GENERALIZING THE NETWORK SCALE-UP METHOD: A NEW ESTIMATOR FOR THE SIZE OF HIDDEN POPULATIONS
GENERALIZING THE NETWORK SCALE-UP METHOD: A NEW ESTIMATOR FOR THE SIZE OF HIDDEN POPULATIONS
复制标题
DOI：
10.1177/0081175016665425
复制
发表时间：
2016-01-01
期刊：
影响因子：
--
通讯作者：
Salganiky, Matthew J.
中科院分区：
文献类型：
--
作者：
Feehan, Dennis M.;Salganiky, Matthew J.
网络放大方法使研究人员能够使用抽样的社交网络数据来估计隐藏人口的规模，如毒品注射者和性工作者。基本的按比例放大估计器提供了优于其他大小估计技术的优势，但它依赖于有问题的建模假设。作者提出了一个新的广义尺度估计，可用于设置与非随机社会混合和不完善的认识成员在隐藏的人口。此外，新的估计可以使用时，数据收集通过复杂的样本设计和不完整的抽样框架。然而，广义尺度估计还需要来自两个样本的数据：一个来自帧总体，一个来自隐藏总体。在某些情况下，可以通过在已计划的研究中添加少量问题来收集来自隐藏人群的这些数据。对于其他情况下，作者开发了可解释的调整因子，可应用于基本的规模扩大估计。最后，作者对未来研究的设计和分析提出了实用的建议。
The network scale-up method enables researchers to estimate the sizes of hidden populations, such as drug injectors and sex workers, using sampled social network data. The basic scale-up estimator offers advantages over other size estimation techniques, but it depends on problematic modeling assumptions. The authors propose a new generalized scale-up estimator that can be used in settings with nonrandom social mixing and imperfect awareness about membership in the hidden population. In addition, the new estimator can be used when data are collected via complex sample designs and from incomplete sampling frames. However, the generalized scale-up estimator also requires data from two samples: one from the frame population and one from the hidden population. In some situations these data from the hidden population can be collected by adding a small number of questions to already planned studies. For other situations, the authors develop interpretable adjustment factors that can be applied to the basic scale-up estimator. The authors conclude with practical recommendations for the design and analysis of future studies.