Journal
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume 52, Issue 7, Pages 2899-2923Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2021.1921799
Keywords
Bayesian; Gibbs sampling; Missing data; Multilevel modeling; Multiple imputation
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This study provides a coherent approach to Bayesian analysis of multilevel models in the presence of missing data, covering the main aspects of the models and missingness.
Missing data are a common occurrence in analyses of multivariate data, including in multilevel modeling. Bayesian approaches to handling missing data in multilevel modeling have garnered increasing attention, either on their own or in service of multiple imputation. However, these applications are largely confined to specific models or missingness patterns. The current work provides a coherent account of Bayesian analysis of multilevel models in the presence of missing data on the outcomes, level-1 predictors, and level-2 predictors, that covers the main aspects of the models and missingness. In doing so, this work provides a grounding for estimation in fully Bayesian approaches that employ Gibbs sampling, and provides an account of how to generate the imputations in the first phase of a multiple imputation approach.
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