Doctoral Dissertation Research: Estimating Congressional District-Level Opinions from National Surveys using a Bayesian Hierarchical Logistic Regression Model

本文的研究课题是建立一个以后分层单元为条件的二元响应变量均值的层次逻辑回归模型。其目的是从全国调查中得出国会地区层面的意见。该方法将小区域估计中常用的建模方法与后分层中使用的人口信息相结合。美国国家科学基金会的论文研究资助将用于从美国人口普查局购买数据，以获得国会地区一级性别、种族、年龄和教育程度的联合人口分布的可靠估计。为了对上面列出的所有变量以及国会选区进行后分层，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、种族和年龄的共同分布。该模型产生了州一级的可靠指标。

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.