The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics

Charles Kojo Assuah; Robert Benjamin Armah; Rufai Sabtiwu; Grace Abedu; Fusheini Awolu

doi:doi:10.11648/j.ajeit.20220601.16

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The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics

Charles Kojo Assuah, Robert Benjamin Armah, Rufai Sabtiwu, Grace Abedu, Fusheini Awolu

Published in American Journal of Education and Information Technology (Volume 6, Issue 1)

Received: 2 April 2022 Accepted: 19 April 2022 Published: 28 April 2022

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Abstract

This study predicts the preference for three mathematics topics among Junior High School students. Four hundred (400) Junior High School (JHS) students, comprising two hundred and eighteen (218) males and one hundred and eighty-two (182) females selected from Junior High Schools in a school district in Ghana, participated in the study. The multinomial logistic regression model, consisting of three unordered outcome categories (i.e., Relations and Functions, Algebraic expressions, and Linear equations), with predictor variables comprising continuous, nominal, and ordinal variables were used for the study. For Relations and Functions, the results indicated that Math self-concept, Arithmetic ability, Motivation, Instructional strategies and methods, Asanti, Fanti, Ga, and Ewe, were statistically significant (p < .05). Hence, for a unit increase in the Math self-concept measure, a student is 5.82 times more likely to be in the Relations and Functions topic category than in the Linear equations topic category, controlling for the other variables. Again, a female student is 1.15 times more likely than a male student to be in the Relations and Functions topic category than in the Linear equations topic category, controlling for other variables. Similarly, for Algebraic expressions, the results indicated that Math self-concept, Math attitude, Motivation, Instructional strategies and methods, female, Asanti, Fanti, Ga, and Ewe, were statistically significant (p < .05). Thus, for a unit increase in the Math self-concept measure, a student is 2.63 times more likely to be in the Algebraic expressions topic category than in the Linear equations topic category, controlling for the other variables. Again, a female student is 3.75 times more likely than a male student to be in the Algebraic expressions topic category than in the Linear equations topic category, controlling for other variables. These significant predictor variables influencing students’ preference for mathematics topics, add to the body of literature on the factors affecting decision-making in mathematics teaching and learning.

Published in	American Journal of Education and Information Technology (Volume 6, Issue 1)
DOI	10.11648/j.ajeit.20220601.16
Page(s)	31-38
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), 2022. Published by Science Publishing Group

Keywords

Relations and Functions, Algebraic Expressions, Linear Equations, Categories, Multinomial Logistic Regression Model

References

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APA Style

Charles Kojo Assuah, Robert Benjamin Armah, Rufai Sabtiwu, Grace Abedu, Fusheini Awolu. (2022). The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics. American Journal of Education and Information Technology, 6(1), 31-38. https://doi.org/10.11648/j.ajeit.20220601.16

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Charles Kojo Assuah; Robert Benjamin Armah; Rufai Sabtiwu; Grace Abedu; Fusheini Awolu. The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics. Am. J. Educ. Inf. Technol. 2022, 6(1), 31-38. doi: 10.11648/j.ajeit.20220601.16

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AMA Style

Charles Kojo Assuah, Robert Benjamin Armah, Rufai Sabtiwu, Grace Abedu, Fusheini Awolu. The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics. Am J Educ Inf Technol. 2022;6(1):31-38. doi: 10.11648/j.ajeit.20220601.16

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@article{10.11648/j.ajeit.20220601.16,
  author = {Charles Kojo Assuah and Robert Benjamin Armah and Rufai Sabtiwu and Grace Abedu and Fusheini Awolu},
  title = {The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics},
  journal = {American Journal of Education and Information Technology},
  volume = {6},
  number = {1},
  pages = {31-38},
  doi = {10.11648/j.ajeit.20220601.16},
  url = {https://doi.org/10.11648/j.ajeit.20220601.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajeit.20220601.16},
  abstract = {This study predicts the preference for three mathematics topics among Junior High School students. Four hundred (400) Junior High School (JHS) students, comprising two hundred and eighteen (218) males and one hundred and eighty-two (182) females selected from Junior High Schools in a school district in Ghana, participated in the study. The multinomial logistic regression model, consisting of three unordered outcome categories (i.e., Relations and Functions, Algebraic expressions, and Linear equations), with predictor variables comprising continuous, nominal, and ordinal variables were used for the study. For Relations and Functions, the results indicated that Math self-concept, Arithmetic ability, Motivation, Instructional strategies and methods, Asanti, Fanti, Ga, and Ewe, were statistically significant (p < .05). Hence, for a unit increase in the Math self-concept measure, a student is 5.82 times more likely to be in the Relations and Functions topic category than in the Linear equations topic category, controlling for the other variables. Again, a female student is 1.15 times more likely than a male student to be in the Relations and Functions topic category than in the Linear equations topic category, controlling for other variables. Similarly, for Algebraic expressions, the results indicated that Math self-concept, Math attitude, Motivation, Instructional strategies and methods, female, Asanti, Fanti, Ga, and Ewe, were statistically significant (p < .05). Thus, for a unit increase in the Math self-concept measure, a student is 2.63 times more likely to be in the Algebraic expressions topic category than in the Linear equations topic category, controlling for the other variables. Again, a female student is 3.75 times more likely than a male student to be in the Algebraic expressions topic category than in the Linear equations topic category, controlling for other variables. These significant predictor variables influencing students’ preference for mathematics topics, add to the body of literature on the factors affecting decision-making in mathematics teaching and learning.},
 year = {2022}
}

