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

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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.