Correcting bias in extreme groups design using a missing data approach

Extreme groups design (EGD) refers to the use of a screening variable to inform further data collection, such that only participants with the lowest and highest scores are recruited in subsequent stages of the study. It is an effective way to improve the power of a study under a limited budget, but produces biased standardized estimates. We demonstrate that the bias in EGD results from its inherent missing at random mechanism, which can be corrected using modern missing data techniques such as full information maximum likelihood. Further, we provide a tutorial on computing correlations in EGD data with FIML using R.