Developing New Test Selection and Weight Stabilization Techniques for Designing Classification Efficient Composites.

Abstract

The major goal of this research was to specify a classification-efficient methodology for the construction of assignment composites of optimally selected and weighted tests drawn from a single battery of ASVAB and experimental tests and targeting a job family. The experiments examine the effects of the number of tests included in a composite, using different figures of merit as the standard for the selection of tests for components and stabilizing test regression weights. The research approach adopted involves a simulation of the Army selection and classification process using Project A validity data. Comparisons of classification efficiency obtained under each experimental condition are reported in terms of mean predicted performance (MPP). Findings indicate that five-test composites, tailored to operational job families and selected by a predictive validity index to provide positive weights, can provide an acceptable approximation of the maximum obtainable MPP. (MM)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1995
Accession Number
ADA298740

Entities

People

  • Cecil D. Johnson
  • Dolores Scholarios
  • Joseph Zeldner

Organizations

  • George Washington University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Army Personnel
  • Classification
  • Construction
  • Data Science
  • Databases
  • Efficiency
  • Employment
  • Experimental Design
  • Information Science
  • Personnel Management
  • Personnel Selection
  • Psychology
  • Simulations
  • Social Sciences
  • Test And Evaluation

Readers

  • Computational Modeling and Simulation
  • Psychometric Testing or Psychological Assessment.