Bayesian Methodology for Disclosure Limitation and Statistical Analysis of Large Government Surveys

涉及分组和分层的复杂概率抽样设计的调查是供政府和科学使用的经验数据的主要来源。调查推断的标准方法是以设计为基础的，统计推断基于抽样分布，总体值被视为固定的。这种方法一直是发展大型调查的客观统计分析的强大力量，在对人口的薄弱假设下具有可靠的操作特点。然而，以设计为基础的范例过于局限，无法处理：(1)从各种来源，如调查、人口普查和行政记录获得的数据越来越多；(2)对分析的需求日益增加，超出了简单的描述性信息，如大领域的平均值和总数；(3)为保密而开发和分析数据；以及(4)对单位和项目无反应的数据进行分析。这些问题可以通过基于模型的贝叶斯方法来解决，模型可以捕捉被研究人群的相关特征，并考虑到样本设计的重要特征，以及限制分析主观性的非信息性先验。随着计算工具的发展，贝叶斯方法正在统计学中复兴，使其在实际中可行。然而，贝叶斯方法在抽样调查中的应用仍然非常有限。这项研究的目标是开发实用的贝叶斯方法，用于抽样调查推断，并具有良好的基于设计的特性。传播公共使用数据文件对研究界来说至关重要，以便进行研究，形成合理决策的基础。这项研究将开发传播详细微数据文件的方法，极大地降低向数据入侵者泄露受访者身份的风险。方法将基于对关键变量的多重归因，这种方法允许使用现有软件进行有效的统计推断，并限制信息损失的程度。这些方法将在国家卫生统计中心和其他联邦机构收集的大型政府调查中进行测试。这项研究还将为复杂调查的统计分析中的三个主题开发贝叶斯方法：(I)对抽样单位具有不同纳入概率的调查的分析；(Ii)调查中单位和项目无回答的处理；以及(Iii)使用旋转面板设计对收集的样本进行分析。这项研究得到了方法学、测量和统计计划以及调查和统计方法学资助机会研究下的一个联邦统计机构联盟的支持。

Surveys with complex probability sampling designs involving clustering and stratification are a major source of empirical data for governmental and scientific use. The standard approach to survey inference is design-based, with statistical inferences being based on the sampling distribution with population values treated as fixed. This approach has been a powerful force for the development of objective statistical analysis of large surveys, with reliable operating characteristics under weak assumptions about the population. However, the design-based paradigm is too limited to handle: (i) the increased availability of data from a variety of sources, such as surveys, censuses and administrative records; (ii) increasing demands for analyses that go beyond simple descriptive information such as means and totals for large domains; (iii) the development and analysis of data masked to preserve confidentiality; and (iv) the analysis of data subject to unit and item nonresponse. These questions can be addressed by a model-based Bayesian approach, with models that capture the relevant features of the population under study and take into account important features of the sample design, and non-informative priors that limit subjectivity in the analysis.Bayesian methods are enjoying a resurgence in statistics, with the development of computational tools that make them practically feasible. However, the application of Bayesian methods to sample surveys remains very limited. The goal of this research is to develop useful, practical Bayesian methods for sample survey inference that have good design-based properties. The dissemination of public use data files is crucial to the research community in order to conduct research that forms the basis for rational policy decisions. This research will develop methods for disseminating detailed micro-data files that greatly reduce the risk of disclosure of the identity of respondents to a data intruder. Methods will be based on multiple imputation of key variables, an approach that allows for valid statistical inferences using existing software and limits the degree of information loss. The methods will be tested on large government surveys collected by the National Center for Health Statistics and other federal agencies. This research also will develop Bayesian methods for three topics in the statistical analysis of complex surveys: (i) the analysis of surveys where the sampled units have differential probabilities of inclusion; (ii) the handling of unit and item nonresponse in surveys; and (iii) the analysis of samples collected using rotating panel designs. This research is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies under the Research on Survey and Statistical Methodology Funding Opportunity.