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

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，因为它们的用户不能代表普通民众。这个项目开发了从不具代表性的大数据来源中得出的推论的泛化方法。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。他们将收集全国范围内的概率样本和单独的在线用户样本，并使用新的统计程序将它们结合在一起，使之能够从所有在线用户中产生统计上有效的估计值。这个项目的结果将广泛适用，因为它能够为医学、经济和许多其他领域的应用创建更准确的统计模型。将使用统计加权来将在线媒体样本与概率调查样本在两个样本所观察到的一组辅助变量中进行匹配。然后，研究人员将把在线用户群体中的数据与针对所有用户测量的第二组变量的加权样本进行匹配。这样做将能够从更能代表一般人群的媒体用户那里提取结果。为了产生实时预测，将使用混合频率时间序列的贝叶斯模型将基于加权的在线分析与传统民意调查相结合。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.