Systematic data loss in HRM settings: A Monte Carlo analysis
通过蒙特卡洛模拟，测试了八种缺失数据处理技术在人力资源管理选拔验证研究中的准确性，发现列表删除和成对删除最准确，均值替代效果最差。
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.