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Challenges of virtual RDS for recruitment of sexual minority women for a behavioral health study
Middleton, D. F., Drabble, L. A., Krug, D., Karriker-Jaffe, K., Mericle, A. A., Hughes, T. L., Iachan, R., & Trocki, K. F. (2022). Challenges of virtual RDS for recruitment of sexual minority women for a behavioral health study. Journal of Survey Statistics and Methodology, 10(2), 466-488. https://doi.org/10.1093/jssam/smab039
Respondent-driven sampling (RDS) is an approach commonly used to recruit nonprobability samples of rare and hard-to-find populations. The purpose of this study was to explore the utility of phone- and web-based RDS methodology to sample sexual minority women (SMW) for participation in a telephone survey. Key features included (i) utilizing a national probability survey sample to select seeds; (ii) web-based recruitment with emailed coupons; and (iii) virtual processes for orienting, screening, and scheduling potential participants for computer-assisted telephone interviews. Rather than resulting in a large diverse sample of SMW, only a small group of randomly selected women completed the survey and agreed to recruit their peers, and very few women recruited even one participant. Only seeds from the most recent of two waves of the probability study generated new SMW recruits. Three RDS attempts to recruit SMW over several years and findings from brief qualitative interviews revealed four key challenges to successful phone- and web-based RDS with this population. First, population-based sampling precludes sampling based on participant characteristics that are often used in RDS. Second, methods that distance prospective participants from the research team may impede development of relationships, investment in the study, and motivation to participate. Third, recruitment for telephone surveys may be impeded by multiple burdens on seeds and recruits (e.g., survey length, understanding the study and RDS process). Finally, many seeds from a population-based sample may be needed, which is not generally feasible when working with a limited pool of potential seeds. This method may yield short recruitment chains, which would not meet key RDS assumptions for approximation of a probability sample. In conclusion, potential challenges to using RDS in studies with SMW, particularly those using virtual approaches, should be considered.