Systematic data loss in HRM settings: A Monte Carlo analysis

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.