Cost-effective sampling for social network data to minimize measurement error

描述(由申请人提供)：本申请建议开发社交网络方法中的三个关键组成部分：(A)评估不同抽样方案下网络统计数据和模型参数对各种类型缺失数据的敏感度，(B)为社交网络方法开发影响大小和功率分析的第一概念，以及(C)当网络研究人员面临有限的资源池时，具有成本效益的抽样方案可最大限度地实现“投资回报”。这些发展是通过一系列现代模拟完成的，同时结合先进的组合数据分析方法。所有的进步都是在一个用户友好的软件包中免费提供的。

DESCRIPTION (provided by applicant): This application proposes to develop three key components in social network methodology: (a) the assessment of the sensitivity of network statistics and model parameters to various types of missing data under different sampling schemes, (b) develops the first notions of effect sizes and power analysis for social network methodology, and (c) cost-effective sampling schemes that maximize the "return on investment" when network researchers are faced with a finite resource pool. These developments are accomplished through a series of modern simulations while being coupled with advanced combinatorial data analytic approaches. All advances are made freely available in a user-friendly software package.