Missing data is a common feature of experimental datasets. Standard methods used by psychology researchers to handle missingness rely on unrealistic assumptions, invalidate random assignment procedures, and bias estimates of effect sizes. We describe different classes of missing data typically encountered in experimental datasets, and we discuss how each of them impacts researchers’ causal inferences. In this tutorial, we provide concrete guidelines for handling each class of missingness, focusing on 2 methods that make realistic assumptions: (a) inverse probability weighting (IPW) for mild instances of missingness, and (b) double sampling and bounds for severe instances of missingness.
All Science Journal Classification (ASJC) codes
- Psychology (miscellaneous)
- Double sampling and bounds
- Inverse probability weighting
- Missing data