diana & Pier Perri, 2011. "A class of estimators for quantitative sensitive data," Statistical Papers, Springer, vol. 52(3), pages 633-650, August.
    2. Singh, Sarjinder & Kim, Jong-Min, 2011. "A pseudo-empirical log-likelihood estimator using scrambled responses," Statistics & Probability Letters, Elsevier, vol. 81(3), pages 345-351, March.
    3. Priyanka Kumari & Trisandhya Pidugu, 2019. "Modelling Sensitive Issues On Successive Waves," Statistics in Transition New Series, Polish Statistical Association, vol. 20(1), pages 41-65, March.
    4. Kuo-Chung Huang, 2010. "Unbiased estimators of mean, variance and sensitivity level for quantitative characteristics in finite population sampling," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(3), pages 341-352, May.
    5. María del Mar Rueda & Beatriz Cobo & Antonio Arcos, 2021. "Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables," Mathematics, MDPI, vol. 9(6), pages 1-13, March.
    6. Antonio Arcos & María del Rueda & Sarjinder Singh, 2015. "A generalized approach to randomised response for quantitative variables," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1239-1256, May.
    7. Pier Francesco Perri & Beatriz Cobo Rodríguez & María del Mar Rueda García, 2018. "A mixed-mode sensitive research on cannabis use and sexual addiction: improving self-reporting by means of indirect questioning techniques," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1593-1611, July.
    8. Muhammad Azeem & Sundus Hussain & Musarrat Ijaz & Najma Salahuddin, 2024. "An improved quantitative randomized response technique for data collection in sensitive surveys," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 329-341, February.
    9. Oluseun Odumade & Sarjinder Singh, 2010. "An Alternative to the Bar-Lev, Bobovitch, and Boukai Randomized Response Model," Sociological Methods & Research, , vol. 39(2), pages 206-221, November.
    10. 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.
    11. María del Mar García Rueda & Pier Francesco Perri & Beatriz Rodríguez Cobo, 2018. "Advances in estimation by the item sum technique using auxiliary information in complex surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 455-478, July.
    12. Zawar Hussain & Mashail M Al-Sobhi & Bander Al-Zahrani, 2014. "Additive and Subtractive Scrambling in Optional Randomized Response Modeling," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    13. Christopher Gjestvang & Sarjinder Singh, 2007. "Forced quantitative randomized response model: a new device," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 66(2), pages 243-257, September.
    14. Giancarlo Diana & Saba Riaz & Javid Shabbir, 2014. "Hansen and Hurwitz estimator with scrambled response on the second call," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(3), pages 596-611, March.
    15. Giancarlo Diana & Pier Francesco Perri, 2010. "New scrambled response models for estimating the mean of a sensitive quantitative character," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1875-1890.
    16. Kumari Priyanka & Pidugu Trisandhya, 2019. "Modelling Sensitive Issues On Successive Waves," Statistics in Transition New Series, Polish Statistical Association, vol. 20(1), pages 41-65, March.
    17. Amitava Saha, 2011. "An optional scrambled randomized response technique for practical surveys," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(2), pages 139-149, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:metrik:v:60:y:2004:i:3:p:255-260. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.