The efficacy of respondent-driven sampling for the health assessment of minority populations. (October 2017)

• Record Type: Journal Article
• Authors: Badowski, Grazyna; Somera, Lilnabeth P.; Simsiman, Brayan; Lee, Hye-Ryeon; Cassel, Kevin; Yamanaka, Alisha; Ren, JunHao
• Is Part Of: Cancer epidemiology. Volume 50: Part B(2017:Oct.)
• Page Start: 214
• Page End: 220
• Publication Date: 2017-10
• Subjects: Respondent-driven sampling -- Health communication -- Guam -- Chamorro -- Federated States of Micronesia -- Micronesians -- Pacific Islanders; Cancer -- Epidemiology -- Periodicals; Cancer -- Prevention -- Periodicals; Cancer -- Diagnosis -- Periodicals; Carcinogenesis -- Periodicals; 616.994005
• DOI: 10.1016/j.canep.2017.07.006
• Languages: English
• ISSNs: 1877-7821

Abstract

Abstract: Background: Respondent driven sampling (RDS) is a relatively new network sampling technique typically employed for hard-to-reach populations. Like snowball sampling, initial respondents or "seeds" recruit additional respondents from their network of friends. Under certain assumptions, the method promises to produce a sample independent from the biases that may have been introduced by the non-random choice of "seeds." We conducted a survey on health communication in Guam's general population using the RDS method, the first survey that has utilized this methodology in Guam. It was conducted in hopes of identifying a cost-efficient non-probability sampling strategy that could generate reasonable population estimates for both minority and general populations. Methods: RDS data was collected in Guam in 2013 (n = 511) and population estimates were compared with 2012 BRFSS data (n = 2031) and the 2010 census data. The estimates were calculated using the unweighted RDS sample and the weighted sample using RDS inference methods and compared with known population characteristics. Results: The sample size was reached in 23 days, providing evidence that the RDS method is a viable, cost-effective data collection method, which can provide reasonable population estimates. However, the results also suggest that the RDS inference methods used to reduce bias, based on self-reported estimates of network sizes, may not always work. Caution is needed when interpreting RDS study findings. Conclusions: For a more diverse sample, data collection should not be conducted in just one location. Fewer questions about network estimates should be asked, and more careful consideration should be given to the kind of incentives offered to participants.