Frontiers in psychology
Heterogeneity within high-risk trajectories: a formal latent class growth modeling approach to college student mental health
By Li Y, Xiao G and Xue H.
Abstract
Objective
Latent Class Growth Modeling (LCGM) identifies distinct developmental trajectories but often treats classes as homogeneous. This study examines within-class heterogeneity in Chinese college students' mental health trajectories, focusing specifically on high-risk groups.
Method
We analyzed 2,562 Chinese college students (74.6% female; Mage = 18.5 years) with complete data across three waves over 30 months (Fall 2022, +18 months, +30 months). Mental health was assessed using the SCL-90 Global Severity Index. We employed formal LCGM with expectation-maximization (EM) algorithms for joint parameter and class membership estimation, validated with 1,000 bootstrap resamples.