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TY  - JOUR
T1  - The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics
AU  - Charles Kojo Assuah
AU  - Robert Benjamin Armah
AU  - Rufai Sabtiwu
AU  - Grace Abedu
AU  - Fusheini Awolu
Y1  - 2022/04/28
PY  - 2022
N1  - https://doi.org/10.11648/j.ajeit.20220601.16
DO  - 10.11648/j.ajeit.20220601.16
T2  - American Journal of Education and Information Technology
JF  - American Journal of Education and Information Technology
JO  - American Journal of Education and Information Technology
SP  - 31
EP  - 38
PB  - Science Publishing Group
SN  - 2994-712X
UR  - https://doi.org/10.11648/j.ajeit.20220601.16
AB  - This study predicts the preference for three mathematics topics among Junior High School students. Four hundred (400) Junior High School (JHS) students, comprising two hundred and eighteen (218) males and one hundred and eighty-two (182) females selected from Junior High Schools in a school district in Ghana, participated in the study. The multinomial logistic regression model, consisting of three unordered outcome categories (i.e., Relations and Functions, Algebraic expressions, and Linear equations), with predictor variables comprising continuous, nominal, and ordinal variables were used for the study. For Relations and Functions, the results indicated that Math self-concept, Arithmetic ability, Motivation, Instructional strategies and methods, Asanti, Fanti, Ga, and Ewe, were statistically significant (p < .05). Hence, for a unit increase in the Math self-concept measure, a student is 5.82 times more likely to be in the Relations and Functions topic category than in the Linear equations topic category, controlling for the other variables. Again, a female student is 1.15 times more likely than a male student to be in the Relations and Functions topic category than in the Linear equations topic category, controlling for other variables. Similarly, for Algebraic expressions, the results indicated that Math self-concept, Math attitude, Motivation, Instructional strategies and methods, female, Asanti, Fanti, Ga, and Ewe, were statistically significant (p < .05). Thus, for a unit increase in the Math self-concept measure, a student is 2.63 times more likely to be in the Algebraic expressions topic category than in the Linear equations topic category, controlling for the other variables. Again, a female student is 3.75 times more likely than a male student to be in the Algebraic expressions topic category than in the Linear equations topic category, controlling for other variables. These significant predictor variables influencing students’ preference for mathematics topics, add to the body of literature on the factors affecting decision-making in mathematics teaching and learning.
VL  - 6
IS  - 1
ER  -

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Author Information

Charles Kojo Assuah

Department of Mathematics Education, University of Education, Winneba, Ghana
Robert Benjamin Armah

Department of Mathematics Education, University of Education, Winneba, Ghana
Rufai Sabtiwu

Department of Mathematics Education, University of Education, Winneba, Ghana
Grace Abedu

University Practice South Inclusive Basic Schools, Winneba Basic Schools, Winneba, Ghana
Fusheini Awolu

Department of Mathematics and Information Communication Technology Education, University for Development Studies, Tamale, Ghana

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Charles Kojo Assuah, Robert Benjamin Armah, Rufai Sabtiwu, Grace Abedu, Fusheini Awolu. (2022). The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics. American Journal of Education and Information Technology, 6(1), 31-38. https://doi.org/10.11648/j.ajeit.20220601.16

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ACS Style

Charles Kojo Assuah; Robert Benjamin Armah; Rufai Sabtiwu; Grace Abedu; Fusheini Awolu. The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics. Am. J. Educ. Inf. Technol. 2022, 6(1), 31-38. doi: 10.11648/j.ajeit.20220601.16

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AMA Style

Charles Kojo Assuah, Robert Benjamin Armah, Rufai Sabtiwu, Grace Abedu, Fusheini Awolu. The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics. Am J Educ Inf Technol. 2022;6(1):31-38. doi: 10.11648/j.ajeit.20220601.16

