Assistant Professor of MarketingUniversity of Notre Dame
Modeling Missing Data in Panel Studies with Multiple Refreshment Samples
Most panel surveys are subject to missing data problems caused by panel attrition. The Additive Non-ignorable (AN) model proposed by Hirano et al. (2001) utilizes refreshment samples in panel surveys to impute missing data, and offers flexibility in modeling the missing data mechanism to incorporate both ignorable and non-ignorable models. We extend the AN model to settings with three waves and two refreshment samples. We address identication and estimation issues related to the proposed model under four different types of survey design, featured by whether the missingness is monotone and whether subjects in the refreshment samples are followed up in subsequent waves of the survey. We apply this approach and multiple imputation techniques to the 2007-2008 Associated Press-Yahoo! News Poll (APYN) panel dataset to analyze factors affecting people's political interest. We find that, when attrition bias is not accounted for, the carry-on effects of past political interest on current political interest are underestimated. This highlights the importance of dealing with attrition bias and the potential of refreshment samples for doing so.