Respondent-Driven Sampling (RDS) employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure to expand the sample and reduce its dependence on the initial (convenience) sample. The primary goal of RDS is typically to estimate population averages in the hard-to-reach population. The current estimates make strong assumptions in order to treat the data as a probability sample. In particular, we evaluate three critical sensitivities of the estimators: to bias induced by the initial sample, to uncontrollable features of respondent behavior, and to the without-replacement structure of sampling. This paper sounds a cautionary note for the users of RDS. While current RDS methodology is powerful and clever, the favorable statistical properties claimed for the current estimates are shown to be heavily dependent on often unrealistic assumptions.
Deep Dive into Respondent-Driven Sampling: An Assessment of Current Methodology.
Respondent-Driven Sampling (RDS) employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure to expand the sample and reduce its dependence on the initial (convenience) sample. The primary goal of RDS is typically to estimate population averages in the hard-to-reach population. The current estimates make strong assumptions in order to treat the data as a probability sample. In particular, we evaluate three critical sensitivities of the estimators: to bias induced by the initial sample, to uncontrollable features of respondent behavior, and to the without-replacement structure of sampling. This paper sounds a cautionary note for the users of RDS. While current RDS methodology is powerful and clever, the favorable statistical properties claimed for the current estimates are shown to be heavily dependent on often unrealistic assumptions.
Respondent-Driven Sampling (RDS, introduced by Heckathorn 1997Heckathorn , 2002, see also Salganik andHeckathorn, 2004, Volz andHeckathorn 2008) is an approach to sampling design and inference in hard-to-reach populations. Hard-to-reach populations are characterized by the difficulty in sampling from them using standard probability methods. RDS is typically employed when a sampling frame for the target population is not available, and its members are rare or stigmatized in the larger population so that it is prohibitively expensive to contact them through available frames. It is often used in populations such as injection drug users, men who have sex with men, and sex workers (Malekinejad et al., 2008), although it has also been used in other populations such as jazz musicians (Heckathorn and Jeffri, 2001), unregulated workers (Bernhardt et al., 2006), and native American subgroups (Walters and Simoni, 2002).
RDS presents two main innovations for this setting: a design for sampling from the target population and a corresponding strategy for estimating properties of the target population based on the resulting sample. It is from the former that the method draws its name: the Respondent-Driven sampling design relies on the respondents at each wave to select or \drive" the next wave of sampling through their selection of other members of the target population. This is typically achieved through the distribution of coupons by respondents to other members of the target population. Thus, RDS exploits the network of social relations connecting the target population to facilitate sampling. This strategy also reduces the confidentiality concerns generally associated with sampling from stigmatized populations.
The second main innovation is in estimating population characteristics based on the sample. As with most studies of hidden populations, a RDS sample begins with a convenience sample of individuals. The key innovation is that through many waves of sampling, the dependence of the final sample on the initial convenience sample is reduced. The estimates of inclusion probabilities in current RDS inference rely on arguments based on a Markov Chain representation of the sampling process. This innovation was proposed by Salganik and Heckathorn (2004) and extended by Volz and Heckathorn (2008).
RDS employs a link-tracing sampling design. In such designs, network links from sampled members of the target population are followed (traced) to select subsequent population members to add to the sample. In the case of RDS, the network links of interest are the social contacts facilitating the transfer of RDS coupons. Two population members related by such a link are said to be alters of one another. In the context of hard-to-reach populations such strategies are often referred to as snowball samples (Goodman, 1961). Snowball sampling is useful in settings where a network of social relations links the members of the target population, such that previously sampled individuals can facilitate the sampling of others in the population. Such samples are often very effective at recruiting large samples from hard-to-reach populations. Despite Goodman’s probabilistic formulation, however, the initial sample is typically a convenience sample, such that the ultimate snowball sample is not a probability sample (i.e. the probabilities of samples are not computable). Therefore, in most snowball samples from hard-to-reach populations, valid statistical inference is not forthcoming.
In RDS, the initial sample (also know as the seeds or 0 th wave) is assumed to be a convenience sample, selected from among the members of the target population known to the researchers. Each respondent is then given a fixed small number of coupons to distribute among their alters in the target population. Each successive wave of the sample consists of population members who are given coupons by members of the previous wave and return those coupons to the survey center. A respondent typically receives additional compensation for each successful recruitment. Respondents are also asked to report their numbers of contacts within the target population, to be used as an estimate of their nodal degree or number of alters. The passing of coupons reduces confidentiality concerns in marginalized populations, and the dual incentive structure encourages the buy-in of participant-recruiters. The limited number of coupons and measurement of degree facilitate the estimation approach described in Section 2.
Absent respondent-driven sampling, frameworks for gathering probability samples of hard-toreach populations are few and unappealing. A time-location sample (Muhib et al., 2001;Peterson et al., 2008) will generate a probability sample, but with probabilities conditional on times and locations, rather than population members. A probability sample from a larger frame such as a door-todoor survey may generate a probability sample, but the rarity of the target population may ma
…(Full text truncated)…
This content is AI-processed based on ArXiv data.
