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Catalogue Information
Field name	Details
Nuova numerazione	83229
Collocazione	UPS BIBL CENTR 39-B-921
Autore	ALI, F.
Titolo	Predicting how well adolescents get along with peers and teachers: a machine learning approach Parte componente di periodico
Descrizione fisica	pp. 1241-1256.
Nota generale	Estratto da: Journal of Youth and Adolescence 2022, 51, 7.
Riassunto	How well adolescents get along with others such as peers and teachers is an important aspect of adolescent development. Current research on adolescent relationship with peers and teachers is limited by classical methods that lack explicit test of predictive performance and cannot efficiently discover complex associations with potential non-linearity and higher-order interactions among a large set of predictors. Here, a transparently reported machine learning approach is utilized to overcome these limitations in concurrently predicting how well adolescents perceive themselves to get along with peers and teachers. The predictors were 99 items from four instruments examining internalizing and externalizing psychopathology, sensation-seeking, peer pressure, and parent-child conflict. The sample consisted of 3232 adolescents (M = 14.0 years, SD = 1.0 year, 49% female). Nonlinear machine learning classifiers predicted with high performance adolescent relationship with peers and teachers unlike classical methods. Using model explainability analyses at the item level, results identified influential predictors related to somatic complaints and attention problems that interacted in nonlinear ways with internalizing behaviors. In many cases, these intrapersonal predictors outcompeted in predictive power many interpersonal predictors. Overall, the results suggest the need to cast a much wider net of variables for understanding and predicting adolescent relationships, and highlight the power of a data-driven machine learning approach with implications on a predictive science of adolescence research.
Tipo di documento	RICERCA.
Soggetto	ADOLESCENTI.
	APPRENDIMENTO.
	PSICOPATOLOGIA.
	RELAZIONI.
	AUTOVALUTAZIONE.
	SINGAPORE.
	PEER-GROUP.
Ambito	Psicologico
Ambito	Sociologico
Autore Secondario	ANG, R.P.
Titolo correlato	Journal of Youth and Adolescence 2022, 51, 7.
Accesso online	Accesso diretto all'articolo

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Predicting how well adolescents get along with peers and teachers: a machine learning approach