A Methodology for the Analysis and Prediction of Air Force Enlisted Aircraft Maintenance Personnel Retention Rates Using Economic Statistics

Abstract

The purpose of this study is to investigate the effects of certain national economic indicators on enlisted aircraft maintenance personnel retention rates. To fulfill this purpose, this study builds a linear regression equation that can predict the retention rate using the civilian unemployment rate and the index of 11 leading economic indicators as the independent variables. Journal literature and economic texts were reviewed to identify the indicators as good predictors for changes in employee turnover. The method of linear regression was used to build the model needed to predict the changes in the retention rate. The equation developed with this technique in the present research may help the Air Force Personnel Center predict future enlisted retention rate changes. AFPC's task of keeping a steady force has a central need of knowing the number of personnel that will be staying and leaving the force. The results of this study show a strong relationship between the future retention rate of the enlisted aircraft maintenance force and the unemployment rate and the index of 11 leading indicators.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1999
Accession Number
ADA369695

Entities

People

  • Peter D. Lommen

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Administrative Personnel
  • Air Force
  • Air Force Personnel
  • Aircraft Maintenance
  • Aircrafts
  • Business Administration
  • Employment
  • Enlisted Personnel
  • Maintenance
  • Maintenance Personnel
  • Management Personnel
  • Organizational Structure
  • Personnel Management
  • Personnel Retention
  • Regression Analysis
  • Statistical Analysis
  • Statistics

Fields of Study

  • Business

Readers

  • Aerospace logistics and air mobility.
  • Computational Modeling and Simulation
  • Naval Personnel Management