Seleção de modelos multiníveis para dados de avaliação educacional
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFSCAR |
Texto Completo: | https://repositorio.ufscar.br/handle/ufscar/9429 |
Resumo: | When a dataset contains a hierarchical data structure, a possible approach is the multilevel regression modelling, which is justified by the significative amout of the data variability that can be explained by macro level processes. In this work, a selection of multilevel regression models for educational data is developed. This analysis is divided into two parts: variable selection and model selection. The latter is subdivided into two categories: classical and Bayesian modeling. Traditional criteria for model selection such as Lasso, AIC, BIC, and WAIC, among others are used in this study as an attempt to identify the factors influencing ninth grade students’ performance in Mathematics of elementary education in the State of São Paulo. Likewise, an investigation was conducted to evaluate the performance of each variable selection criteria and model selection methods applied to fitted models that will be mentioned throughout this work. It was possible to conclude that, under the frequentist approach, BIC is the most efficient, whereas under the bayesian approach, WAIC presented better results. Using Lasso under the frequentist approach, a decrease of 34% on the number of predictors was observed. Finally, we identified that the performance in Mathematics of students in the ninth year of elementary school in the state of São Paulo is most influenced by the following covariates: mother’s educational level, frequency of book reading, time spent with recreation in classroom, the fact of liking Math, school global performance in Mathematics, performance in Portuguese, school administrative dependence, gender, father’s educational degree, failures and age-grade distortion. |
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Coelho, Fabiano RodriguesNoveli, Cibele Maria Russohttp://lattes.cnpq.br/1011098065426388http://lattes.cnpq.br/1142248575230930021e1f85-e088-4344-87ee-e928af2ae31e2018-02-15T16:41:02Z2018-02-15T16:41:02Z2017-08-11COELHO, Fabiano Rodrigues. Seleção de modelos multiníveis para dados de avaliação educacional. 2017. Dissertação (Mestrado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2017. Disponível em: https://repositorio.ufscar.br/handle/ufscar/9429.https://repositorio.ufscar.br/handle/ufscar/9429When a dataset contains a hierarchical data structure, a possible approach is the multilevel regression modelling, which is justified by the significative amout of the data variability that can be explained by macro level processes. In this work, a selection of multilevel regression models for educational data is developed. This analysis is divided into two parts: variable selection and model selection. The latter is subdivided into two categories: classical and Bayesian modeling. Traditional criteria for model selection such as Lasso, AIC, BIC, and WAIC, among others are used in this study as an attempt to identify the factors influencing ninth grade students’ performance in Mathematics of elementary education in the State of São Paulo. Likewise, an investigation was conducted to evaluate the performance of each variable selection criteria and model selection methods applied to fitted models that will be mentioned throughout this work. It was possible to conclude that, under the frequentist approach, BIC is the most efficient, whereas under the bayesian approach, WAIC presented better results. Using Lasso under the frequentist approach, a decrease of 34% on the number of predictors was observed. Finally, we identified that the performance in Mathematics of students in the ninth year of elementary school in the state of São Paulo is most influenced by the following covariates: mother’s educational level, frequency of book reading, time spent with recreation in classroom, the fact of liking Math, school global performance in Mathematics, performance in Portuguese, school administrative dependence, gender, father’s educational degree, failures and age-grade distortion.Quando um conjunto de dados possui uma estrutura hierárquica, uma possível abordagem são os modelos de regressão multiníveis, que se justifica pelo fato de haver uma porção significativa da variabilidade dos dados que pode ser explicada por níveis macro. Neste trabalho, desenvolvemos a seleção de modelos de regressão multinível aplicados a dados educacionais. Esta análise divide-se em duas partes: seleção de variáveis e seleção de modelos. Esta última subdivide-se em dois casos: modelagem clássica e modelagem bayesiana. Buscamos através de critérios como o Lasso, AIC, BIC, WAIC entre outros, encontrar quais são os fatores que influenciam no desempenho em matemática dos alunos do nono ano do ensino fundamental do estado de São Paulo. Também investigamos o funcionamento de cada um dos critérios de seleção de variáveis e de modelos. Foi possível concluir que, sob a abordagem frequentista, o critério de seleção de modelos BIC é o mais eficiente, já na abordagem bayesiana, o critérioWAIC apresentou melhores resultados. Utilizando o critério de seleção de variáveis Lasso para abordagem clássica, houve uma diminuição de 34% dos preditores do modelo. Por fim, identificamos que o desempenho em matemática dos estudantes do nono ano do ensino fundamental do estado de São Paulo é influenciado pelas seguintes covariáveis: grau de instrução da mãe, frequência de leitura de livros, tempo gasto com recreação em dia de aula, o fato de gostar de matemática, o desempenho em matemática global da escola, desempenho em língua portuguesa do aluno, dependência administrativa da escola, sexo, grau de instrução do pai, reprovações e distorção idade-série.