BIGDATA: IA: A Multi-phase Survey Strategy for Generalizing Inferences from Big Data

BIGDATA：IA：用于概括大数据推论的多阶段调查策略

• 批准号: 1837959
• 负责人: Michael Robbins
• 金额: $ 99.11万
• 依托单位: Rand Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837959&HistoricalAwards=false
• 关键词: BIGDATA; IA; Multi; phase; Survey

Many researchers argue that commercial services that yield a high volume of data have little scientific utility because their users are not representative of the general population. This project develops methods for generalizing inferences drawn from non-representative big data sources. The researchers are implementing a multi-phase survey to compare survey results with online social networking data. They will collect a nationwide probability sample and a separate sample of online users, and use novel statistical procedures to combine them in a manner will enable statistically valid estimators to be produced from the universe of online users. The results of this project will be broadly applicable by enabling more accurate statistical models of to be created for applications in medicine, economics, and many other fields. Statistical weighting will be used to match the online media sample with the probability survey sample across a set of auxiliary variables observed for both samples. The researchers will then match the data in the universe of online users to the weighted sample across a second set of variables that is measured for all users. Doing so will enable extracting results from media users that are more representative of the general population. To produce real-time forecasts, Bayesian models for mixed frequency time series will be used to combine the weighted online-based analyses with traditional polls.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

许多研究人员认为，产生大量数据的商业服务几乎没有科学效用，因为它们的用户并不代表一般人群。该项目开发了从非代表性大数据源中归纳推理的方法。研究人员正在实施一项多阶段调查，将调查结果与在线社交网络数据进行比较。他们将收集一个全国性的概率样本和一个单独的在线用户样本，并使用新的统计程序将它们以一种方式联合收割机结合起来，从而能够从在线用户的宇宙中产生统计上有效的估计值。 该项目的结果将通过创建更准确的统计模型来广泛应用于医学，经济学和许多其他领域。 将使用统计加权将在线媒体样本与概率调查样本在两个样本观察到的一组辅助变量中进行匹配。然后，研究人员将在线用户的数据与第二组变量的加权样本进行匹配，这些变量是针对所有用户测量的。这样做将能够从更能代表普通人群的媒体用户中提取结果。为了产生实时预测，混合频率时间序列的贝叶斯模型将被用于联合收割机结合加权的在线分析与传统的民意调查。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。