Latent Class Analysis of University Departments and Employment Rate Comparison in Gyeongbuk Province

Objectives This study classifies university departments nationwide using Latent Class Analysis (LCA) and examines employment rate disparities in Gyeongbuk Province to inform policies for local universities. Methods For this objectives, we collected and refined data from the 2023 academic year graduates listed in the Ministry of Education’s “University Information” disclosures (covering 14,443 departments nationwide) and employed the categorical variables for Latent Class Analysis (LCA). After comparing model fit indices (AIC, BIC, etc.) to determine the optimal number of classes, we analyzed the size and regional distribution of departments within each class to identify the unique characteristics of those located in Gyeongbuk Province. In addition, an ANOVA and post-hoc tests were performed to examine differences in employment rates across the classes, thereby offering a detailed view of how the employment outcomes of Gyeongbuk-based departments compare with those in other regions. Results Five latent groups were identified, with Gyeongbuk departments concentrated in Class 5 (national universities). Employment rates were higher in Class 1 (private universities and vocational colleges) and Class 3 (cyber universities) but lower in Class 4 (vocational colleges offering evening courses) and Class 5. Conclusions The findings highlight the need for customized employment policies, stronger local industry collaboration, and industry-academia linked curricula to improve Gyeongbuk universities' competitiveness.