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Respondent Driven Sampling (RDS) is a novel variant of link tracing sampling that has primarily been used to estimate the characteristics of hard-to-reach groups, such as the HIV prevalence of drug users.1 ‘Seeds’ are selected by convenience from a population of interest (target population) and given coupons. Seeds then use these coupons to recruit other people, who themselves become recruiters. Recruits are given compensation, usually money, for taking part in the survey and also an incentive for recruiting others. This process continues in recruitment ‘waves’ until the survey is stopped. Estimation methods are then applied to account for the biased recruitment, for example, the presumed over-recruitment of people with more acquaintances, in an attempt to generate estimates for the underlying population. RDS has quickly become popular and relied on by major public health organisations, including the US Centers for Disease Control and Prevention and Family Health International, chiefly because it is often found to be an efficient method of recruitment in hard-to-reach groups, but also because of the availability of custom written software incorporating inference methods that are designed to generate estimates that are representative of the wider population of interest, despite the biased sampling.
As demonstrated by RDS's popularity,1 there was a clear need for new methods of data collection on hard-to-reach groups. However, RDS has not been without its critics. Its reliance on the target population for recruitment introduced ethicalw1 and sampling concerns.w2 If RDS estimates are overly biased or the variance is unacceptably high, then RDS will be little more than another method of convenience sampling. If these errors can be minimised however, then RDS has the potential to become a very useful survey methodology.
In this editorial we highlight that ‘RDS’ includes both data collection and statistical inference methods, discuss the limitations …
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