Recruiting data-obscured populations: strategic local government referral sampling for qualitative studies

Shinto Joseph (), Jasmine Mathew () and Joseph Kuncheria ()
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Shinto Joseph: Rajagiri College of Social Sciences (Autonomous)
Jasmine Mathew: Rajagiri College of Social Sciences (Autonomous)
Joseph Kuncheria: Rajagiri College of Social Sciences (Autonomous)

Palgrave Communications, 2025, vol. 12, issue 1, 1-12

Abstract: 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.

Date: 2025
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DOI: 10.1057/s41599-025-04540-5

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