Performance of Predictive Mean Matching When Sample Size Is Small and Distribution Assumptions Have Been Violated

Predictive Mean Matching (PMM) is a method of imputing missing data that is known to be robust under minor violations (e.g., skewed distributions) and small sample sizes. This study examined the performance of PMM with varied proportions of missingness across levels of sample size, correlation, heteroscedasticity, and normality.