Success Predictors for Students to Attend AFIT Department of Systems Management Master's Degree Programs.

Abstract

The author investigated the prediction of academic success among systems management and operations research master's degree students. He analyzed a population of 223 male USAF officers who attended AFIT from 1971 through 1976. Graduate grade point average (GGPA) prediction was investigated using multivariate regression analysis, and success based on degree receipt or nonreceipt was examined through use of the Automatic Interaction Detection algorithm and discriminant analysis. The predictor variables included ability and biographical data plus several surrogate measures of motivation toward degree achievement. The results of the GGPA study confirmed previous findings that students in different disciplines should not be combined for GGPA predictive purposes, and also indicated that the GGPA predictive power attainable is modest; additionally, use of GGPA as a criterion of academic success is of questionable value. Most significant was the finding that students selection criteria truncate from the population those persons who lack the ability to achieve a degree, and that motivational measures were the best predictors of degree receipt.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1977
Accession Number
ADA046110

Entities

People

  • Robert W. Keith

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Data Mining
  • Data Science
  • Discriminant Analysis
  • Information Science
  • Knowledge Management
  • Mathematics
  • Measurement
  • Military Personnel
  • New York
  • Operations Research
  • Regression Analysis
  • Social Sciences
  • Statistical Algorithms
  • Students
  • Systems Engineering
  • United States

Fields of Study

  • Education

Readers

  • Psychometric Testing or Psychological Assessment.
  • Regression Analysis.
  • STEM Education