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Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling

, and . Sociological Methodology, 34 (1): 193--239 (2004)
DOI: 10.2307/3649374

Abstract

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.

Description

Proposes a sampling and estimation technique (respondent-driven sampling) which makes asymptoticly unbiased estimates about hidden populations like injection drug users, the homeless, and artists.

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