<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0871-9187</journal-id>
<journal-title><![CDATA[Revista Portuguesa de Educação]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Port. de Educação]]></abbrev-journal-title>
<issn>0871-9187</issn>
<publisher>
<publisher-name><![CDATA[Centro de Investigação em Educação. Instituto de Educação da Universidade do Minho]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0871-91872021000200042</article-id>
<article-id pub-id-type="doi">10.21814/rpe.18044</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[The reasons why the Regression Tree Method is more suitable than General Linear Model to analyze complex educational datasets]]></article-title>
<article-title xml:lang="pt"><![CDATA[A racionalidade do Método de Regressão em Árvore é mais apropriada do que o Modelo Linear Geral para analisar dados educacionais complexos]]></article-title>
<article-title xml:lang="es"><![CDATA[La racionalidad del Método de Regresión de Árbol es más apropiada que el Modelo Linear General para analizar datos educacionales complejos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gomes]]></surname>
<given-names><![CDATA[Cristiano Mauro Assis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lemos]]></surname>
<given-names><![CDATA[Gina C.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Jelihovschi]]></surname>
<given-names><![CDATA[Enio G.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Federal University of Minas Gerais Laboratory for Cognitive Architecture Mapping (LAICO) ]]></institution>
<addr-line><![CDATA[ Belo Horizonte]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,University of Minho Institute of Education Research Centre on Education (CIEd)]]></institution>
<addr-line><![CDATA[Braga ]]></addr-line>
<country>Portugal</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidade Estadual de Santa Cruz (UESC) Department of Exact and Technological Sciences ]]></institution>
<addr-line><![CDATA[Ilhéus ]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>30</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>30</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>34</volume>
<numero>2</numero>
<fpage>42</fpage>
<lpage>64</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S0871-91872021000200042&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S0871-91872021000200042&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S0871-91872021000200042&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[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&#8217;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&#8217; scores in reading of the 2011&#8217;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.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo Qualquer método quantitativo é formatado por certas regras ou postulados que constituem a sua própria racionalidade. Não fortuitamente, esses postulados determinam as condições e constrangimentos segundo os quais as evidências podem ser construídas. Neste artigo, argumentamos por que a racionalidade do Método de Regressão em Árvore é mais apropriada do que a do Modelo Linear Geral para analisar dados educacionais complexos. Ademais, aplicamos o algoritmo CART do Método de Regressão em Árvore, assim como a Regressão Linear Múltipla, num modelo com 53 preditores, tomando como desfecho as pontuações dos estudantes em leitura da edição de 2011 do Exame Nacional do Ensino Médio (ENEM; N = 3.670.089), o qual é um dado educacional complexo. Esta comparação empírica ilustra como o Método de Regressão em Árvore é superior ao Modelo Linear Geral para fornecer evidências sobre relações não lineares, assim como para lidar com variáveis nominais com muitas categorias, e variáveis ordinais. Concluímos que o Método de Regressão em Árvore constrói melhores evidências sobre as relações entre os preditores e o desfecho em dados complexos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Cualquier método cuantitativo está conformado por ciertas reglas o postulados que constituyen su propia racionalidad. No por casualidad, estos postulados determinan las condiciones y restricciones sobre las cuales se puede construir la evidencia En este artículo, argumentamos por qué la racionalidad del Método de Árbol de Regresión es más apropiada que el Modelo Lineal General para analizar datos educativos complejos. Además, se aplicó el algoritmo CART del Método de Regresión de Árbol, así como la Regresión Linear Múltiple, en un modelo con 53 predictores, tomando como variable de respuesta el desempeño de los estudiantes en lectura de la edición 2011 del Examen Nacional de Educación Secundaria (ENEM; N = 3.670.089), que es un dato educativo complejo. Esta comparación empírica ilustra cómo el Método de Árbol de Regresión es superior al Modelo Linear General al proporcionar evidencia de relaciones no lineales, así como al tratar con variables nominales con muchas categorías y variables ordinales. Llegamos a la conclusión de que el Método de Árbol de Regresión genera mejores evidencias sobre las relaciones entre los predictores y el variable de respuesta en datos complejos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Regression tree model]]></kwd>
<kwd lng="en"><![CDATA[general linear model]]></kwd>
<kwd lng="en"><![CDATA[National Exam of Upper Secondary Education (ENEM)]]></kwd>
<kwd lng="en"><![CDATA[complex datasets.]]></kwd>
<kwd lng="pt"><![CDATA[Modelo de regressão em árvore]]></kwd>
<kwd lng="pt"><![CDATA[modelo linear geral]]></kwd>
<kwd lng="pt"><![CDATA[Exame Nacional do Ensino Médio (ENEM)]]></kwd>
<kwd lng="pt"><![CDATA[dados complexos.]]></kwd>
<kwd lng="es"><![CDATA[Método de regresión de árbol]]></kwd>
<kwd lng="es"><![CDATA[modelo linear general]]></kwd>
<kwd lng="es"><![CDATA[Examen Nacional de la Enseñanza Media (ENEM)]]></kwd>
<kwd lng="es"><![CDATA[datos complejos.]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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