Respondent Driven Sampling for Immigrant Populations: A Health Survey of Foreign-Born Korean Americans
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ORIGINAL PAPER
Respondent Driven Sampling for Immigrant Populations: A Health Survey of Foreign‑Born Korean Americans Sunghee Lee1 · Ai Rene Ong1 · Chen Chen1 · Michael Elliott1 Accepted: 5 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This study examined feasibility and methodological utilities of respondent driven sampling (RDS) for Korean immigrants. We conducted the Health and Life Study of Koreans (HLSK), a Web-based RDS study targeting foreign-born Korean Americans. Through chain referrals, n = 638 participated. Geographic coverage and estimates of HLSK were compared to foreignborn Korean samples in the American Community Survey and the California Health Interview Survey as benchmarks. Compared to the benchmarks, HLSK fared well on the geographic coverage, household type and size, employment and health insurance but over-captured those who were younger, more recent immigrants, with higher education and with disability. Existing RDS-specific estimators were largely ineffective. Conclusions. RDS may serve as a cost-effective tool for recruiting recent immigrants, a harder-to-recruit subgroup within minorities. However, recruitment noncooperation posed operational challenges, a critical gap in the literature. This leaves RDS yet to be a reliable methodology. Keywords Respondent driven sampling · Health surveys · Asian Americans · Immigrants
Background Changes in the racial/ethnic composition of the U.S. population have expanded the scope of research interests and health policy agenda to give explicit considerations to racial/ethnic minorities. While data is necessary to accommodate such a trend, most data collection efforts are at the minimum standard racial/ethnic categories (e.g., Asian). At the same time, there is growing evidence supporting data need for more granular racial/ethnic categories (e.g., Chinese) [1–3]. For instance, a large gap in breast cancer incidence between Korean and Japanese American women [4] would have been lost under the minimum category, Asian. Disaggregated data provides insights into nuances that may go unnoticed under standard categories. This type of research requires a large sample size for minority subgroups, intensifying the cost Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10903-020-01077-4) contains supplementary material, which is available to authorized users. * Sunghee Lee [email protected] 1
Institute for Social Research, University of Michigan, 426 Thompson St., Ann Arbor, MI 48104, USA
burden [5, 6]. Data collection methods overcoming such barriers are in critical need for understanding racial/ethnic minorities and eliminating health disparities [7].
Conceptual Framework Respondent driven sampling (RDS) was introduced to address a lack of feasible approaches for sampling rare and hard-to-reach groups [8]. As a variant of snowball sampling, RDS is not a probability sampling method, entirely different than traditional sampling. RDS recruitment is controlled by
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