Collaborative Research: Smooth National Measurement of Public Opinion Across Boundaries and Levels: A View From the Bayesian Spatial Approach

合作研究：跨越边界和层次的全国舆论平滑测量：贝叶斯空间方法的视角

• 批准号: 1630263
• 负责人: Jeff Gill
• 金额: $ 12.64万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2018-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1630263&HistoricalAwards=false
• 关键词: Collaborative; Research; Smooth; National; Measurement

This research project will measure public opinion in voting constituencies around the United States. The project will provide estimates of opinion in districts with little or no survey data, such as state legislative districts. The project's intellectual merit comes from establishing a new means for measuring public opinion that not only uses survey respondents' answers to polling questions but also incorporates important information about where respondents are located and what that implies about geographic patterns in public opinion. Coupled with population information from the U.S. Census, the project will produce stronger estimates of public sentiment when survey data are sparsely distributed. The investigators will release free software that includes user-friendly functions allowing any citizen to determine public opinion in his or her own district or in districts that have not yet cast any votes, such as proposed congressional districts in a redistricting cycle. The software will allow more sophisticated users to obtain measures for any variable (even if unrelated to public opinion) in relationship to geographic boundaries, which will have extensions to research in public health, epidemiology, economics, sociology, business, and law. The broader impact to society will be that the data and software from this project will provide more information for the news media, the public, and elected officials regarding the outlook of the nation by constituency and locale, thereby providing a better understanding of the American representation process. The project will recruit a diverse group of research assistants that will be trained in this kind of statistical analysis.Studies relating to public opinion often settle for less-than-ideal data. Frequently, researchers will measure public opinion in the 50 states or the 435 congressional districts by pooling together several surveys taken over time (losing a sense of change over time), using old measures of public opinion (which may not be consistent with current public views), or using presidential vote share to approximate public sentiment (which is prone to error because factors besides ideology affect vote choices). With smaller districts than these, such as state legislative districts, the problem is magnified considerably, because it is rare to have many survey respondents in such a small area. In this project, the investigators ask: How can survey responses and the geographic location of the respondents be used to reliably forecast constituency public opinion? To answer this question, the investigators will use the method of Bayesian universal kriging. This technique fits a training model over survey data to determine how demographic factors shape public opinion and how the portion of survey responses that cannot be explained by demographics can be explained by a geographically smoothed process. With a model like this, public opinion in constituencies can be predicted with known population demographics and the values of the geographically smoothed error process over that district.

这项研究项目将衡量美国各地选区的民意。该项目将提供在很少或没有调查数据的地区，如州立法区的意见估计。该项目的智力价值在于建立了一种衡量民意的新方法，不仅使用调查对象对民意调查问题的回答，而且还纳入了关于调查对象所在地的重要信息，以及这对民意地理格局的影响。再加上美国人口普查的人口信息，该项目将在调查数据分布稀疏时产生更强的公众情绪估计。 调查人员将发布免费软件，其中包括用户友好的功能，允许任何公民确定他或她自己的选区或尚未投票的选区的民意，例如在重新划分选区周期中提议的国会选区。 该软件将允许更复杂的用户获得与地理边界有关的任何变量（即使与民意无关）的测量结果，这将扩展到公共卫生，流行病学，经济学，社会学，商业和法律的研究。对社会更广泛的影响将是，该项目的数据和软件将为新闻媒体、公众和民选官员提供更多有关选区和地区国家前景的信息，从而更好地了解美国的代表过程。该项目将招募一批不同的研究助理，他们将接受这类统计分析的培训。通常，研究人员会将50个州或435个国会选区的民意调查汇总在一起（随着时间的推移失去了变化的感觉），使用旧的民意测量方法（可能与当前的公众观点不一致），或者使用总统投票份额来近似公众情绪（这很容易出错，因为除了意识形态之外的因素也会影响投票选择）。在比这些更小的地区，如州立法区，这个问题被放大了很多，因为很少有这么小的地区有很多调查对象。在这个项目中，调查人员问：如何调查的答复和受访者的地理位置被用来可靠地预测选区民意？为了回答这个问题，研究人员将使用贝叶斯通用克里金方法。该技术将训练模型拟合到调查数据上，以确定人口统计因素如何塑造公众舆论，以及如何通过地理平滑过程来解释无法由人口统计解释的调查响应部分。有了这样的模型，选区的民意可以用已知的人口统计数据和该地区地理平滑误差过程的值来预测。