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

RIDIR：协作研究：贝叶斯分析工具，用于改进亚人群和小区域的调查估计

• 批准号: 1926424
• 负责人: Stephen Ansolabehere
• 金额: $ 31.04万
• 依托单位: Harvard University
• 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: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926424&HistoricalAwards=false
• 关键词: RIDIR; Collaborative; Research; Bayesian; analytical

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.

在该项目中，将构建一组工具，以深入分析调查数据，利用并扩展统计方法以估算小型子组。 调查的经典方法集中于总人群级估计，但使用小区域估计，我们可以学到更多。 该项目的目的是构建一个可访问用户的平台，用于建模和可视化调查数据，以允许对人群的任意亚组进行估算，并提供可视化工具以显示感兴趣的估计值。 该模型将适合Stan，这是贝叶斯推论的最先进的开源平台，并为合作国会选举调查（CCES）实施。 可以使用这些方法进行的分析的一个例子是研究投票差距如何因年龄，教育和状态而变化。多层次回归和延伸后（MRP）的统计方法允许推断人口狭窄的片段。 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合并（平滑），并且在调整诸如年龄，性别，族裔和教育等变量时，它是“基于设计的”，可以预测样本中包含的变量。 使用灵活的工具而不是一次性分析提取人群亚组的推断的原因之一是，关键变量可以随时间变化。 多级建模可以灵活地调整大量预测指标，这使得阶段更有效。 作为奖励，这种建模和调整能够提取少数人群的平均调查响应估计值，这可以与消费者特别想要的推论相对应，并且通常从没有样本大小的情况下无法进行调查，而没有大量的调查。该奖项反映了NSF的法定任务，并通过评估了基础的范围，并反映了对基金会的支持，并且对基金会的范围进行了评估，并反映了基金会的范围。