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Updated: Jul 6, 2025

05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.1K
Predicting ranger attrition.
Aaron K Coombs1, Neil M A Hauenstein2
1Department of Command, Leadership, and Management, United States Army War College, Carlisle, Pennsylvania.
Summary
Physical and personality traits strongly predict success in elite military training like the Ranger Assessment and Selection Program (RASP). Understanding these factors can improve candidate selection and reduce high attrition rates.
Area of Science:
- Military psychology
- Personnel selection
- Performance prediction
Background:
- Elite military programs face high attrition rates, exceeding 50% in some cases.
- Effective screening of candidates is crucial for successful program matriculation.
- Existing selection methods may not fully capture predictors of success.
Purpose of the Study:
- To develop and validate predictive models for attrition in the Ranger Assessment and Selection Program (RASP).
- To identify the relative importance of physical abilities, cognitive abilities, and personality scores in predicting candidate success.
- To explore the utility of a composite probability score for admissions decisions.
Main Methods:
- Development and cross-validation of regression models.
- Utilized candidate admissions screening data including physical, cognitive, and personality scores.
- Analysis of predictors across three distinct program timeframes.
Main Results:
- Physical abilities scores were the strongest predictors of RASP attrition, despite their use in initial selection.
- Personality scores explained more variance in candidate success than cognitive ability scores.
- Openness dimensions within personality were particularly predictive of early (week one) attrition.
Conclusions:
- Physical abilities remain critical predictors of attrition, necessitating careful consideration in selection.
- Personality assessment offers valuable insights into predicting candidate success, especially early in training.
- Integrating diverse predictor data can enhance the accuracy of candidate selection for demanding military programs.
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