A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data Case

Explaining what leads to higher or lower levels of subjective well-being (SWB) in childhood and adolescence is one of the cornerstones within this field of studies, since it can lead to the development of more focused preventive and promotion actions. Although many indicators of SWB have been identi...

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Autores: González Carrasco, Mònica, Fabregat, Ramon, Aciar, Silvana Vanesa, Casas Aznar, Ferran, Oriol Granado, Xavier, Fabregat Gesa, Ramon, Malo Cerrato, Sara
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/25333
Acceso en línea:http://hdl.handle.net/10256/25333
Access Level:acceso abierto
Palabra clave:Aprenentatge automàtic
Machine learning
Adolescència
Adolescence
Infants
Children
Benestar
Well-being
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spelling A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data CaseGonzález Carrasco, MònicaFabregat, RamonAciar, Silvana VanesaCasas Aznar, FerranOriol Granado, XavierFabregat Gesa, RamonMalo Cerrato, SaraAprenentatge automàticMachine learningAdolescènciaAdolescenceInfantsChildrenBenestarWell-beingExplaining what leads to higher or lower levels of subjective well-being (SWB) in childhood and adolescence is one of the cornerstones within this field of studies, since it can lead to the development of more focused preventive and promotion actions. Although many indicators of SWB have been identified, selecting one over the other to obtain a reasonably short list poses a challenge, given that models are particularly sensitive to the indicators considered.Two Machine Learning (ML) algorithms, one based on Extreme Gradient Boosting and Random Forest and the other on Lineal Regression, were applied to 77 indicators included in the 3rd wave of the Children’s Worlds project and then compared. ExtremeGradient Boosting outperforms the other two, while Lineal Regression outperforms Random Forest. Moreover, the Extreme Gradient Boosting algorithm was used to compare models for each of the 35 participating countries with that of the pooled sample on the basis of responses from 93,349 children and adolescents collected through a representative sampling and belonging to the 10 and 12-year-olds age groups. Large differences were detected by country with regard to the importance of these 77 indicators in explaining the scores for the five-item-version of the CWSWBS5 (Children’s Worlds Subjective Well-Being Scale). The process followed highlights the greater capacity of some ML techniques in providing models with higher explanatory power and less error, and in more clearly differentiating between the contributions of the different indicators to explain children’s and adolescents’ SWB. This finding is useful when it comes to designing shorter but more reliable questionnaires (a selection of 29 indicators were used in this case)Open Access funding provided thanks to the CRUE-CSIC agreement with Springer NatureSpringer2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/25333Social Indicators Research, 2024, vol. 175, p. 25-47Articles publicats (D-PS)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1007/s11205-024-03429-1info:eu-repo/semantics/altIdentifier/issn/0303-8300info:eu-repo/semantics/altIdentifier/eissn/1573-0921Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/253332026-05-29T05:05:01Z
dc.title.none.fl_str_mv A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data Case
title A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data Case
spellingShingle A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data Case
González Carrasco, Mònica
Aprenentatge automàtic
Machine learning
Adolescència
Adolescence
Infants
Children
Benestar
Well-being
title_short A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data Case
title_full A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data Case
title_fullStr A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data Case
title_full_unstemmed A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data Case
title_sort A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data Case
dc.creator.none.fl_str_mv González Carrasco, Mònica
Fabregat, Ramon
Aciar, Silvana Vanesa
Casas Aznar, Ferran
Oriol Granado, Xavier
Fabregat Gesa, Ramon
Malo Cerrato, Sara
author González Carrasco, Mònica
author_facet González Carrasco, Mònica
Fabregat, Ramon
Aciar, Silvana Vanesa
Casas Aznar, Ferran
Oriol Granado, Xavier
Fabregat Gesa, Ramon
Malo Cerrato, Sara
author_role author
author2 Fabregat, Ramon
Aciar, Silvana Vanesa
Casas Aznar, Ferran
Oriol Granado, Xavier
Fabregat Gesa, Ramon
Malo Cerrato, Sara
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Aprenentatge automàtic
Machine learning
Adolescència
Adolescence
Infants
Children
Benestar
Well-being
topic Aprenentatge automàtic
Machine learning
Adolescència
Adolescence
Infants
Children
Benestar
Well-being
description Explaining what leads to higher or lower levels of subjective well-being (SWB) in childhood and adolescence is one of the cornerstones within this field of studies, since it can lead to the development of more focused preventive and promotion actions. Although many indicators of SWB have been identified, selecting one over the other to obtain a reasonably short list poses a challenge, given that models are particularly sensitive to the indicators considered.Two Machine Learning (ML) algorithms, one based on Extreme Gradient Boosting and Random Forest and the other on Lineal Regression, were applied to 77 indicators included in the 3rd wave of the Children’s Worlds project and then compared. ExtremeGradient Boosting outperforms the other two, while Lineal Regression outperforms Random Forest. Moreover, the Extreme Gradient Boosting algorithm was used to compare models for each of the 35 participating countries with that of the pooled sample on the basis of responses from 93,349 children and adolescents collected through a representative sampling and belonging to the 10 and 12-year-olds age groups. Large differences were detected by country with regard to the importance of these 77 indicators in explaining the scores for the five-item-version of the CWSWBS5 (Children’s Worlds Subjective Well-Being Scale). The process followed highlights the greater capacity of some ML techniques in providing models with higher explanatory power and less error, and in more clearly differentiating between the contributions of the different indicators to explain children’s and adolescents’ SWB. This finding is useful when it comes to designing shorter but more reliable questionnaires (a selection of 29 indicators were used in this case)
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/25333
url http://hdl.handle.net/10256/25333
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s11205-024-03429-1
info:eu-repo/semantics/altIdentifier/issn/0303-8300
info:eu-repo/semantics/altIdentifier/eissn/1573-0921
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Social Indicators Research, 2024, vol. 175, p. 25-47
Articles publicats (D-PS)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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