Latent profile analysis of obsessive-compulsive symptoms and its relationship with the Big Five Personality Inventory among college students

To identify distinct subtypes of obsessive-compulsive symptoms (OCS) among college students using latent profile analysis (LPA), and to examine the independent associations of demographic characteristics and dimensions of the Big Five Personality Inventory with these OCS subtypes. A cross-sectional study was conducted among 1,107 college students at a university in Zhengzhou City. The survey utilized the Questionnaire Star platform, employing the Big Five Inventory-10 (BFI-10) and the Yale-Brown Obsessive Compulsive Scale (Y-BOCS). LPA was employed to identify potential subtypes of OCS among university students. Multinomial logistic regression analysis was then used to examine the independent associations of demographic characteristics and personality dimensions with the different OCS subtypes. LPA revealed three distinct subtypes of OCS among the university students: the mild OCS subtype accounted for 50.8%, the moderate OCS subtype accounted for 36.7%, and the severe OCS subtype accounted for 12.5%. Significant differences were observed across the different OCS subtypes concerning gender and Internet use duration (P < 0.05). Multiple logistic regression analyses further revealed that gender, daily internet usage duration, extraversion, agreeableness, and neuroticism were significant predictors of the latent profile of OCS (P < 0.05). Specifically, males, longer daily internet usage, milder levels of extraversion and agreeableness, and severe levels of neuroticism were associated with more severe OCS subtypes. This study identified distinct OCS subtypes among college students and several key demographic and personality predictors. These findings provide a theoretical basis for the early identification and personalized intervention for OCS in this population.