The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-91872021000200042 |
Resumo: | Abstract Any quantitative method is shaped by certain rules or assumptions which constitute its own rationale. It is not by chance that these assumptions determine the conditions and constraints which permit the evidence to be constructed. In this article, we argue why the Regression Tree Method’s rationale is more suitable than General Linear Model to analyze complex educational datasets. Furthermore, we apply the CART algorithm of Regression Tree Method and the Multiple Linear Regression in a model with 53 predictors, taking as outcome the students’ scores in reading of the 2011’s edition of the National Exam of Upper Secondary Education (ENEM; N = 3,670,089), which is a complex educational dataset. This empirical comparison illustrates how the Regression Tree Method is better suitable than General Linear Model for furnishing evidence about non-linear relationships, as well as, to deal with nominal variables with many categories and ordinal variables. We conclude that the Regression Tree Method constructs better evidence about the relationships between the predictors and the outcome in complex datasets. |
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The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasetsRegression tree modelgeneral linear modelNational Exam of Upper Secondary Education (ENEM)complex datasets.Abstract Any quantitative method is shaped by certain rules or assumptions which constitute its own rationale. It is not by chance that these assumptions determine the conditions and constraints which permit the evidence to be constructed. In this article, we argue why the Regression Tree Method’s rationale is more suitable than General Linear Model to analyze complex educational datasets. Furthermore, we apply the CART algorithm of Regression Tree Method and the Multiple Linear Regression in a model with 53 predictors, taking as outcome the students’ scores in reading of the 2011’s edition of the National Exam of Upper Secondary Education (ENEM; N = 3,670,089), which is a complex educational dataset. This empirical comparison illustrates how the Regression Tree Method is better suitable than General Linear Model for furnishing evidence about non-linear relationships, as well as, to deal with nominal variables with many categories and ordinal variables. We conclude that the Regression Tree Method constructs better evidence about the relationships between the predictors and the outcome in complex datasets.Centro de Investigação em Educação. Instituto de Educação da Universidade do Minho2021-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articletext/htmlhttp://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-91872021000200042Revista Portuguesa de Educação v.34 n.2 2021reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttp://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-91872021000200042Gomes,Cristiano Mauro AssisLemos,Gina C.Jelihovschi,Enio G.info:eu-repo/semantics/openAccess2024-02-06T17:04:09Zoai:scielo:S0871-91872021000200042Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:18:30.514172Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets |
title |
The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets |
spellingShingle |
The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets Gomes,Cristiano Mauro Assis Regression tree model general linear model National Exam of Upper Secondary Education (ENEM) complex datasets. |
title_short |
The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets |
title_full |
The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets |
title_fullStr |
The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets |
title_full_unstemmed |
The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets |
title_sort |
The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets |
author |
Gomes,Cristiano Mauro Assis |
author_facet |
Gomes,Cristiano Mauro Assis Lemos,Gina C. Jelihovschi,Enio G. |
author_role |
author |
author2 |
Lemos,Gina C. Jelihovschi,Enio G. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Gomes,Cristiano Mauro Assis Lemos,Gina C. Jelihovschi,Enio G. |
dc.subject.por.fl_str_mv |
Regression tree model general linear model National Exam of Upper Secondary Education (ENEM) complex datasets. |
topic |
Regression tree model general linear model National Exam of Upper Secondary Education (ENEM) complex datasets. |
description |
Abstract Any quantitative method is shaped by certain rules or assumptions which constitute its own rationale. It is not by chance that these assumptions determine the conditions and constraints which permit the evidence to be constructed. In this article, we argue why the Regression Tree Method’s rationale is more suitable than General Linear Model to analyze complex educational datasets. Furthermore, we apply the CART algorithm of Regression Tree Method and the Multiple Linear Regression in a model with 53 predictors, taking as outcome the students’ scores in reading of the 2011’s edition of the National Exam of Upper Secondary Education (ENEM; N = 3,670,089), which is a complex educational dataset. This empirical comparison illustrates how the Regression Tree Method is better suitable than General Linear Model for furnishing evidence about non-linear relationships, as well as, to deal with nominal variables with many categories and ordinal variables. We conclude that the Regression Tree Method constructs better evidence about the relationships between the predictors and the outcome in complex datasets. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-91872021000200042 |
url |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-91872021000200042 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-91872021000200042 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Centro de Investigação em Educação. Instituto de Educação da Universidade do Minho |
publisher.none.fl_str_mv |
Centro de Investigação em Educação. Instituto de Educação da Universidade do Minho |
dc.source.none.fl_str_mv |
Revista Portuguesa de Educação v.34 n.2 2021 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137276241903616 |