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

基本信息

  • 批准号:
    0318115
  • 负责人:
  • 金额:
    $ 21.49万
  • 依托单位:
  • 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

项目摘要

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来理解和检查多层模型的拟合。新的工具包括对非线性、相互作用和方差成分的模型的平均预测效果的计算,以及对多级模型的基于仿真的模型检查的泛化。同时,将探索民意和投票数据的多层次模型。该项目的一个核心应用领域是使用全国民意调查数据来估计不同州的民意时间趋势,这是现有的分别使用州和全国民意调查的方法无法解决的问题。一个相关的工作领域是模拟个体选民之间的依赖结构;也就是说,选民层面的模型可以将地区、州和国家加起来,以预测现实的群体层面的意见模式。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,多层次模型的诊断方法将广泛涉及社会科学和调查抽样领域的研究人员。其次,民意和投票模式的建模将与州级民意趋势的研究相关(在这个地理极化投票的现代时代,这是一个重要的主题),并有助于理解民意和投票之间的定量关系。

项目成果

期刊论文数量(0)
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Andrew Gelman其他文献

C3(H<sub>2</sub>O) – Generation, quantitation, and marker of human disease
  • DOI:
    10.1016/j.molimm.2018.06.058
  • 发表时间:
    2018-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Michelle Elvington;M. Kathryn Liszewski;Hrishikesh Kulkarni;Andrew Gelman;Alfred Kim;John Atkinson
  • 通讯作者:
    John Atkinson
A default prior distribution for logistic and other regression models ∗
逻辑和其他回归模型的默认先验分布 *
  • DOI:
  • 发表时间:
    2002
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Andrew Gelman;Aleks Jakulin;M. G. Pittau;Yu
  • 通讯作者:
    Yu
An improved BISG for inferring race from surname and geolocation
一种改进的 BISG,用于根据姓氏和地理位置推断种族
  • DOI:
    10.48550/arxiv.2310.15097
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    P. Greengard;Andrew Gelman
  • 通讯作者:
    Andrew Gelman
Ethics and Statistics: It's Too Hard to Publish Criticisms and Obtain Data for Republication
伦理与统计学:发表批评和获取重发表数据太难了
  • DOI:
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Andrew Gelman
  • 通讯作者:
    Andrew Gelman
Community prevalence of SARS-CoV-2 in England during April to September 2020: Results from the ONS Coronavirus Infection Survey
2020 年 4 月至 9 月英格兰 SARS-CoV-2 社区流行情况:ONS 冠状病毒感染调查结果
  • DOI:
    10.1101/2020.10.26.20219428
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    K. Pouwels;T. House;E. Pritchard;J. Robotham;Paul J. Birrell;Andrew Gelman;K. Vihta;N. Bowers;Ian Boreham;Heledd Thomas;James W Lewis;Iain Bell;J. Bell;J. Newton;J. Farrar;I. Diamond;P. Benton;A. Walker
  • 通讯作者:
    A. Walker

Andrew Gelman的其他文献

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{{ truncateString('Andrew Gelman', 18)}}的其他基金

Scalable Bayesian regression: Analytical and numerical tools for efficient Bayesian analysis in the large data regime
可扩展贝叶斯回归:在大数据领域进行高效贝叶斯分析的分析和数值工具
  • 批准号:
    2311354
  • 财政年份:
    2023
  • 资助金额:
    $ 21.49万
  • 项目类别:
    Standard Grant
RAPID: Flexible, Efficient, and Available Bayesian Computation for Epidemic Models
RAPID:灵活、高效、可用的流行病模型贝叶斯计算
  • 批准号:
    2055251
  • 财政年份:
    2020
  • 资助金额:
    $ 21.49万
  • 项目类别:
    Standard Grant
Collaborative Research: PPoSS: Planning: Scalable Systems for Probabilistic Programming
协作研究:PPoSS:规划:概率编程的可扩展系统
  • 批准号:
    2029022
  • 财政年份:
    2020
  • 资助金额:
    $ 21.49万
  • 项目类别:
    Standard Grant
RIDIR: Collaborative Research: Bayesian analytical tools to improve survey estimates for subpopulations and small areas
RIDIR:协作研究:贝叶斯分析工具,用于改进亚人群和小区域的调查估计
  • 批准号:
    1926578
  • 财政年份:
    2019
  • 资助金额:
    $ 21.49万
  • 项目类别:
    Standard Grant
CI-SUSTAIN: Stan for the Long Run
CI-SUSTAIN:长远发展
  • 批准号:
    1730414
  • 财政年份:
    2017
  • 资助金额:
    $ 21.49万
  • 项目类别:
    Standard Grant
Collaborative Research: Multilevel Regression and Poststratification: A Unified Framework for Survey Weighted Inference
协作研究:多级回归和后分层:调查加权推理的统一框架
  • 批准号:
    1534414
  • 财政年份:
    2015
  • 资助金额:
    $ 21.49万
  • 项目类别:
    Standard Grant
CI-ADDO-NEW: Stan, Scalable Software for Bayesian Modeling
CI-ADDO-NEW:Stan,用于贝叶斯建模的可扩展软件
  • 批准号:
    1205516
  • 财政年份:
    2012
  • 资助金额:
    $ 21.49万
  • 项目类别:
    Standard Grant
CMG: Reconstructing Climate from Tree Ring Data
CMG:从树木年轮数据重建气候
  • 批准号:
    0934516
  • 财政年份:
    2009
  • 资助金额:
    $ 21.49万
  • 项目类别:
    Standard Grant
Design and Analysis of "How many X's do you know" surveys for the study of polarization in social networks
用于研究社交网络极化的“你知道多少个 X”调查的设计和分析
  • 批准号:
    0532231
  • 财政年份:
    2005
  • 资助金额:
    $ 21.49万
  • 项目类别:
    Standard Grant
Doctoral Dissertation Research: Estimating Congressional District-Level Opinions from National Surveys using a Bayesian Hierarchical Logistic Regression Model
博士论文研究:使用贝叶斯分层逻辑回归模型从全国调查中估计国会选区级意见
  • 批准号:
    0241709
  • 财政年份:
    2003
  • 资助金额:
    $ 21.49万
  • 项目类别:
    Standard Grant

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