Systematic Data Loss in HRM Settings: A Monte Carlo Analysis

The accuracy of eight missing data techniques under conditions of systematically missing data was tested using a Monte Carlo analysis and indicated that listwise and pairwise deletion were the most accurate methods, followed closely by imputation methods such as regression and hot-deck.

Abstract

The accuracy of eight missing data techniques (MDTs) under conditions of systematically missing data was tested using a Monte Carlo analysis. Data were generated from a population correlation matrix, then deleted using several patterns that might be found in a human resource management (HRM) selection validation study. The results indicated that listwise and pairwise deletion were the most accurate methods, followed closely by imputation methods such as regression and hot-deck. Mean substitution was substantially inferior to the other methods tested. Future research that examines different missing data patterns is recommended.