Estimation of Seroprevalence, Rape, and Homelessness in the United States Using a Social Network Approach

Peter D. Killworth, Christopher McCarty, H. Russell Bernard, Gene Ann Shelley and Eugene C. Johnsen
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Peter D. Killworth: Southampton Oceanography Centre
Christopher McCarty: University of Florida
H. Russell Bernard: University of Florida
Gene Ann Shelley: Georgia State University
Eugene C. Johnsen: University of California, Santa Barbara

Evaluation Review, 1998, vol. 22, issue 2, 289-308

Abstract: The authors have developed and tested scale-up methods, based on a simple social network theory, to estimate the size of hard-to-count subpopulations. The authors asked a nationally representative sample of respondents how many people they knew in a list of 32 subpopulations, including 29 subpopulations of known size and 3 of unknown size. Using these responses, the authors produced an effectively unbiased maximum likelihood estimate of the number of people each respondent knows. These estimates were then used to back-estimate the size of the three populations of unknown size. Maximum likelihood values and 95% confidence intervals are found for seroprevalence, 800,000 ±43,000; for homeless, 526,000 ±35,000; and for women raped in the last 12 months, 194,000±21,000. The estimate for seroprevalence agrees strikingly with medical estimates, the homeless estimate is well within the published estimates, and the authors' estimate lies in the middle of the published range for rape victims.

Date: 1998
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Persistent link: https://EconPapers.repec.org/RePEc:sae:evarev:v:22:y:1998:i:2:p:289-308

DOI: 10.1177/0193841X9802200205

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