Research Article
Hierarchical Generalized Linear Mixed Models for Multilevel Analysis of Indonesian Student’s PISA Mathematics Literacy Achievement
@INPROCEEDINGS{10.4108/eai.2-8-2019.2290468, author={Tonah Tonah and Anang Kurnia and Kusman Sadik}, title={Hierarchical Generalized Linear Mixed Models for Multilevel Analysis of Indonesian Student’s PISA Mathematics Literacy Achievement}, proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia}, publisher={EAI}, proceedings_a={ICSA}, year={2020}, month={1}, keywords={hierarchical generalized linear mixed models multilevel generalized linear models pisa mathematics literacy achievement}, doi={10.4108/eai.2-8-2019.2290468} }- Tonah Tonah
Anang Kurnia
Kusman Sadik
Year: 2020
Hierarchical Generalized Linear Mixed Models for Multilevel Analysis of Indonesian Student’s PISA Mathematics Literacy Achievement
ICSA
EAI
DOI: 10.4108/eai.2-8-2019.2290468
Abstract
Generally, learning assessment and evaluation data in educational has a hierarchical structures one of which is PISA data. Multilevel models are methods that can be used to analyse hierarchical data structures and can be considered as HGLM models. This study has two objectives namely, examine the distribution of variable mathematical literacy and selecting the best HGLM model to determine student and school level variables that significantly influence students' mathematical literacy achievement. The result we have obtained are mathematical literacy achievement has lognormal distribution and M7 model is the best model.
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