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

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Dec 31, 2002
Accession Number
ADA409042

Entities

People

  • Donna Retzlaff-roberts
  • James Van Scotter
  • Jose H. Dula

Organizations

  • University of Memphis

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Classification
  • Computer Programming
  • Computing-Related Activities
  • Data Science
  • Data Sets
  • Department Of Defense
  • Discriminant Analysis
  • Distortion
  • Information Operations
  • Information Science
  • Interdisciplinary Science
  • Linear Programming
  • Management Personnel
  • Military Research
  • Mississippi
  • Normal Distribution
  • Universities

Readers

  • Computational Modeling and Simulation
  • Regression Analysis.
  • Systems Analysis and Design