Abstract

Estimating the size of hard-to-reach populations is an important problem for many fields. The network scale-up method (NSUM) is a relatively new approach to estimate the size of these hard-to-reach populations by asking respondents the question, “How many X’s do you know,” where X is the population of interest (e.g., “How many female sex workers do you know?”). The answers to these questions form aggregated relational data (ARD). The NSUM has been used to estimate the size of a variety of subpopulations, including female sex workers, drug users, and even children who have been hospitalized for choking. Within the network scale-up methodology, there are a multitude of estimators for the size of the hidden population, including direct estimators, maximum likelihood estimators, and Bayesian estimators. In this article, we first provide an in-depth analysis of ARD properties and the techniques to collect the data. Then, we comprehensively review different estimation methods in terms of the assumptions behind each model, the relationships between the estimators, and the practical considerations of implementing the methods. We apply many of the models discussed in the review to one canonical dataset and compare their performance and unique features, presented in the supplementary materials. Finally, we provide a summary of the dominant methods and an extensive list of the applications, and discuss the open problems and potential research directions in this area.

摘要

估计难以到达的人群的规模是许多领域的一个重要问题。网络扩大方法 (NSUM) 是一种相对较新的方法，通过向受访者询问“您知道多少个 X”的问题来估计这些难以到达的人群的规模，其中 X 是感兴趣的人群（例如，“你认识多少女性性工作者？”）。这些问题的答案形成聚合关系数据（ARD）。NSUM 已被用来估计各种亚人群的规模，包括女性性工作者、吸毒者，甚至因窒息而住院的儿童。在网络扩展方法中，有多种用于隐藏群体大小的估计器，包括直接估计器、最大似然估计器和贝叶斯估计器。在本文中，我们首先深入分析 ARD 属性和收集数据的技术。然后，我们根据每个模型背后的假设、估计量之间的关系以及实施这些方法的实际考虑，全面回顾不同的估计方法。我们将评论中讨论的许多模型应用于一个规范数据集，并比较补充材料中介绍的它们的性能和独特功能。最后，我们总结了主要方法和广泛的应用列表，并讨论了该领域的开放问题和潜在的研究方向。