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.

这个项目将开发一套通用的工具，用于理解和检查多层次模型的拟合。 这些新工具包括计算具有非线性、相互作用和方差分量的模型的平均预测效果，以及对多水平模型进行基于模拟的模型检验。 与此同时，将探讨民意和投票数据的多层次模型。 该项目的一个中心应用领域是使用全国民意调查数据来估计不同州民意的时间趋势，这是一个无法通过单独使用州和全国民意调查的现有方法来解决的问题。 一个相关的工作领域是对个体选民之间的依赖结构进行建模;也就是说，选民级别的模型可以将地区、州和国家相加，以预测现实的群体级别的意见模式。 这对投票权有影响，也与概率论和社会学中的网络研究有关。预计该项目将在两个方面产生更广泛的影响。 首先，多水平模型的诊断方法将与社会科学和调查抽样领域的广泛研究人员相关。 其次，民意和投票模式的建模将与州一级的民意趋势（在这个地理极化投票的现代时代的一个重要课题）的研究相关，并了解意见和投票之间的定量关系。