PREDICTING MIDSHIPMEN'S OUTCOMES AT THE UNITED STATES NAVAL ACADEMY

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Authors
Jamison, George R.
Subjects
United States Naval Academy
USNA
MBTI
academic grades
physical fitness
non-cognitive
academic order of merit
SAT
ACT
extracurricular activities
attrition
attrite
logistic regression
classification trees
random forests
Advisors
Buttrey, Samuel E.
Date of Issue
2021-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
This research examines, describes, and analyzes factors that are associated with midshipmen’s outcomes at the United States Naval Academy. Specifically, we identify factors that help predict which midshipmen will graduate in the top 10%, bottom 10%, or undergo attrition. The goal is to identify a list of factors which company officers and senior enlisted leaders can use to help develop midshipmen morally, mentally, and physically. We used logistic regression, classification trees, and random forests to seek the most effective prediction model for midshipmen’s outcomes. The results of our logistic regression model accurately identify 71.4% of midshipman who are predicted to graduate in the top 10%, and 66.7% of midshipmen who are predicted to graduate in the bottom 10%. Additionally, whole person multiple, math SAT scores, participation in extracurricular activities, Myers-Briggs Type Indicator results, and mile times are key factors for predicting the top 10%. For the bottom 10%, the key factors are whole person multiple, math SAT scores, race/ethnicity, and prior enlistment. Due to a lack of specific attrition data, attrition models were unsuccessful. This study summarizes results, makes recommendations to the United States Naval Academy, and lists potential future work for Naval Postgraduate students.
Type
Thesis
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Series/Report No
Department
Operations Research (OR)
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NPS Report Number
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Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.