Author
Listed:
- Shinto Joseph
(Rajagiri College of Social Sciences (Autonomous))
- Jasmine Mathew
(Rajagiri College of Social Sciences (Autonomous))
- Joseph Kuncheria
(Rajagiri College of Social Sciences (Autonomous))
Abstract
The research community must develop innovative methodologies that encompass all segments of society in social science research. Undeniably, there are neglected or unidentified sections of society, and including them in research poses challenges. The Gulf Breadwinner Bereavement (GBB) research conducted among the Kerala women who lost their husbands in the Gulf countries to COVID-19 introduces a new category called Data-Obscured Populations (DOP). It outlines the Local Government Referral Sampling (LGRS) strategy for identifying them. LGRS leverages the established networks and the trust of local government authorities to identify and recruit participants. The GBB study demonstrates the effectiveness of the LGRS in engaging DOP across Kerala. Despite challenges, the method proved to be robust and adaptable, providing valuable insights and data saturation. The study underscores the potential of LGRS to revolutionise qualitative research methodologies, offering a scalable and culturally sensitive approach to participant recruitment. This paper expands the methodological toolkit of social science research by establishing a strong foundation for future applications of LGRS in various research contexts, emphasising its ability to support ethical and inclusive research practices and reliable findings.
Suggested Citation
Shinto Joseph & Jasmine Mathew & Joseph Kuncheria, 2025.
"Recruiting data-obscured populations: strategic local government referral sampling for qualitative studies,"
Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-12, December.
Handle:
RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04540-5
DOI: 10.1057/s41599-025-04540-5
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
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:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04540-5. 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: https://www.nature.com/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.