Multilevel Modeling for the Study of Public Opinion and Voting

用于民意和投票研究的多层次建模

• 批准号: 0318115
• 负责人: Andrew Gelman
• 金额: $ 21.49万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0318115&HistoricalAwards=false
• 关键词: Multilevel; Modeling; Study; Public; Opinion

This project will develop a general set of tools for understanding and checking the fit of multilevel models. The new tools include computations of average predictive effects for models with nonlinearity, interactions, and variance components, and generalization of simulation-based model checking for multilevel models. In parallel, multilevel models will be explored for public opinion and voting data. A central application area of this project is to use national poll data to estimate time trends in public opinion for different states, a problem that cannot be solved by existing approaches using state and national polls separately. A related area of work is to model dependence structures among individual voters; that is, voter-level models that can add up districts, states, and the country to predict realistic group-level opinion patterns. This has implications for voting power and also is related to studies of networks in probability theory and sociology.This project is anticipated to have broader impacts in two ways. First, the diagnostic methods for multilevel models will be relevant to a wide range of researchers in social science and survey sampling. Second, the modeling of public opinion and voting patterns will be relevant to studies of state-level opinion trends (an important topic in this modern era of geographically-polarized voting) and for understanding the quantitative relationships between opinion and voting.

该项目将开发一组通用的工具，以理解和检查多级模型的拟合度。 新工具包括针对非线性，交互和方差组件模型的平均预测效应的计算，以及对多级模型的基于仿真的模型检查的概括。 同时，将探索多层次模型，以获取公众舆论和投票数据。 该项目的一个中心应用领域是使用国家民意调查数据来估计不同州的公众舆论的时间趋势，这一问题无法通过单独使用州和国家民意调查来解决现有方法。 相关的工作领域是模拟各个选民之间的依赖性结构；也就是说，选民级别的模式可以加大地区，州和国家，以预测现实的团体级别舆论模式。 这对投票能力具有影响，并且与概率理论和社会学中的网络研究有关。预计该项目将以两种方式产生更大的影响。 首先，多级模型的诊断方法将与社会科学和调查采样的广泛研究人员有关。 其次，公众舆论和投票模式的建模将与对国家级别舆论趋势的研究（现代地理偏振投票时代的重要话题）有关，并了解观点与投票之间的定量关系。