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

小组调查是衡量个人、家庭和经济单位的强大工具，但几乎所有的小组调查都受到小组流失的影响。小组自然减员，即参加小组第一波的人在后一波中退出，如果退出的趋势与感兴趣的实质性结果系统相关，则会减少有效样本量，并可能在调查估计中引入偏差。分析员不可能通过只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在多大程度上影响分析。需要外部信息来源。更新样本--新的、随机抽样的受访者在第二次或随后的小组讨论中提供问卷调查的同时--可以提供这些信息。该项目开发了各种新的统计方法，以利用茶点样本中的信息来纠正由于面板磨损而产生的偏差。其基本思想是使用原始数据和刷新数据来估计统计模型，以归因于缺失值，从而产生有效校正磨损引起的偏差的完整数据集。这些方法将应用于两个备受瞩目的小组研究：2006-2008年的一般社会调查和2007-2008年的美联社/雅虎新闻选举小组。这项研究将改进使用刷新样本的小组研究的统计分析，从而从小组数据集中获得更准确的结论。更具体地说，该项目将为赞助带有茶点样本的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用途数据集。这项研究还将通过结合适当的统计方法和软件来展示茶点样本的优点，从而为未来小组研究的设计提供参考。

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.