How Many People Do You Know?: Efficiently Estimating Personal Network Size
你认识多少人？高效估计个人社会网络规模AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确估计个人社会网络度数、度数分布及群体之间的混合率。
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你认识多少人？高效估计个人社会网络规模
- 研究问题
- 如何通过受访者认识特定子群体人数的回答，估计个人社会网络规模、总体规模分布及群体之间的社会混合程度？
- 方法与思路
- 作者在规模外推方法基础上提出潜在非随机混合模型，用调查数据估计个人度数、总体度数分布和群体混合率。
- 材料陈述的贡献
- 摘要称，模型解决既有方法的三个问题，并提出调查设计建议；恰当选择姓名子群可使简单外推获得相同偏差减小。
- 阅读关注点
- 应核对非随机混合模型的识别、三个既有问题的具体含义，以及姓名选择使简单方法减偏的条件和调查可行性。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；未列出三个问题或姓名选择条件，不能推断一般调查中均有相同减偏。
原文摘要
In this paper we develop a method to estimate both individual social network size (i.e., degree) and the distribution of network sizes in a population by asking respondents how many people they know in specific subpopulations (e.g., people named Michael). Building on the scale-up method of Killworth et 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 mixing between population groups. We demonstrate the model using a sample of 1,370 adults originally 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 to be asked about are chosen properly, the simple scale-up degree estimates can enjoy the same bias-reduction as that from the our more complex latent non-random mixing model.