Van Lissa Caspar J, Beinhauer Lukas, Branje Susan, Meeus Wim H J
Department of Methodology & Statistics, Tilburg University, Tilburg, The Netherlands.
Department of Methodology and Statistics for Psychology, Helmut-Schmidt-Universität, Hamburg, Germany.
J Res Adolesc. 2023 Sep;33(3):870-889. doi: 10.1111/jora.12845. Epub 2023 Mar 20.
As 20% of adolescents develop emotion regulation difficulties, it is important to identify important early predictors thereof. Using the machine learning algorithm SEM-forests, we ranked the importance of (87) candidate variables assessed at age 13 in predicting quadratic latent trajectory models of emotion regulation development from age 14 to 18. Participants were 497 Dutch families. Results indicated that the most important predictors were individual differences (e.g., in personality), aspects of relationship quality and conflict behaviors with parents and peers, and internalizing and externalizing problems. Relatively less important were demographics, bullying, delinquency, substance use, and specific parenting practices-although negative parenting practices ranked higher than positive ones. We discuss implications for theory and interventions, and present an open source risk assessment tool, ERRATA.
由于20%的青少年会出现情绪调节困难，因此识别其重要的早期预测因素很重要。我们使用机器学习算法SEM-森林，对13岁时评估的87个候选变量在预测14至18岁情绪调节发展的二次潜在轨迹模型中的重要性进行了排名。参与者为497个荷兰家庭。结果表明，最重要的预测因素是个体差异（如性格方面）、与父母和同伴的关系质量和冲突行为方面，以及内化和外化问题。相对不太重要的是人口统计学、欺凌、犯罪、物质使用和特定的养育方式——尽管消极的养育方式比积极的养育方式排名更高。我们讨论了对理论和干预措施的影响，并展示了一个开源风险评估工具ERRATA。
