TY  - JOUR
T1  - Bayesian Latent Pattern Mixture Models for Handling Attrition in Panel Studies With Refreshment Samples
JF  - ArXiv
Y1  - 2015
A1  - Yajuan Si
A1  - Jerome P. Reiter
A1  - D. Sunshine Hillygus
KW  - Categorical
KW  - Dirichlet pro- cess
KW  - Multiple imputation
KW  - Non-ignorable
KW  - Panel attrition
KW  - Refreshment sample
AB  - 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.
UR  - http://arxiv.org/abs/1509.02124
IS  - 1509.02124
ER  -