Discriminating between Graduates and Failure in the USAF Medical Laboratory Specialist School: An Explorative Approach.

Abstract

The purpose of this study was to identify predictors of success in the USAF Medical Service Specialist school and to explore those characteristics that best differentiate Failures and Graduates. Composite scores from the Armed Services Vocational Aptitude Battery (ASVAB Form 6 and 7), a course-developed mathematics pretest score, a general intelligence score, and student demographics were used as predictors of the dicotomous criterion for 784 enlisted personnel entered into this occupational speciality. Group mean differences, correlation analysis, and the development of a linear discriminant function (LDF) were accomplished to determine those variables that best differentiated the two groups. Results of these analyses indicate that the most powerful predictor of graduation and discrimination between Graduates and Failures was the course-developed mathematics pretest. General intelligence, electrical aptitude, and age appear to offer additional predictive information. Distributions of the standardized discriminant scores in reduced-space appear to indicate a significant deviation from a normal distribution for the Failure population based on the variables studied.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1981
Accession Number
ADA116775

Entities

People

  • Mark D. Williams

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Administrative Personnel
  • Air Force
  • Data Science
  • Employment
  • Enlisted Personnel
  • Health Care
  • Health Services
  • Information Science
  • Knowledge Management
  • Management Personnel
  • Medical Personnel
  • Personnel Management
  • Psychological Tests
  • Reasoning
  • Statistical Algorithms
  • Students
  • Surveys

Fields of Study

  • Education

Readers

  • Instructional Design and Training Evaluation.
  • Naval Personnel Management
  • Regression Analysis.

Technology Areas

  • Space