![]() ![]() Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling. Sociol Methodol. ĭiscussion of assumptions and potential biases of RDS, with recommendations for future research. The Importance of Measuring and Accounting for Potential Biases in Respondent-Driven Samples. AIDS Behav. 2013 17:2244-2252. Įmpirical comparison of RDS estimates to total population data. Evaluation of respondent-driven sampling.Epidemiology. 2012 23:138-147. doi. McCreesh, N, Frost, SDW, Seeley, J, et al. RDS simulation, with special attention to variance. Assessing respondent-driven sampling. PNAS. Respondent-Driven Sampling: A New Approach to the Study of Hidden Populations. Soc Probl. Johnston, LG. Introduction to HIV/AIDS and Sexually Transmitted Infection Surveillance, Module 4: Introduction to Respondent Driven Sampling. A great place to start if you’re new to RDS. (Eds.), The SAGE Handbook of Innovation in Social Research Methods. p. Chapter 22: Respondent-Driven Sampling: Operational Procedures, Evolution of Estimators, and Topics for Future Research. Improving the way that variance (and sample size) are calculated is an area of active research in RDS. The RDS I estimator has a tendency to underestimate variance, and the RDS II estimator has a tendency to overestimate variance. The main difference between the two different methods relates to variance, with RDS I using a bootstrap technique and RDS II using an analytical technique. RDS I and RDS II estimators tend to yield similar results when data-smoothing is used. Syx = proportion of group Y selected by group Xĭx = average network size (degree) for group Xĭy = average network size (degree) for group Y Sxy = proportion of group X selected by group Y RDS inference or analysis focuses on two main sources of bias:ĭifferential social network sizes: People with small social networks are weighted more heavily than people with large social networks to compensate for the fact that people with small networks are likely underrepresented.ĭifferential recruitment: People whose probability of recruitment is artificially increased due to homophily (e.g., same race as the recruiter) are weighted less than people who may be left out of the sample simply because they have certain characteristics that are different than the recruiters.įinally, calculations are derived using a form of RDS Estimator: Once the sample has been recruited, statistical techniques have been developed to try and reduce biases in the data. Furthermore, the population has to be large enough to sustain long referral chains without repeated participants. RDS only works in populations that are connected to one another. Incentives: Participants receive two incentives: one for completing the interview, and one for each peer that is successfully recruited. This referral chain continues until the desired sample size is reached. The recruits of Wave 1 then complete the interview process and recruit Wave 2. Interviews and recruitment: Seeds complete the interview process and receive a predetermined number of coupons that they can use to recruit other people like them (Wave 1). Seeds should be diverse and well-networked, but they do not need to be chosen randomly. Seed selection: All RDS studies begin with a small number of seeds from the target population (e.g., 3-15 people). ![]() RDS sampling consists of the following three steps: The extent to which RDS-derived estimates are valid and generalizable remains a source of controversy in the peer-reviewed literature. RDS relies on multiple waves of peer-to-peer recruitment and statistical adjustments to try and approximate random sampling. Douglas Heckathorn, a professor of Sociology at Cornell and has been applied to groups ranging from men who have sex with men, injection drug users, children living on the street and jazz musicians. RDS is a type of snowball sampling used for analyzing characteristics of hidden or hard-to-reach populations. ![]()
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