Modeling Non-Ignorable Missing Data with Bayesian Rasch Models

Non-ignorable missingness is a challenge when conducting item response theory analyses. Selection models which account for the missing data process are promising tools to addressing missingness. In this simulation study, we evaluate a Bayesian selection modeling approach to non-ignorable missingness analysis in Rasch models, and discuss its benefits and limitations.