From theoretical to real-world networks: Using Empirical Samples of Female Sex Workers in China to Evaluate Respondent Driven Sampling

RDS is an increasingly popular chain-referral sampling method used to recruit samples of hidden and hard-to-reach populations. It seeks to provide a probability-based inferential structure for representations of population groups with status characteristics that are not likely to be revealed by omnibus survey research because they are rare and socially stigmatized and/or illegal. In RDS the target for representation is the hidden population of a well-defined geographic area (e.g. a city). RDS starts by recruiting survey respondents through a chain-referral approach that is initiated by the selection of a limited number of “seed” respondents known to the researchers administering the study and belonging to the population of interest. Seeds are interviewed and given a limited number of coupons which they are asked to distribute to their immediate social contacts in the target population as a means of recruiting other participants from among their social networks. Members of the seeds’ social circles who receive coupons and then choose to participate in the study form the first “wave” of the sample. This process proceeds recursively (hence the term “chain-referral”) through multiple waves until a desired sample size is reached.