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
• 负责人: Andrew Gelman
• 金额: $ 1.2万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-02-15 至 2003-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0241709&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Estimating; Congressional

This dissertation research project constructs a hierarchical logistic regression model for the mean of a binary response variable conditional on poststratification cell. The object is to produce congressional district-level opinions from national surveys. This approach combines the modeling approach often used in small-area estimation with the population information used in poststratification. The NSF dissertation research grantwill be used to purchase data from the US Census Bureau to obtain reliable estimates of the joint population distributions of sex, ethnicity, age, and education at the congressional district-level. In order to poststratify on all the variables listed above, along with the congressional district, we need the joint population distribution of the demographic variables within each congressional district. Currently, the U.S. Census only provides without charge the joint distributions of sex, ethnicity, and age. The model has produced reliable measures that the state-level.

本论文的研究计画是建立一个以后分层细胞为条件的二元反应变数均值的阶层式逻辑回归模型。 其目的是从全国调查中产生国会选区一级的意见。这种方法结合了小区域估计中常用的建模方法和后分层中使用的人口信息。NSF的论文研究可以用来购买美国人口普查局的数据，以获得可靠的估计联合人口分布的性别，种族，年龄和教育在国会选区一级。为了对上面列出的所有变量进行后分层，沿着国会选区，我们需要每个国会选区内人口统计变量的联合人口分布。 目前，美国人口普查只免费提供性别、种族和年龄的联合分布。 该模型产生了可靠的措施，国家一级。