A Monte Carlo Analysis of Missing Data Techniques in a HRM Setting

摘要: Researchers have examined various techniques to solve the problem of missing data. Simple included listwise deletion, pairwise mean substitution, regression imputation and hot-deck imputation. Past research suggests that deletion generally result in less dispersion around true score values while results more scores. Unfortunately, this spent much time examining whether lead overestimation or underestimation statistics. The present study utilized a Monte Carlo Analysis simulate an HRM setting evaluate data techniques. Pairwise resulted least scores average error any technique for calculating correlations. Implications use these future were explored.