US Army Post Initial Entry Training First-Term Attrition Analysis: Part 2
Technical Report,01 Jun 2018,30 Sep 2019
The Research and Analysis Center (TRAC)-Monterey Monterey
Pagination or Media Count:
This research continues previous work in Army post Initial Entry Training IET first-term attrition by including medical data with existing personnel data to predict and understand attrition. We use supervised machine learning models to 1 identify the demographic and medical factors of Army enlisted personnel with highest probability of failure to implement preventative measures and 2 estimate total failures during the first enlistment term to set proper recruiting targets. We use classification and survival analysis techniques within the Person-event Data Environment PDE to inform sponsors on attrition trends. We use model results as inputs to an application that displays the predicted probability of success for first term enlistees. The results and application have applicability to other DoD organizations concerned with accession and retention. We find that a soldiers medical history, particularly his Dental Class, PULHES Deployable status, and the duration of the initial contract are significant predictors of whether a soldier will complete his or her first term. Knowledge of the key factors and other influencing variables assists the Army Resiliency Directorate in creation of models to better advise U.S. Army leadership on intervention strategies and preventative measures to preclude the loss of first-term soldiers.
- Personnel Management and Labor Relations
- Military Forces and Organizations