The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 10, Number 1 (2016), 118-143.
Bayesian latent pattern mixture models for handling attrition in panel studies with refreshment samples
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 nonignorable 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.
Ann. Appl. Stat., Volume 10, Number 1 (2016), 118-143.
Received: December 2014
Revised: August 2015
First available in Project Euclid: 25 March 2016
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Si, Yajuan; Reiter, Jerome P.; Hillygus, D. Sunshine. Bayesian latent pattern mixture models for handling attrition in panel studies with refreshment samples. Ann. Appl. Stat. 10 (2016), no. 1, 118--143. doi:10.1214/15-AOAS876. https://projecteuclid.org/euclid.aoas/1458909910
- Bayesian latent pattern mixture models for handling attrition in panel studies with refreshment samples. The supplement includes the MCMC algorithms for the BLPM and DPMPM models, additional analyses of the APYN data using the DPMPM model and semi-parametric AN model, and details of the BLPM model diagnostics.