Respondent‐driven sampling: an assessment of current methodology
Krista J. Gile,Mark S. Handcock +1 more
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In this article, the authors 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.Abstract:
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 current estimators of population averages make strong assumptions in order to treat the data as a probability sample. 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.Our analysis indicates: (1) that the convenience sample of seeds can induce bias, and the number of sample waves typically used in RDS is likely insufficient for the type of nodal mixing required to obtain the reputed asymptotic unbiasedness; (2) that preferential referral behavior by respondents leads to bias; (3) that when a substantial fraction of the target population is sampled the current estimators can have substantial bias.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. We recommend ways to improve the methodology.read more
Citations
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Journal ArticleDOI
Statistical Models for Social Networks
TL;DR: This review first treats models for single (cross-sectionally observed) networks and then for network dynamics, and actor-oriented models here provide a model that can represent many dependencies in a flexible way.
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Comment: snowball versus respondent-driven sampling
TL;DR: This comment summarizes the development of the RDS method, distinguishing among seven forms of the estimator, and offers a clarification of a related set of issues.
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Summary Report of the AAPOR Task Force on Non-probability Sampling
Reg Baker,J. Michael Brick,Nancy Bates,Mike Battaglia,Mick P. Couper,Jill A. Dever,Krista J. Gile,Roger Tourangeau +7 more
TL;DR: A wide range of non-probability designs exist and are being used in various settings, including case control studies, clinical trials, evaluation research, and more.
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Assessing respondent-driven sampling
Sharad Goel,Matthew J. Salganik +1 more
TL;DR: Investigating the performance of RDS by simulating sampling from 85 known, network populations finds that RDS is substantially less accurate than generally acknowledged and that reported RDS confidence intervals are misleadingly narrow.
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Comment: on the concept of snowball sampling
Mark S. Handcock,Krista J. Gile +1 more
TL;DR: The concept of snowball sampling has been in informal use for a long time, but it certainly predates Coleman (1958) and Trow (1957) as mentioned in this paper, and the earliest systematic work dates to the 1940s from the Columbia Bureau of Applied Social Research, led by Paul Lazarsfeld.
References
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A generalization of sampling without replacement from a finite universe.
D. G. Horvitz,D. J. Thompson +1 more
TL;DR: In this paper, two sampling schemes are discussed in connection with the problem of determining optimum selection probabilities according to the information available in a supplementary variable, which is a general technique for the treatment of samples drawn without replacement from finite universes when unequal selection probabilities are used.
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Respondent-driven sampling : A new approach to the study of hidden populations
TL;DR: A new variant of chain-referral sampling, respondent-driven sampling, is introduced that employs a dual system of structured incentives to overcome some of the deficiencies of such samples and discusses how respondent- driven sampling can improve both network sampling and ethnographic investigation.
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Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling
TL;DR: This paper develops a sampling and estimation technique called respondent-driven sampling, which allows researchers to make asymptotically unbiased estimates about the characteristics of hidden populations such as injection drug users, the homeless, and artists.
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Respondent-Driven Sampling II: Deriving Valid Population Estimates from Chain-Referral Samples of Hidden Populations
TL;DR: Inertial energy storage apparatus having two contrarotating rotors the fellies of which include a number of thin rings of glass or embedded fiber composite material supported by elastic support means so that the radial separations between adjacent rings produced by centrifugal force do not cause failure of the rotors by mechanical rupture of the ring support means.
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Targeted sampling: options for the study of hidden populations
TL;DR: Targeted sampling provides a cohesive set of research methods that can help researchers study health or social problems that exist among populations that are difficult to reach because of their attributed social stigma, legal status, and consequent lack of visibility.