Imputation and Variance Estimation for Survey Data

调查数据的插补和方差估计

• 批准号: 9803112
• 负责人: Jun Shao
• 金额: $ 5.97万
• 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: 1998
• 资助国家: 美国
• 起止时间: 1998-08-01 至 2002-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9803112&HistoricalAwards=false
• 关键词: Imputation; Variance; Estimation; Survey; Data

9803112 Jun ShaoThis research involves development of imputation techniques and variance estimation methods for survey data with nonrespondents. The investigator focues on (1) validating and comparing (both theoretically and empirically) the existing imputation techniques and developing better procedures if necessary; and (2) developing correct variance estimators for a given imputation method that produces correct survey estimates. Special attention will be paid on random hot deck imputation using models, nearest neighbor imputation, cold deck imputation, multivariate imputation, longitudinal imputation, imputation for quantiles, and imputation for non-ignorable response. Many variance estimation techniques (such as the linearization/Taylor expansion, jackknife, balanced half sample or balanced repeated replication, random groups, and bootstrap) will be studied. Particular issues that will be addressed in variance estimation include non-negligible sampling fractions, approximation in applying replication methods (such as grouping and collapsing), complex and composite imputation methods (in the sense that a number of different imputation methods are used and/or imputed data are used to impute nonrespondents for other variables), variance estimation for nearest neighbor imputation, variance estimation for sample quantiles, and problems with imputed values that cannot be identified from the data set.Most surveys have nonrespondents. Item nonresponse occurs when some sampled units cooperate in the survey but fail to provide answers to some questions. Commonly used compensation procedures for handling item nonresponse are imputation techniques which insert values for nonrespondents. It is a common practice to treat the imputed values as if they had been observed, and compute survey estimates and assess their varibility using standard formulas designed for the case of no nonresponse. This, however, could lead to some problems and biases in statistical analysis. For example, the use of standard formulas to assess varibility in analysis may seriously underestimate the true varibility, because standard formulas do not account for the changes in varibility due to nonresponse and/or imputation. This research involves development of correct and simple to implement statistical procedures to analyze survey data with nonrespondents and imputation; and will solve some real statistical problems in survey agencies such as the Census Bureau, the Bureau of Labor Statistics, and Westat.

9803112邵军这项研究涉及非受访者调查数据的补偿技术和方差估计方法的发展。研究人员专注于(1)验证和比较(理论和经验)现有的估算技术，并在必要时开发更好的程序；(2)为给定的估算方法开发正确的方差估计器，以产生正确的调查估计。将特别注意使用模型的随机热甲板估计、最近邻估计、冷甲板估计、多变量估计、纵向估计、分位数估计和不可忽略响应的估计。将研究多种方差估计技术(如线性化/泰勒展开、折刀、平衡半样本或平衡重复复制、随机分组和自举)。在方差估计中将解决的特殊问题包括不可忽略的抽样分数、应用复制方法时的近似性(例如分组和折叠)、复杂和复合的补偿方法(即使用许多不同的补偿方法和/或使用估算数据来对其他变量的非受访者进行估算)、最近邻的估算的方差估计、样本分位数的方差估计以及无法从数据集中识别的估计值的问题。当一些抽样单位在调查中配合调查，但未能回答某些问题时，就会出现项目无反应。通常用于处理项目无应答的补偿程序是为无应答插入值的补偿技术。通常的做法是，将估算的值视为已观察到的值，并使用为无反应的情况设计的标准公式来计算调查估计值并评估它们的可变性。然而，这可能会导致统计分析中的一些问题和偏差。例如，在分析中使用标准公式来评估变异性可能会严重低估真实的变异性，因为标准公式没有考虑由于无响应和/或归因引起的变异性的变化。这项研究涉及开发正确且易于实施的统计程序，以分析非受访者和归责的调查数据；并将解决人口普查局、劳工统计局和Westat等调查机构的一些实际统计问题。