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Collecting data from migrants in Ghana: Lessons learned using respondent-driven sampling

Author

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  • Samantha R. Lattof

    (London School of Economics and Political Science)

Abstract

Background: Policymakers and program implementers require high-quality data on migrants and migration in low- and middle-income countries (LMIC); however, a shortage of high-quality data exists in these settings. Sampling migrant populations requires better techniques. Respondent-driven sampling (RDS) may be one such solution. Objective: Using Ghana as a case study, the objectives of this paper are to: 1) assess RDS recruitment productivity, network size, and ties of internal migrants; 2) test for homophily; and 3) detail the successes of and challenges to implementing RDS in Ghana and how these lessons can be applied to migrant populations in other LMIC settings. Methods: This study used RDS to sample 625 rural–urban female migrants working as market porters (kayayei) in Accra, Ghana. Results: This study generated the most comprehensive data set on kayayei to date. Network size increases as participants become more educated and migrate more often to Accra. Ethnic group membership is an important determinant of recruitment, with certain groups preferring to recruit from within. Employing members of the kayayei population to collect data built crucial trust. Conclusions: Whilst RDS is not a one-size-fits-all solution for sampling hard-to-reach migrants in LMIC, it can be a powerful tool to uncover and to recruit hard-to-reach migrant populations. In countries with multiple ethnolinguistic groups, recruiting a migrant population with greater ethnolinguistic overlap may facilitate quicker equilibrium. Contribution: This study expands the evidence base on use of RDS among migrant populations in LMIC and provides lessons learned to assist other researchers implementing RDS in LMIC settings.

Suggested Citation

  • Samantha R. Lattof, 2018. "Collecting data from migrants in Ghana: Lessons learned using respondent-driven sampling," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(36), pages 1017-1058.
    • Handle: RePEc:dem:demres:v:38:y:2018:i:36
    DOI: 10.4054/DemRes.2018.38.36
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    References listed on IDEAS

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    1. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    2. Victor Agadjanian & Natalya Zotova, 2012. "Sampling and Surveying Hard-to-Reach Populations for Demographic Research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(5), pages 131-150.
    3. Lucinda Platt & Tom Frere-Smith & Renee Luthra, 2015. "Adapting chain referral methods to sample new migrants," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 33(24), pages 665-700.
    4. Alex Carballo-Diéguez & Ivan Balan & Rubén Marone & María A Pando & Curtis Dolezal & Victoria Barreda & Cheng-Shiun Leu & María Mercedes Ávila, 2011. "Use of Respondent Driven Sampling (RDS) Generates a Very Diverse Sample of Men Who Have Sex with Men (MSM) in Buenos Aires, Argentina," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-8, November.
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    Cited by:

    1. Lattof, Samantha R. & Coast, Ernestina & Leone, Tiziana, 2018. "Priorities and challenges accessing health care among female migrants," LSE Research Online Documents on Economics 90288, London School of Economics and Political Science, LSE Library.
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    3. Hans Walter Steinhauer & Jean Philippe Décieux & Manuel Siegert & Andreas Ette & Sabine Zinn, 2024. "Establishing a probability sample in a crisis context: the example of Ukrainian refugees in Germany in 2022," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 18(1), pages 77-97, March.
    4. Héctor Mullo & Ismael Sánchez-Borrego & Sara Pasadas-del-Amo, 2020. "Respondent-Driven Sampling for Surveying Ethnic Minorities in Ecuador," Sustainability, MDPI, vol. 12(21), pages 1-17, November.
    5. Carmen Koschollek & Katja Kajikhina & Susanne Bartig & Marie-Luise Zeisler & Patrick Schmich & Antje Gößwald & Alexander Rommel & Thomas Ziese & Claudia Hövener, 2022. "Results and Strategies for a Diversity-Oriented Public Health Monitoring in Germany," IJERPH, MDPI, vol. 19(2), pages 1-18, January.

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    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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