Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFarhan Alien
dc.contributor.authorAng, Rebecca P.en
dc.identifier.citationFarhan Ali, & Ang, R. P. (2022). Predicting how well adolescents get along with peers and teachers: A machine learning approach. Journal of Youth and Adolescence. Advance online publication.
dc.identifier.issn0047-2891 (print)-
dc.identifier.issn1573-6601 (online)-
dc.description.abstractHow 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.en
dc.relation.ispartofJournal of Youth and Adolescenceen
dc.titlePredicting how well adolescents get along with peers and teachers: A machine learning approachen
dc.description.versionAccepted versionen
dc.description.projectPG 05/21 FA-
dc.grant.fundingagencyMinistry of Education, Singaporeen
dc.subject.keywordMachine learningen
dc.subject.keywordYouth self-reporten
item.fulltextWith file-
Appears in Collections:Journal Articles
Files in This Item:
File Description SizeFormat 
JYA-2022-016055.pdf8.53 MBAdobe PDFThumbnail
Show simple item record

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.