A Methodology for Long-Term Forecasts of Air Force Pilot Retention Rates: A Management Perspective

Abstract

Personnel planners in various Air Force agencies use models, among other things, to aid them in forecasting pilot retention rates. This Theses attempted to forecast retention rates three years ahead with the use of multiple regression analysis techniques. Such models can be of use of Air Force leaders to develop proactive policies and programs to combat poor retention forecasts. Economically quantifiable variables were primarily used in the modeling effort. However, some year groups could not be adequately explained with the use of economic variables alone. The models for year groups eight, twelve, and thirteen used the retention rates of 'peer groups' to assist in explaining their own retention rates. All models were subjected to common internal tests associated with linear regression. External validity was verified by the use of a withheld data set. Forecasts were made for Fiscal Years 90, 91, and 92, using independent variable data from 1987, 1988, and 1989, respectively. All tests and forecasts were thoroughly documented. The practical and policy implications of these forecasts were discussed, and some thoughts about possible policies and programs to increase retention were advanced. Improvements to further the utility of these models were suggested.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1990
Accession Number
ADA229541

Entities

People

  • Bruce A. Guzowski

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Biomedical
  • Materials and Manufacturing Processes
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Personnel
  • Business Administration
  • Data Science
  • Data Sets
  • Databases
  • Economic Analysis
  • Employment
  • Group Processes (Social Psychology)
  • Information Science
  • Management Personnel
  • Military Personnel
  • Military Pilots
  • Peer Groups
  • Personnel Management
  • Regression Analysis
  • Warfare

Fields of Study

  • Environmental science

Readers

  • Computational Modeling and Simulation
  • Naval Personnel Management