MULTIVARIATE STATISTICAL MODELING OF ACADEMIC, BEHAVIORAL, AND LEARNING FACTORS ASSOCIATED WITH UNIVERSITY STUDENT PERFORMANCE
DOI:
https://doi.org/10.69980/qhadjq59Keywords:
Academic performance, Multivariate analysis, University students, Statistical modeling, Learning behaviorAbstract
Academic performance among university students is influenced by multiple academic, behavioral, demographic, and learning-related factors. Understanding these relationships through multivariate statistical analysis can support more effective identification of factors associated with student achievement. A quantitative, retrospective, secondary-data analytical design was used. Information from 1,194 university students was analyzed. Current cumulative grade point average (CGPA) was considered the primary outcome. Descriptive statistics, independent-samples t-tests, chi-square tests, Pearson or Spearman correlation, and multiple linear regression were applied. Statistical significance was set at p < 0.05. Previous academic performance showed the strongest positive relationship with current CGPA. Attendance was also positively associated with academic achievement. Significant group differences were observed according to scholarship status, learning mode, probation history, and living arrangement. In the multivariable model, previous SGPA, attendance, completed credits, and scholarship status were positive predictors of CGPA, while lower English-language proficiency, health issues, teacher consultation, and increasing age were associated with poorer performance. The regression model explained a substantial proportion of variation in current academic achievement. University student performance is influenced by a combination of academic, behavioral, and background characteristics. Multivariate statistical modeling provides a useful approach for identifying independent predictors and can support targeted academic monitoring and student-support strategies.
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