A Hybrid Analysis Using Adaptive and Self-Adjusting Boosting and Logistic Regression

Abstract: This study investigates factors influencing the employment outcomes of sports science graduates, specifically their ability to secure decent jobs. Utilizing data from the Graduates Occupational Mobility Survey (GOMS) from 2015 to 2019, the study analyzed a sample of 1,019 sports science graduates aged 19 to 34. Both traditional statistical methods and advanced machine learning techniques, including Adaptive & Self-Adjusting Boosting and logistic regression analysis, were employed to identify significant predictors and assess their impact. Key variables examined included gender, job-related courses, corporate recruitment briefings, parental education, TOEIC scores, and employment goals set before graduation. Logistic regression analysis revealed several significant predictors of decent job employment. Male graduates had significantly higher odds of securing decent jobs compared to female graduates (OR=1.45, 95% CI: 1.10-1.90, p=0.02). The number of job-related courses taken (OR=1.30, 95% CI: 1.05-1.60, p=0.04) and participation in corporate recruitment briefings (OR=1.25, 95% CI: 1.02-1.53, p=0.03) were positively associated with decent job employment. Parental education (OR=1.15, 95% CI: 1.01-1.30, p=0.05) and TOEIC scores (OR=1.10, 95% CI: 1.00-1.22, p=0.06) also showed modest but significant effects. Setting employment goals before graduation significantly increased the odds of securing decent jobs (OR=1.20, 95% CI: 1.05-1.37, p=0.03). The study highlights critical factors influencing the employment outcomes of sports science graduates, with gender disparities evident as male graduates had better employment prospects. Findings emphasize the importance of job-related education, corporate engagement, and proactive career planning. Universities should enhance these aspects to improve employability, and targeted interventions are needed to support female graduates in achieving comparable outcomes. The integration of traditional statistical methods and machine learning techniques provided a comprehensive analysis framework, offering valuable insights for policymakers, educators, and employers.

Haewon Byeon. “A Hybrid Analysis Using Adaptive and Self-Adjusting Boosting and Logistic Regression”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.3 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170349

@article{Byeon2026,
title = {A Hybrid Analysis Using Adaptive and Self-Adjusting Boosting and Logistic Regression},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170349},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170349},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {3},
author = {Haewon Byeon}
}