Survival Analysis of US Air Force Officer Retention Rate

Abstract

As part of the effort to ensure proper retention rates for rated officers, retention models are created by the Air Force Personnel division that assist in predicting future retention patterns and accession needs. The techniques for creating these models, known as the sustainment line, involve utilizing average retention percentages obtained from historical data. In this study, more statistical-based methods involving logistic regression analysis and survival analysis are utilized to obtain similar retention models for rated officers. The survival analysis curve produces similar results to the sustainment line, but the sustainment line currently employed is a one-dimensional view of retention patterns. It simply models the rate at which officers leave. The value of the survival curve created in this study is that it can be updated very quickly, is flexible in its construction, and can incorporate covariates into the model that are significant to retention rates. The Air Force has long known that there are external (e.g., economic) factors that impact retention. Using a survival analysis regression model instead of simply modeling the rate at which officers leave, this study was able to identify six demographic and one economic factor that may be significant to rated officer retention.

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Document Details

Document Type
Technical Report
Publication Date
Mar 23, 2017
Accession Number
AD1051631

Entities

People

  • Courtney N. Franzen

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Air Force Personnel
  • Attrition
  • Business Administration
  • Congress
  • Data Mining
  • Databases
  • Demography
  • Employment
  • Engineering
  • Flight Training
  • Governments
  • Human Resources
  • Management Personnel
  • Military Organizations
  • Military Personnel
  • Military Pilots
  • Operations Research
  • Organizational Structure
  • Personnel Management
  • Predictive Modeling
  • Regression Analysis
  • Reliability
  • Statistics
  • Students
  • United States

Readers

  • Computational Modeling and Simulation
  • Military Leadership and Professional Education.
  • Statistical inference.