Leveraging Auxiliary Information on Marginal Distributions in Multiple Imputation for Survey Nonresponse

这个研究项目将发展各种方法和实用工具，以便利用来自辅助数据源的信息，例如由私营部门数据汇总者收集的行政记录和数据库，以调整调查中没有答复的情况。现代调查显示回复率急剧下降。这些下降威胁到基于那些不完整数据的二次分析的有效性。然而，政府机构和调查组织面临越来越大的预算压力，其结果是可用于广泛的无反应后续活动的资源越来越少。在这种环境下，政府机构和调查组织需要新的选择来处理丢失的数据。该项目将提供这些选项，增强数据生产者创建高质量公共使用数据集的能力，以弥补缺失数据。该项目将使数据使用者受益，包括使用调查数据的学者和对评估和纠正因无反应而导致的偏见的方法感兴趣的人。将开发一个开源包，并通过综合R档案网络广泛提供。这套方案将使各机构和其他用户能够利用方法上的进步。该项目将培养两名来自代表性不足群体的博士生，一名研究统计科学，一名研究政治学。该项目还将邀请两名本科生参与数据科学暑期研究体验。在这个项目中要解决的方法发展将集中在以下问题上：在调整无反应时，调查组织如何利用辅助数据源中提供的关于调查变量边际分布的信息？该项目将开发方法，使用户能够为不同的值块设定不同的缺失数据机制规范。该项目还将开发基于机器学习技术的多个插补例程，以处理具有大量变量的数据库中的辅助信息插补。多重输入框架不仅可以从缺失数据中传播不确定性，还可以从基于人口的具有潜在非平凡不确定性的辅助边缘信息中传播不确定性。该项目将融合贝叶斯建模和经典调查加权估计的特点，以确保估算考虑到复杂的调查设计。该方法将在审查当前人口调查（CPS）中人口分组的选民投票率的申请中加以说明。该应用程序将使用基于人口的辅助数据，这些数据来自美国选举项目（United States Elections Project）提供的政府选举统计数据，以及Catalist提供的选民档案。Catalist是一家领先的全国选民登记数据供应商。辅助边际的信息将被用来调整没有反应的CPS数据，使用比以前基于CPS的选民投票率分析更合理的假设集。CPS选民投票率应用程序将使学者和政策制定者了解选举参与中的不平等现象，并为提高选民投票率提供可能的政策选择。

This research project will develop methods and practical tools for leveraging the information from auxiliary data sources, such as administrative records and databases gathered by private-sector data aggregators, to adjust for nonresponse in surveys. Modern surveys have seen steep declines in response rates. These declines threaten the validity of secondary analyses based on those incomplete data. Government agencies and survey organizations are under increasing budgetary pressures, however, and the result is fewer resources available for extensive nonresponse follow-up activities. In this environment, government agencies and survey organizations need new options for handling missing data. This project will provide such options, enhancing the ability of data producers to create high-quality public use datasets that account for missing data. The project will benefit data users, including scholars who use survey data and those interested in methods for evaluating and correcting for biases due to nonresponse. An open-source package will be developed and made widely available via the Comprehensive R Archive Network. This package will enable agencies and other users to take advantage of the methodological advances. The project will train two Ph.D. students from underrepresented groups, one in statistical science and one in political science. The project also will engage two undergraduate students in a data science summer research experience.The methodological developments to be addressed in this project will focus on the following question: How can survey organizations take advantage of information about the marginal distributions of survey variables that are available in auxiliary data sources when adjusting for nonresponse? The project will develop methods that enable users to posit distinct specifications of missing data mechanisms for different blocks of values. The project also will develop multiple imputation routines based on machine learning techniques to handle imputation with auxiliary information in databases with large numbers of variables. The multiple imputation framework is leveraged to propagate uncertainty not only from the missing data, but also from population-based auxiliary marginal information with potentially non-trivial uncertainty. The project will fuse features of Bayesian modeling and classical survey-weighted estimation to ensure imputations account for complex survey designs. The methodology will be illustrated on an application examining voter turnout among subgroups of the population in the Current Population Survey (CPS). The application will use population-based auxiliary data from government election statistics available in the United States Elections Project and voter files available from Catalist, a leading national vendor of voter registration data. The information in the auxiliary margins will be used to adjust the CPS data for nonresponse with a more reasonable set of assumptions than previous analyses of voter turnout based on the CPS. The CPS voter turnout application will inform scholars and policy makers about inequalities in electoral participation and provides insights about possible policy alternatives for improving voter turnout.