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本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

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分类依据与纠错

AI 根据所提供的论文材料归类:摘要明确利用社会关系网络调查及结交倾向异质性估计人口中的网络结构。

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正在核对关联版本…

代码、数据与补充材料

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关注这篇论文的作者
Tian Zheng
Matthew J Salganik
Andrew Gelman

你认识多少在监狱中的人?

研究问题
随机调查中受访者认识各类人群的人数,除估计难以统计的人口规模外,能否揭示熟人数量与群体结交倾向的异质性?
方法与思路
作者对已有美国随机样本的网络调查数据使用多层过度离散泊松回归,同时利用回答均值与变异推断社会结构特征。
材料陈述的贡献
摘要称,美国人的熟人数量差异很大,对囚犯、无家可归者等群体的结交倾向变异明显,而对某些其他群体变异较小。
阅读关注点
应核对熟人及群体定义、过度离散的解释、受访者知晓与报告偏差,并检查从计数变异推断网络结构的条件。
阅读边界
仅据未注明版本的 OpenAlex 摘要;网络结构推断的识别条件与不确定性未说明,也不能据群体结交差异推断关系形成的因果机制。

原文摘要

Networks—sets of objects connected by relationships—are important in a number of fields. The study of networks has long been central to sociology, where researchers have attempted to understand the causes and consequences of the structure of relationships in large groups of people. Using insight from previous network research, Killworth et al. and McCarty et al. have developed and evaluated a method for estimating the sizes of hard-to-count populations using network data collected from a simple random sample of Americans. In this article we show how, using a multilevel overdispersed Poisson regression model, these data also can be used to estimate aspects of social structure in the population. Our work goes beyond most previous research on networks by using variation, as well as average responses, as a source of information. We apply our method to the data of McCarty et al. and find that Americans vary greatly in their number of acquaintances. Further, Americans show great variation in propensity to form ties to people in some groups (e.g., males in prison, the homeless, and American Indians), but little variation for other groups (e.g., twins, people named Michael or Nicole). We also explore other features of these data and consider ways in which survey data can be used to estimate network structure.

笔记与分组