Multiple Imputation Methods for Handling Missing Data in Longitudinal Studies with Refreshment Samples

处理更新样本纵向研究中缺失数据的多重插补方法

• 批准号: 1061241
• 负责人: Jerome Reiter
• 金额: $ 16万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1061241&HistoricalAwards=false
• 关键词: Multiple; Imputation; Methods; Handling; Missing

Panel surveys are a powerful tool for measuring individuals, households, and economic units, but almost all suffer from panel attrition. Panel attrition, whereby those participating in the first wave of a panel drop out in later waves, reduces the effective sample size and can introduce bias in survey estimates if the tendency to drop out is systematically related to the substantive outcomes of interest. It is not possible for analysts to determine the degree to which attrition degrades analyses by using only the collected data without making untestable assumptions about the attrition process. External sources of information are needed. Refreshment samples -- new, randomly-sampled respondents given the questionnaire at the same time as a second or subsequent wave of the panel -- can provide this information. The project develops a variety of novel statistical methodologies for utilizing the information in refreshment samples to correct biases due to panel attrition. The underlying idea is to use the original and refreshment data to estimate statistical models for imputation of the missing values, thereby resulting in completed datasets that effectively correct for biases caused by attrition. Applications of the methods will be made to two high-profile panel studies with refreshment samples: the 2006-2008 General Social Survey and the 2007-2008 AP/Yahoo News Election Panel. This research will improve statistical analyses of panel studies with refreshment samples, hence enabling more accurate conclusions from panel datasets. More specifically, the project will provide government agencies that sponsor large panel studies with refreshment samples with better options for creating public-use datasets that account for attrition. The research also will inform the design of future panel studies by demonstrating the virtues of refreshment samples when coupled with appropriate statistical methods and software.

追踪调查是衡量个人、家庭和经济单位的有力工具，但几乎所有的追踪调查都存在追踪流失的问题。 小组减员，即参加小组第一波的人在以后的几波中退出，减少了有效的样本量，如果退出的趋势与感兴趣的实质性结果有系统的关系，则可能在调查估计中引入偏差。 分析师不可能仅使用收集的数据而不对损耗过程做出不可检验的假设来确定损耗降低分析的程度。 需要外部信息来源。 刷新样本-新的，随机抽样的受访者给予问卷调查的同时，作为第二波或后续波的小组-可以提供这一信息。 该项目开发了各种新颖的统计方法，利用信息在点心样本，以纠正偏见，由于面板磨损。 其基本思想是使用原始数据和更新数据来估计用于填补缺失值的统计模型，从而产生有效校正由损耗引起的偏差的完整数据集。 这些方法将应用于两个高知名度的小组研究与刷新样本：2006-2008年综合社会调查和2007-2008年美联社/雅虎新闻选举小组。该研究将改进具有刷新样本的面板研究的统计分析，从而使面板数据集的结论更准确。 更具体地说，该项目将为赞助大型小组研究的政府机构提供更好的选择，以创建考虑自然减员的公共使用数据集。 该研究还将通过展示与适当的统计方法和软件相结合时刷新样本的优点，为未来面板研究的设计提供信息。