  Many panel studies collect refreshment samples---new, randomly sampled
respondents who complete the questionnaire at the same time as a subsequent
wave of the panel. With appropriate modeling, these samples can be leveraged to
correct inferences for biases caused by non-ignorable attrition. We present
such a model when the panel includes many categorical survey variables. The
model relies on a Bayesian latent pattern mixture model, in which an indicator
for attrition and the survey variables are modeled jointly via a latent class
model. We allow the multinomial probabilities within classes to depend on the
attrition indicator, which offers additional flexibility over standard
applications of latent class models. We present results of simulation studies
that illustrate the benefits of this flexibility. We apply the model to correct
attrition bias in an analysis of data from the 2007-2008 Associated Press/Yahoo
News election panel study.
