Relating character to conduct violations at US service academies
Dustin Wood1, Leonard M Giambra2, Kevin Mullaney3
1Leadership Ethics and Law Department, United States Naval Academy, Annapolis, Maryland.
Summary
Character traits predict misconduct in military cadets. Specific Values in Action (VIA) scales like Self-Regulation and Prudence correlated with fewer offenses, while Social Intelligence and Creativity predicted more, highlighting key character development areas.
Area of Science:
- Psychology
- Military Leadership Studies
- Character Development
Background:
- Military service academies prioritize developing "leaders of character."
- Institutional systems identify and penalize misconduct to uphold this mission.
- Understanding character attributes is crucial for predicting and mitigating cadet misconduct.
Purpose of the Study:
- To investigate how the Values in Action (VIA) instrument predicts conduct offenses among cadets.
- To identify specific character dimensions associated with misconduct at service academies.
- To assess the predictive power of individual VIA items versus broader scales.
Main Methods:
- Utilized an early version of the Values in Action (VIA) instrument administered to cadets.
- Collected data on cumulative conduct offenses recorded during academy attendance.
- Employed correlational analysis to link VIA dimensions and individual items with misconduct records.
Main Results:
- Self-Regulation, Modesty/Humility, and Prudence scales predicted fewer conduct offenses.
- Social Intelligence, Originality/Creativity, and Humor/Playfulness scales predicted more conduct offenses.
- Individual VIA items offered significantly improved prediction of conduct offenses compared to broader scales.
Conclusions:
- A consistent profile of character attributes predicts misconduct across military service academies.
- Specific character strengths and weaknesses can be identified as risk factors for misconduct.
- Findings may inform character development programs and misconduct prevention strategies in similar contexts.
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