The accuracy of eight missing data techniques (MDTs) under conditions of sy
stematically missing data was tested using a Monte Carlo analysis. Data wer
e generated from a population correlation matrix, then deleted using severa
l patterns that might be found in a human resource management (HRM) selecti
on validation study. The results indicated that listwise and pairwise delet
ion were the most accurate methods, followed closely by imputation methods
such as regression and hot-deck. Mean substitution was substantially inferi
or to the other methods tested. Future research that examines different mis
sing data patterns is recommended.