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Many panel studies collect refreshment samples---new, randomly sampledrespondents who complete the questionnaire at the same time as a subsequentwave of the panel. With appropriate modeling, these samples can be leveraged tocorrect inferences for biases caused by non-ignorable attrition. We presentsuch a model when the panel includes many categorical survey variables. Themodel relies on a Bayesian latent pattern mixture model, in which an indicatorfor attrition and the survey variables are modeled jointly via a latent classmodel. We allow the multinomial probabilities within classes to depend on theattrition indicator, which offers additional flexibility over standardapplications of latent class models. We present results of simulation studiesthat illustrate the benefits of this flexibility. We apply the model to correctattrition bias in an analysis of data from the 2007-2008 Associated Press/YahooNews election panel study.

Bayesian Latent Pattern Mixture Models for Handling Attrition in Panel Studies With Refreshment Samples