Classification and Selection for Personnel Applications Using a Data Envelopment Analysis Approach

摘要

This project develops and tests a new approach for improving the effectiveness of personnel selection and classification decisions There are three main data characteristics that cause difficulty for existing classification methods: (1) unbalanced group sizes, (2) unequal misclassification costs, and (3) non-normal data Preliminary research suggests a hybrid method incorporating data envelopment analysis (DEA) and linear programming discriminant analysis (DEA/DA) is effective in this difficult situation and outperforms other methods Research is conducted to fully develop and test this promising methodology. Results suggest ways DEA/DA may help alleviate long-standing selection and classification problems.