5. Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling

TLDR

Standard statistical methods often cannot accurately estimate characteristics of hidden populations such as injection drug users, the homeless, and artists. This paper further develops respondent‑driven sampling to enable asymptotically unbiased estimates of hidden populations. Respondent‑driven sampling uses a snowball‑type design that is cheaper, quicker, and easier than other methods, and the authors compare its samples of jazz musicians in New York and San Francisco with institutional samples. The authors show that respondent‑driven sampling yields asymptotically unbiased estimates of trait prevalence under general conditions, regardless of seed selection, and that standard methods can produce misleading results.

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.