The Network Scale-up Method (NSUM) uses social networks and answers to "How many X's do you know?" questions to estimate hard-to-reach population sizes. This paper focuses on two biases associated with the NSUM. First, different populations are known to have different average social network sizes, introducing degree ratio bias. This is especially true for marginalized populations like sex workers and drug users, where members tend to have smaller social networks than the average person. Second, large subpopulations are weighted more heavily than small subpopulations in current NSUM estimators, leading to poor size estimates of small subpopulations. We show how the degree ratio affects size estimates, provide a method to estimate degree ratios without collecting additional data, and demonstrate that rescaling size estimates improves the estimates for smaller subpopulations. We demonstrate that our adjustment procedures improve the accuracy of NSUM size estimates using simulations and data from two data sources.

翻译：网络推估法（NSUM）利用社交网络及"你认识多少X？"类问题的答案，估计难以接触人群的规模。本文聚焦于NSUM相关的两类偏差。首先，不同人群的社交网络平均规模存在差异，这引入了度数比偏差。对于性工作者、吸毒者等边缘化群体尤为显著，其成员社交网络规模通常小于普通人群。其次，在现有NSUM估计量中，大型子群体被赋予更高权重，导致小型子群体的规模估计效果欠佳。我们展示了度数比如何影响规模估计，提出无需额外数据即可估算度数比的方法，并证明重新调整规模估计值可改善对小型子群体的估计。通过模拟实验及两组真实数据源验证，我们的修正流程能有效提升NSUM规模估计的准确性。