Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling

Matthew J. Salganik, Douglas D. Heckathorn

Research output: Contribution to journalArticlepeer-review

1353 Scopus citations


Standard statistical methods often provide no way to make accurate estimates about the characteristics of hidden populations such as injection drug users, the homeless, and artists. In this paper, we further develop a sampling and estimation technique called respondent-driven sampling, which allows researchers to make asymptotically unbiased estimates about these hidden populations. The sample is selected with a snowball-type design that can be done more cheaply, quickly, and easily than other methods currently in use. Further, we can show that under certain specified (and quite general) conditions, our estimates for the percentage of the population with a specific trait are asymptotically unbiased. We further show that these estimates are asymptotically unbiased no matter how the seeds are selected. We conclude with a comparison of respondent-driven samples of jazz musicians in New York and San Francisco, with corresponding institutional samples of jazz musicians from these cities. The results show that some standard methods for studying hidden populations can produce misleading results.

Original languageEnglish (US)
Pages (from-to)193-240
Number of pages48
JournalSociological Methodology
Issue number1
StatePublished - Dec 2004

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science


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