Killworth P D, McCarty C, Bernard H R, Shelley G A, Johnsen E C
Southampton Oceanography Centre, UK.
Eval Rev. 1998 Apr;22(2):289-308. doi: 10.1177/0193841X9802200205.
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.
作者基于简单的社会网络理论开发并测试了放大方法，以估计难以计数的亚人群规模。作者询问了具有全国代表性的受访者样本，让他们说出在包含32个亚人群的列表中认识多少人，其中包括29个已知规模的亚人群和3个未知规模的亚人群。利用这些回答，作者得出了每个受访者认识人数的有效无偏最大似然估计值。然后，这些估计值被用于反向估计三个未知规模人群的规模。得出了血清阳性率的最大似然值和95%置信区间，分别为800,000 +/- 43,000；无家可归者人数为526,000 +/- 35,000；过去12个月内遭受强奸的女性人数为194,000 +/- 21,000。血清阳性率的估计值与医学估计值惊人地一致，无家可归者人数的估计值在已公布的估计范围内，作者对强奸受害者人数的估计值处于已公布范围的中间。
