Binary Logistic Regression Modeling for Predicting Potential Offenders of Discipline Rules in an Integrated School

Two binary logistic regression models (Model C and Model D) were selected from four alternative models developed to determine if a combination of gender, religion, and grade level could significantly predict potential violators of school discipline rules. Grades 7 to 12 students enrolled during the Academic Year 2022-2023 in a private Integrated School in Quezon City, Philippines composed the target population of the study. The 753 sample cases used was calculated using the formula suggested by Peduzzi et al. (1996). Assumptions on multicollinearity and outliers were satisfied. Simultaneous entry of all independent variables and classification cutoff value of .5 were used. Models C and D surpassed the other two alternative Models (A and B) in terms of Goodness-of-Fit tests (Chi-Square Omnibus test, Cox & Snell R2, Nagelkerke R2, and Hosmer-Lemeshow test) and Percentage Overall Predictive Accuracy (72.5%), as compared to 65.40% and 69.30% for Model A and Model B, respectively. Both Models C and D showed 70.0% Sensitivity, 74.8% Specificity, 71.8% Positive Predictive Value, and 73.1% Negative Predictive Value. Although Models C and D addressed different research questions, both revealed that Gender and Grade Level were highly significant predictors (in a positive or negative way) of violators (p < .001), but not Religion. Concerning Grade Level, Model C showed that Grade 7, Grade 11, and Grade 12 students had significant odds or likelihood of committing violations. Model D revealed that Grades 7, 8, 10, 11, and 12 students had significant odds (positive or negative effect) of committing offenses. Both Models did not find Grade 9 students as likely violators.