How many people do you know in prison?: Using overdispersion in count data to estimate social structure in networks

被引:102
作者
Zheng, Tian [1 ]
Salganik, Matthew J.
Gelman, Andrew
机构
[1] Columbia Univ, Dept Stat, New York, NY 10027 USA
[2] Columbia Univ, Dept Polit Sci, New York, NY 10027 USA
[3] Columbia Univ, Dept Sociol, New York, NY 10027 USA
[4] Columbia Univ, Inst Social & Econ Res & Policy, New York, NY 10027 USA
基金
美国国家科学基金会;
关键词
negative binomial distribution; overdispersion; sampling; social networks; social structure;
D O I
10.1198/016214505000001168
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
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.
引用
收藏
页码:409 / 423
页数:15
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