Using machine learning to identify early predictors of adolescent emotion regulation development |
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Authors: | Caspar J Van Lissa Lukas Beinhauer Susan Branje Wim H J Meeus |
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Institution: | 1. Department of Methodology & Statistics, Tilburg University, Tilburg, The Netherlands;2. Department of Methodology and Statistics for Psychology, Helmut-Schmidt-Universität, Hamburg, Germany;3. Department of Youth and Family, Utrecht University, Utrecht, The Netherlands |
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Abstract: | 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. |
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Keywords: | adolescence emotion regulation machine learning random forests theory formation |
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