This study was conducted to find a more equitable education system for all students regardless of ethnicity, social status, gender, or even problems arising as a result of the Covid-19 pandemic. The effects of major factors that played a critical role on indicators of equity in education were analyzed. A data set from World Inequity Database on Education (WIDE) was used to gather information to estimate the inequity gap for different indicators and countries. The upper secondary completion rate was selected within this database to explore inequity in worldwide education and several stacked bar plots were drawn to describe the differences among demographics and incomes of countries. The learning achievement in reading(upper secondary) was chosen to explore inequity in education in Canada. A three-factor analysis model was built to study the effects of gender, location, wealth, and their interaction on learning achievement in reading. After model selection and model diagnosis, an additive model was chosen as the final model and it proved that there resided significant main effects. Also, a longitudinal model was built to explore whether observations of learning achievement in reading varied among the different years with four different levels as covariate variables. Three models related to repeated measures design were built, and the final model was chosen based on the AIC value. As a result, it provided a clear indication that the government should take responsibility and well-needed actions to eliminate inequity in education for many disparities that exist within comparisons between groups. Examples are: gender, rural or urban areas, and the social status of families.
| Published in | Mathematical Modelling and Applications (Volume 6, Issue 4) |
| DOI | 10.11648/j.mma.20210604.14 |
| Page(s) | 107-116 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Equity, Education, Three-factor Analysis, Longitudinal Analysis, Visualization
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APA Style
Mandi Jin. (2021). Inequity in Education: Three-factor and Longitudinal Analysis. Mathematical Modelling and Applications, 6(4), 107-116. https://doi.org/10.11648/j.mma.20210604.14
ACS Style
Mandi Jin. Inequity in Education: Three-factor and Longitudinal Analysis. Math. Model. Appl. 2021, 6(4), 107-116. doi: 10.11648/j.mma.20210604.14
AMA Style
Mandi Jin. Inequity in Education: Three-factor and Longitudinal Analysis. Math Model Appl. 2021;6(4):107-116. doi: 10.11648/j.mma.20210604.14
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Download@article{10.11648/j.mma.20210604.14,
author = {Mandi Jin},
title = {Inequity in Education: Three-factor and Longitudinal Analysis},
journal = {Mathematical Modelling and Applications},
volume = {6},
number = {4},
pages = {107-116},
doi = {10.11648/j.mma.20210604.14},
url = {https://doi.org/10.11648/j.mma.20210604.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mma.20210604.14},
abstract = {This study was conducted to find a more equitable education system for all students regardless of ethnicity, social status, gender, or even problems arising as a result of the Covid-19 pandemic. The effects of major factors that played a critical role on indicators of equity in education were analyzed. A data set from World Inequity Database on Education (WIDE) was used to gather information to estimate the inequity gap for different indicators and countries. The upper secondary completion rate was selected within this database to explore inequity in worldwide education and several stacked bar plots were drawn to describe the differences among demographics and incomes of countries. The learning achievement in reading(upper secondary) was chosen to explore inequity in education in Canada. A three-factor analysis model was built to study the effects of gender, location, wealth, and their interaction on learning achievement in reading. After model selection and model diagnosis, an additive model was chosen as the final model and it proved that there resided significant main effects. Also, a longitudinal model was built to explore whether observations of learning achievement in reading varied among the different years with four different levels as covariate variables. Three models related to repeated measures design were built, and the final model was chosen based on the AIC value. As a result, it provided a clear indication that the government should take responsibility and well-needed actions to eliminate inequity in education for many disparities that exist within comparisons between groups. Examples are: gender, rural or urban areas, and the social status of families.},
year = {2021}
}
TY - JOUR T1 - Inequity in Education: Three-factor and Longitudinal Analysis AU - Mandi Jin Y1 - 2021/12/24 PY - 2021 N1 - https://doi.org/10.11648/j.mma.20210604.14 DO - 10.11648/j.mma.20210604.14 T2 - Mathematical Modelling and Applications JF - Mathematical Modelling and Applications JO - Mathematical Modelling and Applications SP - 107 EP - 116 PB - Science Publishing Group SN - 2575-1794 UR - https://doi.org/10.11648/j.mma.20210604.14 AB - This study was conducted to find a more equitable education system for all students regardless of ethnicity, social status, gender, or even problems arising as a result of the Covid-19 pandemic. The effects of major factors that played a critical role on indicators of equity in education were analyzed. A data set from World Inequity Database on Education (WIDE) was used to gather information to estimate the inequity gap for different indicators and countries. The upper secondary completion rate was selected within this database to explore inequity in worldwide education and several stacked bar plots were drawn to describe the differences among demographics and incomes of countries. The learning achievement in reading(upper secondary) was chosen to explore inequity in education in Canada. A three-factor analysis model was built to study the effects of gender, location, wealth, and their interaction on learning achievement in reading. After model selection and model diagnosis, an additive model was chosen as the final model and it proved that there resided significant main effects. Also, a longitudinal model was built to explore whether observations of learning achievement in reading varied among the different years with four different levels as covariate variables. Three models related to repeated measures design were built, and the final model was chosen based on the AIC value. As a result, it provided a clear indication that the government should take responsibility and well-needed actions to eliminate inequity in education for many disparities that exist within comparisons between groups. Examples are: gender, rural or urban areas, and the social status of families. VL - 6 IS - 4 ER -
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