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）：本申请提议在社交网络方法论中开发三个关键组成部分：（a）评估网络统计数据和模型参数的敏感性在不同样本下的各种类型的丢失数据中的敏感性，（b）开发了对社交网络方法的效应大小和功能分析的第一个概念，以及（c）对效率分析的效率分析，以及（c）成本效益，（c）具有成本效益的范围，（c）效率分析了（c），（c）效率分析了（c），（c）成本效益的分析，则效率分析效果。 水池。这些发展是通过一系列现代模拟来实现的，同时与先进的组合数据分析方法相结合。所有进步均在用户友好的软件包中免费提供。