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

这项研究项目将衡量美国各地投票选区的民意。该项目将在调查数据很少或没有调查数据的地区提供意见估计，例如州立法区。该项目的学术价值来自于建立了一种衡量民意的新方法，这种方法不仅使用调查受访者对民意调查问题的回答，还纳入了关于受访者所在位置的重要信息，以及这意味着公众舆论中的地理模式。再加上美国人口普查的人口信息，当调查数据分散分布时，该项目将产生对公众情绪的更强估计。调查人员将发布包括用户友好功能的免费软件，允许任何公民确定自己选区或尚未投票的选区的民意，例如重新划分选区的拟议国会选区。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的测量(即使与公众舆论无关)，这将扩展到公共卫生、流行病学、经济学、社会学、商业和法学的研究。对社会的更广泛影响将是，这个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程序。该项目将招聘一组不同的研究助理，他们将接受这种统计分析方面的培训。与公众舆论有关的研究往往满足于不太理想的数据。通常，研究人员将通过将几项随着时间推移进行的调查汇集在一起来衡量50个州或435个国会选区的民意(随着时间的推移失去了变化感)，使用旧的民意衡量标准(这可能与当前的公众观点不一致)，或者使用总统选票份额来近似公众情绪(这容易出错，因为意识形态以外的因素会影响投票选择)。有了比这些更小的选区，比如州立法选区，这个问题就被放大了很多，因为在这么小的区域里有这么多受访者是很少见的。在这个项目中，调查人员问：如何利用调查结果和受访者的地理位置来可靠地预测选民的民意？为了回答这个问题，调查人员将使用贝叶斯泛克里格法。这项技术适用于调查数据的训练模型，以确定人口因素如何塑造公众舆论，以及人口统计无法解释的调查答复部分如何通过地理上平滑的过程来解释。有了这样的模型，可以用已知的人口统计数据和该地区的地理平滑误差过程的值来预测选区的民意。

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.