A Mixed Methods Study of Ranger Attrition: Examining the Relationship of Candidate Attitudes, Attributions and Goals

Abstract

Elite military selection programs like the 75th Ranger Regiment's Ranger Assessment and Selection Program (RASP) are known for their difficulty and high attrition rates, despite substantial candidate screening just to get into such programs. The current study analyzes Ranger candidates 'attitudes, attributions, and goals (AAGs) and their relationship with successful completion of pre-RASP, a preparation phase for the demanding eight-week RASP program. Candidates' entry and exit surveys were analyzed using natural language processing (NLP), as well as more traditional statistical analyses of Likert-measured survey items to determine what reasons for joining and what individual goals related to candidate success. Candidates' Intrinsic Motivations and Satisfaction as measured on entry surveys were the strongest predictors of success. Specifically, candidates' desire to deploy or serve in combat, and the goal of earning credibility in the Rangers were the most important reasons and goals provided through candidates' open-text responses. Additionally, between-groups analyses between Black Candidates, Hispanic Candidates, and White Candidates showed that differences in candidate abilities and motivations better explains pre-RASP attrition than demographic proxies such as race or ethnicity. The study's use of NLP demonstrates the practical utility of applying machine learning to quantitatively analyze open-text responses that have traditionally been limited to qualitative analysis or subject to human coding, although predictive models utilizing more traditional Likert-measurement of AAGs had better predictive accuracy.