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

对社交网络数据进行经济高效的采样，以最大程度地减少测量误差

• 批准号: 8732584
• 负责人: Douglas L Steinley
• 金额: $ 14.02万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-10 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8732584
• 关键词: Address; Affect; Area; Back; Computer software; Coupled; Data; Data Analyses; Development; Exhibits; Foundations; Funding Mechanisms; Graph; Individual; Investments; Link; Measurement; Measures; Methodology; Methods; Modeling; Pathway Analysis; Research Personnel; Resources; Sample Size; Sampling; Sampling Studies; Scheme; Series; Social Network; Techniques; Walking; base; combinatorial; cost effective; design; interest; network models; predictive modeling; public health relevance; simulation; statistics; user friendly software

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.

描述（由申请人提供）：这个应用程序提出开发社会网络方法论的三个关键组成部分：(a)评估网络统计和模型参数在不同采样方案下对各种缺失数据的敏感性，(b)为社会网络方法论开发了效应大小和功率分析的第一个概念，以及(c)在网络研究人员面临有限资源池时最大化“投资回报”的成本效益采样方案。这些发展是通过一系列现代模拟来完成的，同时与先进的组合数据分析方法相结合。所有的进展都在一个用户友好的软件包中免费提供。