Testing for MAR can be challenging because it involves assumptions about the missing data. Researchers often use statistical tests and models, such as Likelihood-based methods or Multiple imputation, to evaluate the plausibility of the MAR assumption. These methods rely on the observed data to make inferences about the missing data.
