Analysis of Survey Data Using Imputation for Nonrespondents

使用非受访者插补分析调查数据

• 批准号: 0705033
• 负责人: Jun Shao
• 金额: $ 21.64万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0705033&HistoricalAwards=false
• 关键词: Analysis; Survey; Data; Using; Imputation

Imputation is a popular technique in handling nonresponse in surveys. This project focuses on the development of imputation methods that produce approximately unbiased and efficient survey estimators when imputed values are treated as observed data and standard methods are applied to compute the survey estimators. Various imputation methods will be studied, such as the nearest neighbor imputation, kernel nonparametric regression imputation, empirical likelihood, and techniques of handling measurement error. Emphasis will be placed on the study of multivariate survey variables and/or multivariate covariates, and problems with nonresponse in not only the main survey variables but also the covariates. For each imputation method, variance estimation that takes nonresponse and imputation into account will be studied, using a direct derivation approach or a replication method (such as the jackknife, the balanced half samples, the random groups, and the bootstrap) that contains a re-imputation component to assess the variability caused by imputation. In particular, some shortcut replication methods that reduce the amount of computation will be investigated.Many statistics and government agencies collect data through surveys. Most surveys have nonresponse. Item nonresponse occurs when some sampled units cooperate in the survey but fail to provide answers to some questions. Imputation techniques, which insert values for nonrespondents, are commonly used compensation procedures for item nonresponse. In some cases, when auxiliary information is properly used, imputation increases statistical accuracy. An essential requirement for an imputation method is that one can obtain unbiased (or approximately unbiased) survey estimators and their variability estimators by treating the imputed values as observed data and using the standard estimation formulas designed for the case of no nonresponse. This requires developments on imputation methodology and statistical analysis procedures to take nonresponse and imputation into account. Since most of the proposed research topics are motivated by problems in survey agencies such as the Census Bureau, the Bureau of Labor Statistics, Westat, and Statistics Canada, results obtained from the proposed research will have significant impacts on the imputation and variance estimation methodology for these survey agencies. The research is supported by the Methodology, Measurement, and Statistics Program, the Statistics and Probability Program, and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.

插补是处理调查中无回答问题的一种常用技术。 该项目的重点是插补方法的发展，产生近似无偏和有效的调查估计值时，插补值被视为观察数据和标准方法应用于计算调查估计。 各种填补方法将被研究，如最近邻填补，核非参数回归填补，经验似然，和处理测量误差的技术。 重点将放在多变量调查变量和/或多变量协变量的研究，以及不仅在主要调查变量，而且在协变量无响应的问题。 对于每种插补方法，将使用直接推导方法或重复方法（如刀切法、平衡半样本、随机组和自助法）研究考虑无应答和插补的方差估计，其中包含重新插补组件，以评估插补引起的变异性。 特别是一些减少计算量的快捷复制方法将被调查。许多统计和政府机构通过调查收集数据。 大多数调查没有回答。 项目无应答是指某些抽样单位在调查中合作，但未能提供某些问题的答案。 插补技术，插入值的nonresponses，是常用的补偿程序项目无应答。 在某些情况下，如果适当使用辅助信息，插补可提高统计准确性。 插补方法的一个基本要求是，通过将插补值视为观察数据并使用为无无响应情况设计的标准估计公式，可以获得无偏（或近似无偏）调查估计量及其变异性估计量。 这就需要发展估算方法和统计分析程序，以考虑到无答复和估算。 由于大多数拟议的研究课题的动机调查机构，如人口普查局，劳工统计局，Westat和加拿大统计局的问题，从拟议的研究所获得的结果将有显着影响这些调查机构的插补和方差估计方法。 这项研究得到了方法、测量和统计方案、统计和概率方案以及联邦统计机构联盟的支持，作为支持调查和统计方法研究的联合活动的一部分。