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: 1926578
• 负责人: Andrew Gelman
• 金额: $ 63.22万
• 依托单位: Columbia 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=1926578&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.

在这个项目中，将建立一套工具，用于深入分析调查数据，利用和扩大统计方法，对小的分组进行估计。 传统的调查方法侧重于总体人口水平的估计，但我们可以了解更多使用小面积估计。 该项目的目标是建立一个用户可访问的平台，用于对调查数据进行建模和可视化，从而为人口的任意分组提供估计，沿着可视化工具以显示感兴趣的估计。 该模型将适用于Stan，这是一个最先进的贝叶斯推理开源平台，并为合作国会选举调查（CCES）实施。 可以使用这些方法进行分析的一个例子是研究投票中的人口差距如何随年龄、教育和州而变化，多水平回归和后分层（MRP）统计方法允许对人口的狭窄切片进行推断。 在调查方法的术语中，MRP是“基于模型的”，因为它使用回归对小区域和人口切片进行部分汇集（平滑），并且它是“基于设计的”，以调整年龄，性别，种族和教育等变量，这些变量可以预测样本中的包含。 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以随着时间的推移而变化。 多水平建模提供了调整大量预测因子的灵活性，这使得后分层更加有效。 作为奖励，这种建模和调整可以提取一小部分人群的平均调查响应估计值，这可以对应于消费者特别想要的各种推断，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响进行评估来支持审查标准。

项目成果

The Swing Voter Paradox: Electoral Politics in a Nationalized Era

摇摆选民悖论：国有化时代的选举政治

• 发表时间: 2021
• 期刊: Harvard University
• 作者: Shiro Kuriwaki

Bayesian hierarchical weighting adjustment and survey inference

贝叶斯分层权重调整和调查推断

• 发表时间: 2020
• 期刊: Survey methodology
• 影响因子: 0.9
• 作者: Si, Yajuan;Trangucci, Rob;Gabry, Jonah;and Gelman, Andrew
• 通讯作者: and Gelman, Andrew

Information, incentives, and goals in election forecasts

• DOI: 10.1017/s1930297500007981
• 发表时间: 2020-09
• 期刊: Judgment and Decision Making
• 影响因子: 2.5
• 作者: A. Gelman;J. Hullman;Christopher Wlezien;G. E. Morris