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

利用多重插补中边际分布的辅助信息来解决调查无答复问题

基本信息

批准号：
1733835
负责人：
Jerome Reiter
金额：
$ 30万
依托单位：
Duke University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1733835&HistoricalAwards=false
关键词：
Leveraging Auxiliary Information Marginal Distributions

项目摘要

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.

这一研究项目将制定方法和实用工具，利用私营部门数据汇总机构收集的行政记录和数据库等辅助数据来源的信息，对调查中的无答复情况进行调整。现代调查的答复率急剧下降。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越来越大的预算压力，结果是可用于广泛的无答复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缺失的数据。该项目将提供这些选项，提高数据生产者创建高质量的公共使用数据集的能力，以说明缺失的数据。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答复而产生的偏见的方法感兴趣的人。将开发一个开放源码软件包，并通过综合R档案网络广泛提供。这套资料将使各机构和其他用户能够利用方法上的进步。该项目将培养两名博士。来自代表性不足群体的学生，一个在统计科学，一个在政治科学。该项目还将聘请两名本科生在数据科学暑期研究experience.The方法的发展，以解决在这个项目将集中在以下问题：调查组织如何利用的边缘分布的信息，可在辅助数据源的调查变量时，调整无响应？该项目将制定方法，使用户能够为不同的价值块确定缺失数据机制的不同规格。该项目还将开发基于机器学习技术的多个插补例程，以在具有大量变量的数据库中处理辅助信息的插补。利用多重插补框架不仅可以传播来自缺失数据的不确定性，还可以传播来自具有潜在非平凡不确定性的基于人口的辅助边缘信息的不确定性。该项目将融合贝叶斯建模和经典调查加权估计的特征，以确保插补能够解释复杂的调查设计。该方法将在当前人口调查中审查人口分组投票率的应用程序中加以说明。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基于人口的辅助数据，以及全国领先的选民登记数据供应商Catalist提供的选民档案。在辅助利润率的信息将被用来调整CPS数据的无响应与一个更合理的假设比以前的分析选民投票率的基础上CPS。 CPS选民投票率应用程序将向学者和决策者通报选举参与方面的不平等现象，并提供有关提高选民投票率的可能政策选择的见解。