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Respondent-Driven Sampling

Snowball sampling is popular and fun to do, but it does not produce a statistically representative sample in a large population. In a small community, like practitioners of alternative medicine in a town, the members are likely to be in contact with one another.

Here, snowball sampling is an effective way to build an exhaustive sampling frame from which you can select people at random to interview—or elect to interview all of them. In large communities, though—like all the native curers in a city such as Nairobi—the members who are well known have a better chance of being named in a snowball procedure than members who are less well known. And in large populations, people who have large networks name more people than do people who have small networks. For large populations, then, snowball sampling can produce useful nonprobability samples, but every person does not have the same chance of being included.

Douglas Heckathorn (1997) developed respondent-driven sampling to deal with this problem. Like snowball sampling, RDS begins with a few informants who act as seeds. The informants are paid for being interviewed and are then asked to recruit up to three members of their networks into the study. To move this process along, Heckathorn paid each of his seed informants $10 (this might be $50 today, but you get the idea) and he gave them three coupons. Anyone who came to Heckathorn to be interviewed and who had one of those coupons was paid the same $10. (He upped the bounty to $15 for referring a female drug injector because they were harder to find.) Those informants, in turn, got several coupons and recruited others into the study.

There are two improvements to snowball sampling here. (1) The people whom an informant names in a snowball interview may not want you even to know about their existence, much less be anxious to grant you an interview. In respondent-driven sampling, the initial members of the sample are volunteers as are the people they recruit. (2) When it’s done right, the RDS method produces samples that are less biased than are traditional snowball samples (Salganik and Heckathorn 2004) (Further Reading: chain referral, snowball, and RDS sampling) (box 7.3).

BOX 7.3


RDS is now widely used in the study of injection drug users and other populations at high risk for HIV/AIDS. Scott (2008) interviewed 70 injection drug users in Chicago who had participated in the full entire RDS sequence: They had received $20 for completing an interview and had collected $30 more for bringing in three associates (at $10 each) who had also completed an interview. Forty-eight of the 70 had sold at least one of their coupons to someone else because they could not trust three people in their network to complete the interview. This may introduce some distortion to the samples, but RDS remains the best available method for locating and studying populations at risk for HIV/ AIDS.

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