How Many People Do You Know?: Efficiently Estimating Personal Network Size

• 成果类型: Article
• 署名作者: McCormick, Tyler H.; Salganik, Matthew J.; Zheng, Tian
• 署名单位: Columbia University; Princeton University; Princeton University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.ap08518
• 发表日期: 2010
• 页码: 59-70
• 关键词: INFORMANT ACCURACY; social networks; seroprevalence; subpopulation; FRAMEWORK; contacts; error

摘要

In this article we develop a method to estimate both individual social network size (ie, degree) and die distribution of network sizes in a population by asking respondents how many people they know in specific subpopulations (c g people named Michael) Building on the scale-up method of Killworth ei al (1998b) and other previous attempts to estimate individual network size we propose a latent non-random mixing model which resolves three known problems with previous approaches As a byproduct our method also provides estimates of the rate of social nix tug between population groups We demonstrate the model using a sample of 1.370 adults orginally collected by McCarty et al (2001) Based on insights developed during the statistical modeling, we conclude by offering practical guidelines for the design of future surveys to estimate social network size Most importantly. we show that if the first names asked about rue chosen properly, the estimates from the simple scale-up model enjoy the same bias-reduction as the estimates front our more complex latent nonrandom mixing model