RIDIR: Collaborative Research: Bayesian analytical tools to improve survey estimates for subpopulations and small areas

在这个项目中，将建立一套工具，对调查数据进行深入分析，利用和推广小群体估计的统计方法。传统的调查方法侧重于总体人口水平的估计，但我们可以通过小区域估计学到更多。该项目的目标是建立一个用户可访问的平台，用于模拟和可视化调查数据，给出人口中任意分组的估计数，以及显示感兴趣的估计数的可视化工具。该模型将适用于Stan，一个最先进的贝叶斯推理开源平台，并在合作国会选举调查(CCES)中实现。可以使用这些方法进行的一类分析的一个例子是关于投票中的人口差距如何随年龄、教育和州而变化的研究。多水平回归和后分层(MRP)的统计方法允许对人口的狭小部分进行推断。在调查方法的术语中，MRP是“基于模型的”，因为它使用回归来对小区域和人口切片进行部分汇集(平滑)，并且它在调整诸如年龄、性别、种族和教育等预测纳入样本的变量方面是“基于设计的”。使用灵活的工具而不是一次性分析来提取人口亚组的推论的一个原因是，关键变量可能会随着时间的推移而变化。多水平建模提供了针对大量预测因素进行调整的灵活性，这使得后期分层更加有效。作为一个额外的好处，这种建模和调整能够提取对一小部分人口的平均调查响应的估计，这些估计可以对应于消费者特别想要的非常类型的推论，并且通常在没有大量样本的情况下无法从调查中获得。该奖项反映了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。

In this project, a set of tools will be built for in-depth analysis of survey data, making use of and extending statistical methods for estimation for small subgroups. Classical methods for surveys are focused on aggregate population-level estimates but we can learn much more using small-area estimation. The goal of this project is to build a user-accessible platform for modeling and visualizing survey data that would give estimates for arbitrary subgroups of the population, along with visualization tools to display estimates of interest. The model would be fit in Stan, a state-of-the-art open-source platform for Bayesian inference, and implemented for the Cooperative Congressional Election Survey (CCES). An example of the sort of analysis that could be performed using these methods is a study of how demographic gaps in voting vary by age, education, and state.The statistical method of multilevel regression and poststratification (MRP) allows inferences for narrow slices of the population. In the terminology of survey methods, MRP is "model-based" in that it uses regression to do partial pooling (smoothing) for small areas and demographic slices, and it is "design-based" in adjusting for variables such as age, sex, ethnicity, and education that are predictive of inclusion in the sample. One reason for extracting inferences for population subgroups using a flexible tool rather than one-time analyses is that key variables can change over time. Multilevel modeling gives the flexibility to adjust for large numbers of predictors, which makes poststratification more effective. As a bonus, this modeling and adjustment enables extraction of estimates of average survey responses for small slices of the population, which can correspond to the very sorts of inferences that consumers particularly want, and which typically are unavailable from surveys without huge sample sizes.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.