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)porUniversidade Federal de São CarlosCâmpus São CarlosPrograma de Pós-graduação em Estatística - Interinstitucional (PIPGEs)UFSCarModelos multiníveisSeleção de modelosCritério de informaçãoProva BrasilMultilevel modelsModel selectionInformation criterionBrazil exam - basic education assessmentCIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICASeleção de modelos multiníveis para dados de avaliação educacionalSelection of multilevel models for educational evaluation datainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisOnline6006001ba7f7bf-2925-40a8-bb44-7847a67d57d1info:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARORIGINALDissFRC.pdfDissFRC.pdfapplication/pdf3393587https://repositorio.ufscar.br/bitstream/ufscar/9429/1/DissFRC.pdf5de42c4fe796872e17fe5e0ddf7ddd37MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81957https://repositorio.ufscar.br/bitstream/ufscar/9429/2/license.txtae0398b6f8b235e40ad82cba6c50031dMD52TEXTDissFRC.pdf.txtDissFRC.pdf.txtExtracted texttext/plain378633https://repositorio.ufscar.br/bitstream/ufscar/9429/3/DissFRC.pdf.txtdb73e0c4df03af6305b09c6d394aebfeMD53THUMBNAILDissFRC.pdf.jpgDissFRC.pdf.jpgIM Thumbnailimage/jpeg5025https://repositorio.ufscar.br/bitstream/ufscar/9429/4/DissFRC.pdf.jpg90595bc62ac0a02987f406885f6ca4a3MD54ufscar/94292023-09-18 18:31:12.483oai:repositorio.ufscar.br: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Repositório InstitucionalPUBhttps://repositorio.ufscar.br/oai/requestopendoar:43222023-09-18T18:31:12Repositório Institucional da UFSCAR - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.por.fl_str_mv |
Seleção de modelos multiníveis para dados de avaliação educacional |
dc.title.alternative.eng.fl_str_mv |
Selection of multilevel models for educational evaluation data |
title |
Seleção de modelos multiníveis para dados de avaliação educacional |
spellingShingle |
Seleção de modelos multiníveis para dados de avaliação educacional Coelho, Fabiano Rodrigues Modelos multiníveis Seleção de modelos Critério de informação Prova Brasil Multilevel models Model selection Information criterion Brazil exam - basic education assessment CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
title_short |
Seleção de modelos multiníveis para dados de avaliação educacional |
title_full |
Seleção de modelos multiníveis para dados de avaliação educacional |
title_fullStr |
Seleção de modelos multiníveis para dados de avaliação educacional |
title_full_unstemmed |
Seleção de modelos multiníveis para dados de avaliação educacional |
title_sort |
Seleção de modelos multiníveis para dados de avaliação educacional |
author |
Coelho, Fabiano Rodrigues |
author_facet |
Coelho, Fabiano Rodrigues |
author_role |
author |
dc.contributor.authorlattes.por.fl_str_mv |
http://lattes.cnpq.br/1142248575230930 |
dc.contributor.author.fl_str_mv |
Coelho, Fabiano Rodrigues |
dc.contributor.advisor1.fl_str_mv |
Noveli, Cibele Maria Russo |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/1011098065426388 |
dc.contributor.authorID.fl_str_mv |
021e1f85-e088-4344-87ee-e928af2ae31e |
contributor_str_mv |
Noveli, Cibele Maria Russo |
dc.subject.por.fl_str_mv |
Modelos multiníveis Seleção de modelos Critério de informação Prova Brasil |
topic |
Modelos multiníveis Seleção de modelos Critério de informação Prova Brasil Multilevel models Model selection Information criterion Brazil exam - basic education assessment CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
dc.subject.eng.fl_str_mv |
Multilevel models Model selection Information criterion Brazil exam - basic education assessment |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
description |
When a dataset contains a hierarchical data structure, a possible approach is the multilevel regression modelling, which is justified by the significative amout of the data variability that can be explained by macro level processes. In this work, a selection of multilevel regression models for educational data is developed. This analysis is divided into two parts: variable selection and model selection. The latter is subdivided into two categories: classical and Bayesian modeling. Traditional criteria for model selection such as Lasso, AIC, BIC, and WAIC, among others are used in this study as an attempt to identify the factors influencing ninth grade students’ performance in Mathematics of elementary education in the State of São Paulo. Likewise, an investigation was conducted to evaluate the performance of each variable selection criteria and model selection methods applied to fitted models that will be mentioned throughout this work. It was possible to conclude that, under the frequentist approach, BIC is the most efficient, whereas under the bayesian approach, WAIC presented better results. Using Lasso under the frequentist approach, a decrease of 34% on the number of predictors was observed. Finally, we identified that the performance in Mathematics of students in the ninth year of elementary school in the state of São Paulo is most influenced by the following covariates: mother’s educational level, frequency of book reading, time spent with recreation in classroom, the fact of liking Math, school global performance in Mathematics, performance in Portuguese, school administrative dependence, gender, father’s educational degree, failures and age-grade distortion. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017-08-11 |
dc.date.accessioned.fl_str_mv |
2018-02-15T16:41:02Z |
dc.date.available.fl_str_mv |
2018-02-15T16:41:02Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
COELHO, Fabiano Rodrigues. Seleção de modelos multiníveis para dados de avaliação educacional. 2017. Dissertação (Mestrado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2017. Disponível em: https://repositorio.ufscar.br/handle/ufscar/9429. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufscar.br/handle/ufscar/9429 |
identifier_str_mv |
COELHO, Fabiano Rodrigues. Seleção de modelos multiníveis para dados de avaliação educacional. 2017. Dissertação (Mestrado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2017. Disponível em: https://repositorio.ufscar.br/handle/ufscar/9429. |
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https://repositorio.ufscar.br/handle/ufscar/9429 |
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openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de São Carlos Câmpus São Carlos |
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Programa de Pós-graduação em Estatística - Interinstitucional (PIPGEs) |
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UFSCar |
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Universidade Federal de São Carlos Câmpus São Carlos |
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