IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/4559663.html
   My bibliography  Save this article

A Two-Stage Unrelated Question Randomized Response Model for Estimating the Prevalence of Stigmatized Attribute

Author

Listed:
  • Ghulam Narjis
  • Javid Shabbir
  • Ronald Onyango
  • Dost Muhammad Khan

Abstract

The randomized response technique (RRT) is an effective method designed to obtain the stigmatized information from respondents while assuring the privacy. In this study, we propose a new two-stage unrelated question RRT model to estimate the prevalence of sensitive attribute π when the proportion of unrelated innocuous attribute is known and unknown. A simulation study is carried out to validate the theoretical results of proposed estimators. The utility of proposed two-stage unrelated question RRT model under stratification is also explored. An efficiency comparison between the proposed two-stage unrelated question RRT model and the Singh and Suman RRT model is carried out numerically under simple and stratified random sampling.

Suggested Citation

  • Ghulam Narjis & Javid Shabbir & Ronald Onyango & Dost Muhammad Khan, 2022. "A Two-Stage Unrelated Question Randomized Response Model for Estimating the Prevalence of Stigmatized Attribute," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-18, May.
    • Handle: RePEc:hin:jnlmpe:4559663
    DOI: 10.1155/2022/4559663
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/4559663.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/mpe/2022/4559663.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/4559663?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
    ---><---

    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:hin:jnlmpe:4559663. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.