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.

该研究项目将衡量美国各地投票选区的民意。该项目将为很少或没有调查数据的地区（例如州立法区）提供意见估计。该项目的智力价值来自于建立一种衡量民意的新方法，该方法不仅使用调查受访者对民意调查问题的回答，而且还纳入了有关受访者所在位置的重要信息以及这对公众舆论中的地理模式意味着什么。结合美国人口普查的人口信息，当调查数据分布稀疏时，该项目将产生更强有力的公众情绪估计。 调查人员将发布免费软件，其中包括用户友好的功能，允许任何公民确定其所在选区或尚未投票的选区（例如重新划分周期中拟议的国会选区）的民意。 该软件将允许更熟练的用户获得与地理边界相关的任何变量（即使与舆论无关）的测量，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关于按选区和地区划分的国家前景的信息，从而更好地了解美国的代表性过程。该项目将招募一批多元化的研究助理，他们将接受此类统计分析的培训。与公众舆论相关的研究通常会采用不太理想的数据。通常，研究人员会通过将一段时间内进行的多项调查汇总在一起（失去随时间变化的感觉）来衡量 50 个州或 435 个国会选区的民意，使用旧的民意衡量标准（这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，因为意识形态之外的因素也会影响投票选择）。对于比这些更小的地区，例如州立法区，这个问题会被大大放大，因为在如此小的地区很少有这么多的调查受访者。在这个项目中，调查人员提出这样的问题：如何利用调查结果和受访者的地理位置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泛克里金法。该技术适合调查数据的训练模型，以确定人口统计因素如何影响公众舆论，以及如何通过地理平滑过程来解释无法用人口统计数据解释的调查响应部分。通过这样的模型，可以利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选区的公众舆论。