Cross-sectional survey identifies personality subgroups in university students, revealing differing mental health outcomes and network effects.
Key Points
This study aims to identify personality subgroups based on Dark and Light Triad traits and to examine the impact of these traits on depression and anxiety symptoms.
Cross-sectional survey design with a sample of 2228 university students.
Utilized Light Triad Scale, Dirty Dozen, PHQ, and GAD scales for assessment.
Employed latent profile analysis and Ising network models with NodeIdentifyR algorithm for data analysis.
Three personality subgroups identified: high DT (9.25%), high LT (52.51%), and medium traits (38.24%).
High DT subgroup had higher depression and anxiety scores compared to others (p < .001).
High LT subgroup demonstrated the largest projected network changes, with a 45% decrease (node D4) and 67% increase (node A4).