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：网络规模估计方法（NSUM）利用社交网络以及“你认识多少X类人？”这类问题的回答，来估计难以接触的人群规模。本文聚焦于与NSUM相关的两种偏差。首先，不同人群的平均社交网络规模存在差异，这引入了度数比例偏差。这对于性工作者和吸毒者等边缘化群体尤为明显，其成员的社交网络规模往往小于普通人。其次，在当前的NSUM估计量中，大规模子群体比小规模子群体被赋予更高权重，导致对小规模子群体的规模估计偏差较大。我们展示了度数比例如何影响规模估计，提出了一种无需额外数据即可估计度数比例的方法，并证明了重新调整规模估计值可改善对小规模子群体的估计精度。通过模拟实验及来自两项数据源的数据，我们验证了调整流程能提升NSUM规模估计的准确性。