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@article{10.11648/j.ajeit.20220601.16,
  author = {Charles Kojo Assuah and Robert Benjamin Armah and Rufai Sabtiwu and Grace Abedu and Fusheini Awolu},
  title = {The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics},
  journal = {American Journal of Education and Information Technology},
  volume = {6},
  number = {1},
  pages = {31-38},
  doi = {10.11648/j.ajeit.20220601.16},
  url = {https://doi.org/10.11648/j.ajeit.20220601.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajeit.20220601.16},
  abstract = {This study predicts the preference for three mathematics topics among Junior High School students. Four hundred (400) Junior High School (JHS) students, comprising two hundred and eighteen (218) males and one hundred and eighty-two (182) females selected from Junior High Schools in a school district in Ghana, participated in the study. The multinomial logistic regression model, consisting of three unordered outcome categories (i.e., Relations and Functions, Algebraic expressions, and Linear equations), with predictor variables comprising continuous, nominal, and ordinal variables were used for the study. For Relations and Functions, the results indicated that Math self-concept, Arithmetic ability, Motivation, Instructional strategies and methods, Asanti, Fanti, Ga, and Ewe, were statistically significant (p < .05). Hence, for a unit increase in the Math self-concept measure, a student is 5.82 times more likely to be in the Relations and Functions topic category than in the Linear equations topic category, controlling for the other variables. Again, a female student is 1.15 times more likely than a male student to be in the Relations and Functions topic category than in the Linear equations topic category, controlling for other variables. Similarly, for Algebraic expressions, the results indicated that Math self-concept, Math attitude, Motivation, Instructional strategies and methods, female, Asanti, Fanti, Ga, and Ewe, were statistically significant (p < .05). Thus, for a unit increase in the Math self-concept measure, a student is 2.63 times more likely to be in the Algebraic expressions topic category than in the Linear equations topic category, controlling for the other variables. Again, a female student is 3.75 times more likely than a male student to be in the Algebraic expressions topic category than in the Linear equations topic category, controlling for other variables. These significant predictor variables influencing students’ preference for mathematics topics, add to the body of literature on the factors affecting decision-making in mathematics teaching and learning.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - The Multinomial Logistic Regression Model’s Utility to Assess Parameters in Predicting Junior High School Students’ Preference for Selected Mathematics Topics
AU  - Charles Kojo Assuah
AU  - Robert Benjamin Armah
AU  - Rufai Sabtiwu
AU  - Grace Abedu
AU  - Fusheini Awolu
Y1  - 2022/04/28
PY  - 2022
N1  - https://doi.org/10.11648/j.ajeit.20220601.16
DO  - 10.11648/j.ajeit.20220601.16
T2  - American Journal of Education and Information Technology
JF  - American Journal of Education and Information Technology
JO  - American Journal of Education and Information Technology
SP  - 31
EP  - 38
PB  - Science Publishing Group
SN  - 2994-712X
UR  - https://doi.org/10.11648/j.ajeit.20220601.16
AB  - This study predicts the preference for three mathematics topics among Junior High School students. Four hundred (400) Junior High School (JHS) students, comprising two hundred and eighteen (218) males and one hundred and eighty-two (182) females selected from Junior High Schools in a school district in Ghana, participated in the study. The multinomial logistic regression model, consisting of three unordered outcome categories (i.e., Relations and Functions, Algebraic expressions, and Linear equations), with predictor variables comprising continuous, nominal, and ordinal variables were used for the study. For Relations and Functions, the results indicated that Math self-concept, Arithmetic ability, Motivation, Instructional strategies and methods, Asanti, Fanti, Ga, and Ewe, were statistically significant (p < .05). Hence, for a unit increase in the Math self-concept measure, a student is 5.82 times more likely to be in the Relations and Functions topic category than in the Linear equations topic category, controlling for the other variables. Again, a female student is 1.15 times more likely than a male student to be in the Relations and Functions topic category than in the Linear equations topic category, controlling for other variables. Similarly, for Algebraic expressions, the results indicated that Math self-concept, Math attitude, Motivation, Instructional strategies and methods, female, Asanti, Fanti, Ga, and Ewe, were statistically significant (p < .05). Thus, for a unit increase in the Math self-concept measure, a student is 2.63 times more likely to be in the Algebraic expressions topic category than in the Linear equations topic category, controlling for the other variables. Again, a female student is 3.75 times more likely than a male student to be in the Algebraic expressions topic category than in the Linear equations topic category, controlling for other variables. These significant predictor variables influencing students’ preference for mathematics topics, add to the body of literature on the factors affecting decision-making in mathematics teaching and learning.
VL  - 6
IS  - 1
ER  -

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