Simulation-Based Evaluation of Incident Commander (IC) Competencies: A Multivariate Analysis of Certification Outcomes in South Korea

This study investigates the certification outcomes of intermediate-level ICs in The National Fire Service Academy in South Korea through a comprehensive quantitative analysis of their evaluated competencies. Using assessment data from 141 candidates collected from 2022 to 2024, we examine how scores on six higher-order competencies—comprising 35 sub-competencies—influence pass or fail results. Descriptive statistics, correlation analysis, logistic regression (a statistical model for binary outcomes), random forest modeling (an ensemble decision-tree machine-learning method), and principal component analysis (PCA; a dimensionality reduction technique) were applied to identify significant predictors of certification success. Visualization techniques, including heatmaps, box plots, and importance bar charts, were used to illustrate performance gaps between successful and unsuccessful candidates. Results indicate that competencies related to decision-making under pressure and crisis leadership most strongly correlate with positive outcomes. Furthermore, unsupervised clustering analysis (a data-driven grouping method) revealed distinctive performance patterns among candidates. These findings suggest that current evaluation frameworks effectively differentiate command readiness but also highlight specific skill domains that may require enhanced instructional focus. The study offers practical implications for fire training academies, policymakers, and certification bodies, particularly in refining curriculum design, competency benchmarks, and evaluation criteria to improve fireground leadership training and assessment standards.