Exploring Predictors of Bullying Perpetration Among Adolescents Using Machine Learning Approach.

This study used machine learning methods to detect risk and protective factors for bullying perpetration in adolescents. The study sample consisted of 777 students with an age range of 11 to 16 years old. Multidimensional data covering both individual and environmental levels were collected. Individ...

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Detalles Bibliográficos
Publicado en:	Journal of Interpersonal Violence Vol. 41; no. 11/12; pp. 3077 - 3102
Autores principales:	Zhou, Huiling, Zheng, Qubo, Jiang, Huaibin, Lu, Jiamei
Formato:	Artículo
Publicado:	Sage Publications Inc. Jun2026
Materias:	China Bullying prevention Psychology of middle school students Parent-child relationships Affinity groups Self-control Ethics Bullying Psychological disengagement Adolescence Random forest algorithms Risk assessment Prediction models Receiver operating characteristic curves Research funding Logistic regression analysis Questionnaires Research evaluation Surveys Machine learning Data analysis software Accuracy Algorithms
Acceso en línea:	Ver este registro en EBSCOhost