Improving USMC Retention Quality Through Reenlistment Pre-Approval

Abstract

Improving the quality of Marines retained has long been an objective of the Marine Corps mission. This study assesses the effectiveness of utilizing a recently proposed binary logistic regression to select the most qualified Marines, based on their performance data, for pre-approved retention. Currently, all Marines desiring retention must submit a Reenlistment, Extension, and Lateral Move (RELM) request and await the Marine Corps approval or rejection decision. Implementing a targeted reenlistment pre-approval process could improve the quality of retention in the Marine Corps. To target the highest quality Marines, this study looks at the quality of Marines selected for pre-approved retention in relation to the overall First-term Alignment Plan (FTAP) retention goal and examines the effectiveness of pre-approval selection at identifying improved subsequent term performance for those Marines who have already been retained. This study also analyzes the potential impact of pre-approved retention on the availability of boat-spaces and the number of reenlistment requests submitted. The results suggest that by targeting the highest quality (Tier-I) Marines, improved quality retention can be obtained without exceeding FTAP retention goals. Additionally, the results indicate the proposed pre-approval model effectively predicts quality performance in a Marines subsequent term as indicated by tier calculation performance variables.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2021
Accession Number
AD1150711

Entities

People

  • Nicholas Norville

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Active Duty
  • Availability
  • Business Administration
  • Court Martial
  • Data Sets
  • Department Of Defense
  • Enlisted Personnel
  • Governments
  • Instructors
  • Law
  • Marine Corps
  • Military Personnel
  • National Security
  • Personnel Management
  • Predictive Modeling
  • Recruiting
  • Reenlistment
  • Schools
  • Statistical Analysis
  • Statistics
  • United States

Readers

  • Maritime Security/Maritime Homeland Security
  • Naval Personnel Management

Technology Areas

  • Space