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

这一研究项目将制定方法和实用工具，利用来自辅助数据来源的信息，例如由私营部门数据聚集者收集的行政记录和数据库，以调整调查中的无答复。现代调查显示，回应率急剧下降。这些下降威胁到基于这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着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减少。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。该项目将提供这样的选择，加强数据生产者创建高质量的公共使用数据集的能力，以解决丢失的数据。该项目将使数据用户受益，包括使用调查数据的学者和那些对评估和纠正因无答复而产生的偏差的方法感兴趣的人。将开发一个开放源码包，并通过全面R档案网广泛提供。这一一揽子计划将使各机构和其他用户能够利用方法上的进步。该项目将培训两名来自代表性不足群体的博士生，一名是统计科学，一名是政治学。该项目还将邀请两名本科生参加数据科学暑期研究体验。该项目中要解决的方法学发展将集中在以下问题上：调查组织如何利用辅助数据源中可用的调查变量的边际分布信息，当调整无回答时？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的不同规范。该项目还将开发基于机器学习技术的多个归罪例程，以处理具有大量变量的数据库中具有辅助信息的归罪。多重补偿框架不仅被用来传播来自缺失数据的不确定性，而且被用来传播具有潜在非平凡不确定性的基于总体的辅助边缘信息。该项目将融合贝叶斯建模和经典调查加权估计的特征，以确保分配考虑到复杂的调查设计。这一方法将在《当前人口统计调查》(CPS)中审查人口分组选民投票率的应用程序中说明。该应用程序将使用美国选举项目提供的政府选举统计数据中的基于人口的辅助数据，以及领先的全国选民登记数据供应商Catist提供的选民文件。辅助边距中的信息将被用来调整无反应的CPS数据，并采用一套比以前根据CPS对选民投票率进行的分析更合理的假设。CPS选民投票率应用程序将向学者和政策制定者通报选举参与方面的不平等，并提供有关提高选民投票率的可能政策替代方案的见解。

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.