Predicting How Well Adolescents Get Along with Peers and Teachers: A Machine Learning Approach.
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 discove...
| Publicado en: | Journal of Youth & Adolescence Vol. 51; no. 7; pp. 1241 - 1257 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Jul2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=158112397&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 158112397 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00472891 JYA jtl: Journal of Youth & Adolescence issn: 00472891 maglogo: N pubinfo: dt: Jul2022 vid: 51 iid: 7 pid: 237 pub: Springer Nature artinfo: ui: 158112397 10.1007/s10964-022-01605-5 ppf: 1241 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.4MB tig: atl: Predicting How Well Adolescents Get Along with Peers and Teachers: A Machine Learning Approach. aug: au: Ali, Farhan Ang, Rebecca P. affil: Learning Sciences and Assessment Academic Group, National Institute of Education, Nanyang Technological University, Singapore, Singapore Psychology and Child & Human Development Academic Group, National Institute of Education, Nanyang Technological University, Singapore, Singapore su: Affinity groups Peer pressure Conflict (Psychology) Interpersonal relations Teenagers' conduct of life Teachers Pathological psychology Machine learning sug: subj: Affinity groups Peer pressure Conflict (Psychology) Interpersonal relations Teenagers' conduct of life Teachers Pathological psychology Machine learning keyword: Adolescents Psychopathology Relationships Youth self-report Adolescents Psychopathology Relationships Youth self-report ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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