@article {1866, title = {Accounting for nonignorable unit nonresponse and attrition in panel studies with refreshment samples}, journal = {Journal of Survey Statistics and Methodology}, volume = {3}, year = {2015}, pages = {265-295}, chapter = {265}, abstract = { Panel surveys typically su↵er from attrition, which can lead to biased inference when basing analysis only on cases that complete all waves of the panel. Unfortunately, panel data alone cannot inform the extent of the bias from the attrition, so that analysts using the panel data alone must make strong and untestable assumptions about the missing data mechanism. Many panel studies also include refreshment samples, which are data collected from a random sample of new individuals during some later wave of the panel. Refreshment samples o↵er information that can be utilized to correct for biases induced by nonignorable attrition while reducing reliance on strong assumptions about the attrition process. To date, these bias correction methods have not dealt with two key practical issues in panel studies: unit nonresponse in the initial wave of the panel and in the refreshment sample itself. As we illustrate, nonignorable unit nonresponse can significantly compromise the analyst{\textquoteright}s ability to use the refreshment samples for attrition bias correction. Thus, it is crucial for analysts to assess how sensitive their inferences{\textemdash}corrected for panel attrition{\textemdash}are to di↵erent assumptions about the nature of the unit nonresponse. We present an approach that facilitates such sensitivity analyses, both for suspected nonignorable unit nonresponse in the initial wave and in the refreshment sample. We illustrate the approach using simulation studies and an analysis of data from the 2007-2008 Associated Press/Yahoo News election panel study. }, doi = {10.1093/jssam/smv007}, url = {http://jssam.oxfordjournals.org/content/3/3/265.abstract}, author = {Schifeling, T. and Cheng, C. and Hillygus, D. S. and Reiter, J. P.} } @article {http://arxiv.org/abs/1508.03758, title = {Nonparametric Bayesian models with focused clustering for mixed ordinal and nominal data}, journal = {ArXiV}, year = {2015}, publisher = {arXiv}, abstract = {Dirichlet process mixtures can be useful models of multivariate categorical data and effective tools for multiple imputation of missing categorical values. In some contexts, however, these models can fit certain variables well at the expense of others in ways beyond the analyst{\textquoteright}s control. For example, when the data include some variables with non-trivial amounts of missing values, the mixture model may fit the marginal distributions of the nearly and fully complete variables at the expense of the variables with high fractions of missing data. Motivated by this setting, we present a Dirichlet process mixture model for mixed ordinal and nominal data that allows analysts to split variables into two groups: focus variables and remainder variables. The model uses three sets of clusters, one set for ordinal focus variables, one for nominal focus variables, and one for all remainder variables. The model uses a multivariate ordered probit specification for the ordinal variables and independent multinomial kernels for the nominal variables. The three sets of clusters are linked using an infinite tensor factorization prior, as well as via dependence of the means of the latent continuous focus variables on the remainder variables. This effectively specifies a rich, complex model for the focus variables and a simpler model for remainder variables, yet still potentially captures associations among the variables. In the multiple imputation context, focus variables include key variables with high rates of missing values, and remainder variables include variables without much missing data. Using simulations, we illustrate advantages and limitations of using focused clustering compared to mixture models that do not distinguish variables. We apply the model to handle missing values in an analysis of the 2012 American National Election Study.}, url = {http://arxiv.org/abs/1508.03758}, author = {DeYoreo, Maria and Reiter , J. P. and Hillygus, D. S.} }