Predicting positive youth development among Chinese adolescents: A machine learning approach using multiwave longitudinal data.
TL;DR
Machine learning is applied to four-wave longitudinal data from 5019 Chinese adolescents to identify the key predictors of PYD at T4 (controlling for prior PYD at T3), measured by the Chinese 4Cs model (Character, Competence, Confidence, Connection).
Abstract
Despite growing recognition that positive youth development (PYD) depends on the dynamic interaction of individual and ecological resources, existing studies rely on linear models that cannot capture high-dimensional, nonlinear predictor configurations. This study applied machine learning to four-wave longitudinal data from 5019 Chinese adolescents (ages 9-19) to identify the key predictors of PYD at T4 (controlling for prior PYD at T3), measured by the Chinese 4Cs model (Character, Competence, Confidence, Connection). We compared 12 algorithms; CatBoost achieved the best prediction ( R 2 = .816). SHAP analysis identified school psychological climate, depression, and parental loneliness as the top three predictors. Heterogeneity analyses revealed an age gradient: School climate dominated for primary and middle school students, whereas parental loneliness dominated for high school students. Student type analyses uncovered three distinct developmental pathways: an aspirational pathway characterized by social mobility belief for migrant children, a relational pathway characterized by parental loneliness for left-behind and urban children, and a clinical pathway characterized by depression for rural ordinary children. These findings provide empirical support for differentiated, context-sensitive intervention strategies targeting PYD across diverse Chinese adolescent populations.