Grazyna Badowski1, Lilnabeth P Somera2, Brayan Simsiman3, Hye-Ryeon Lee4, Kevin Cassel5, Alisha Yamanaka3, JunHao Ren3. 1. University of Guam, College of Natural & Applied Sciences, 303 University Drive, Mangilao, Guam 96923, United States. 2. University of Guam, Division of Communication and Fine Arts, 303 University Drive, Mangilao, Guam 96923, United States. Electronic address: lpsomera@triton.uog.edu. 3. University of Guam, Cancer Research Center, 303 University Drive, Mangilao, Guam 96923, United States. 4. University of Hawaii at Manoa, Department of Communicology, 2560 Campus Road, George 329, Honolulu, HI 96822, United States. 5. University of Hawaii Cancer Center, 701 Ilalo St., Honolulu, HI 96813, United States.
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 23days, 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.
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 23days, 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.
Authors: Nicky McCreesh; Simon D W Frost; Janet Seeley; Joseph Katongole; Matilda N Tarsh; Richard Ndunguse; Fatima Jichi; Natasha L Lunel; Dermot Maher; Lisa G Johnston; Pam Sonnenberg; Andrew J Copas; Richard J Hayes; Richard G White Journal: Epidemiology Date: 2012-01 Impact factor: 4.822
Authors: Mary Thiongo; Peter Gichangi; Patrick K Macho; Meagan E Byrne; Peter Kimani; Michael Waithaka; Scott Radloff; Philip Anglewicz; Michele R Decker Journal: BMC Res Notes Date: 2022-06-07