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5. Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling
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.
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Cites
Networks, Dynamics, and the Small‐World Phenomenon
Watts, D. J. (1999) · American Journal of Sociology · doi:10.1086/210318 · Cited by 1,510
Heckathorn, D. D., Semaan, S., Broadhead, R. S., & Hughes, J. J. (2002) · AIDS and Behavior · doi:10.1023/a:1014528612685 · Cited by 369
Self-citation
Cited by
Smith, K. P., & Christakis, N. A. (2008) · Annual Review of Sociology · doi:10.1146/annurev.soc.34.040507.134601 · Cited by 1,638
Social research 2.0: virtual snowball sampling method using Facebook
Baltar, F., & Brunet, I. (2012) · Internet Research · doi:10.1108/10662241211199960 · Cited by 1,284
Social Networks and Health: Models, Methods, and Applications
Valente, T. W. (2010) · doi:10.1093/acprof:oso/9780195301014.001.0001 · Cited by 933
Effects of missing data in social networks
Kossinets, G. (2006) · Social Networks · doi:10.1016/j.socnet.2005.07.002 · Cited by 926
A Survey of Statistical Network Models
Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2010) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000005 · Cited by 819
Metadata
- Authors
- Salganik, M. J., & Heckathorn, D. D.
- Venue
- Sociological Methodology
- Publisher
- SAGE Publications
- Published
- 2004-12
- Volume
- 34
- Issue
- 1
- Pages
- 193-240
- ISSN
- 0081-1750 (ISSN-L)
- 1467-9531
- OpenAlex
- W2117740169
- Cited by
- 1,953
- References
- 2
- FWCI
- 24.28
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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