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

描述（由申请人提供）：本申请提出开发社交网络方法中的三个关键组件：（a）在不同的抽样方案下，评估网络统计和模型参数对各种类型缺失数据的敏感性，（B）为社会网络方法学发展了效应大小和功效分析的第一个概念，以及（c）在网络研究人员面临有限的资源库时最大限度地提高“投资回报”的具有成本效益的抽样办法。这些发展是通过一系列现代模拟，同时结合先进的组合数据分析方法来实现的。所有进展都以方便用户的软件包免费提供。

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.