Comparing Binary and Ordered Logistic Regression in Studies of Education and Learner Behavior

Abstract

This study compares the effectiveness of binary logistic regression and ordered logistic regression in analyzing office workers’ readiness to pursue postgraduate education. A simulated survey dataset generated by ChatGPT includes 400 observations encompassing motivational, barrier, and support factors. Two models were applied: binary logistic regression (combining levels 4–5 as "ready" and levels 1–3 as "not ready") and ordered logistic regression (retaining all five ordinal levels). The ordered model identified a significant negative effect of family- related barriers on readiness (β = -0.468, OR = 0.63, p = 0.021), which was not detected in the binary model. Both models showed low overall fit (Pseudo R2 < 3%). The findings suggest that ordered logistic regression better utilizes ordinal information, while binary regression offers a simpler, more intuitive approach. The study recommends field surveys to validate these results.