| Peer-Reviewed

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

Received: 2 April 2022     Accepted: 19 April 2022     Published: 28 April 2022
Views:       Downloads:
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
[1] Acevedo, M., & Krueger, J. I. (2004). Two egocentric sources of the decision to vote: The voter's illusion and the belief in personal relevance. Political Psychology, 25 (1), 115–134. https://doi.org/10.1111/j.1467-9221.2004.00359.x.
[2] Adair. J., (1986). Effective teambuilding. Gower.
[3] Agresti, A. (2002). Categorical data analysis (2nd ed.). John Wiley & Sons, Inc. http://dx.doi.org/10.1002/0471249688.
[4] Allison, S. T., Jordan, A. M. R., & Yeatts, C. E. (1992). A cluster-analytic approach toward identifying the structure and content of human decision-making. Human Relations, 45 (1), 49-72.
[5] Barker, A. (1998). How to be a better decision maker, Instanbul. Retrieved on 18th April 2022, from https://dergipark.org.tr/en/pub/comuybd/issue/43601/533909
[6] Blunden, R. (1994). The concept of choice in health and social services: An overview. In creating opportunities for choice for people with learning difficulties. Proceedings of a conference held in Southampton, 10-11 November. Southampton: University of Southampton Institute for Health Policy Studies.
[7] Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2007). Individual differences in adult decision-making competence. Journal of Personality and Social Psychology, 92 (5), 938–956. https://doi.org/10.1037/0022-3514.92.5.938.
[8] Byrnes, J., P. (2002). The Development of Decision-Making. Journal of Adolescent Health, 31 (6), 208-215.
[9] Cote Sparks, S. & Cote, D. L. (2012). Teaching choice-making to elementary students with mild to moderate disabilities. Intervention in School and Clinic, 47 (5), 290–296.
[10] Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approach. Sage Publications.
[11] Deci, E. L., Connell, J. P., & Ryan, R. M. (1989). Self-determination in a work organization. Journal of Applied Psychology, 74 (4), 580–590. https://doi.org/10.1037/0021-9010.74.4.580
[12] Deci, E. L. & Flaste, R. (1996). Why we do what we do: Understanding self-motivation. Penguin Books.
[13] Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media. https://doi.org/10.1007/978-1-4899-2271-7.
[14] Deci, E. L., Ryan, R. M. & Williams, G. C. (1996). Need satisfaction and the self-regulation of learning. Learning and Individual Differences, 8, 165-183.
[15] Difference Between Choice and Decision. Retrieved on 11th March 2022 from https://pediaa.com/difference-between-choice-and-decision.
[16] El-Habil, A. M. (2012). An application of the multinomial logistic regression model. Pakistan Journal of Statistics and Operation Research, 8 (2), 271-291.
[17] Finucane, M. L., Mertz, C. K., Slovic, P. & Schmidt, E. S. (2005). Task complexity and older adults’ decision-making competence. Psychology and Aging, 20 (1).
[18] Garcia, T. & Pintrich, P. R. (1996). The effects of autonomy motivation and performance in the college classroom. Contemporary Educational Psychology, 21, 477-486.
[19] Gay, L. R., & Airasian, P. (2003). Education research. Competencies for analysis and applications. McGraw-Hills.
[20] Hosmer, D. W. & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). John Wiley & Sons, Inc. doi: 10.1002/0471722146.
[21] Jullisson, E. A., Karlsson, N., & Garling, T. (2005). Weighing the past and the future in decision-making. European Journal of Cognitive Psychology, 17 (4), 561-575. doi: 10.1080/09541440440000159.
[22] Kaltsounis, T. (1987). Teaching social studies in elementary school. Prentice Hall.
[23] Kaplan, R. M., & Saccuzzo, D. P. (2012). Psychological testing: Principles, applications, and issues. Cengage Learning.
[24] Kuzgun, Y. (1992): Decision strategy scale: Development and standardization. VII. Scientific studies of the National Congress of Psychology. Retrieved on 18th April, 2022 from https://www.caves.res.in/journal/articles/Amb_Sci_07(Sp1)_Oa29.pdf.
[25] Sagi, A., & Friedland, N. (2007). The cost of richness: The effect of the size and diversity of decision sets on post-decision regret. Journal of Personality and Social Psychology, 93 (4), 515–524. https://doi.org/10.1037/0022-3514.93.4.515
[26] Schiefele, U. (2001). The role of interest in motivation and learning, In J. M. Collis and S. Messick (Eds.). Intelligence and personality: Bridging the gap in theory and measurement. Mahwah, NJ: Erlbaum, 163–193.
[27] Shah, A. K., & Oppenheimer, D. M. (2011). Grouping information for judgments. Journal of Experimental Psychology: General, 140 (1), 1–13. https://doi.org/10.1037/a0021946
[28] Stanovich, K. E., & West, R. F. (2008). On the relative independence of thinking biases and cognitive ability. Journal of Personality and Social Psychology, 94 (4), 672-695. doi: 10.1037/0022-3514.94.4.672.
[29] Tatlılıoğlu, K. (2014). A research subscales of undergraduates’ personality traits according to five factor personality theory in terms of some variants. Journal of History School (JOHS), 7 (XVII), 939-971.
[30] Wadsworth, B. J. (2015). Piaget’s theory of cognitive development: An introduction for students of psychology and education. Mckay.
[31] West R. F., Toplak M. E., Stanovich K. E. (2008). Heuristics and biases as measures of critical thinking: associations with cognitive ability and thinking dispositions. Journal of Educational Psychology, 100, 930–941.
[32] Zeleny, M. (1982). Multi-criteria decision-making. McGraw-Hills.
Cite This Article
  • 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

    Copy | Download

    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

    Copy | Download

    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

    Copy | Download

  • @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  - 

    Copy | Download

Author Information
  • Department of Mathematics Education, University of Education, Winneba, Ghana

  • Department of Mathematics Education, University of Education, Winneba, Ghana

  • Department of Mathematics Education, University of Education, Winneba, Ghana

  • University Practice South Inclusive Basic Schools, Winneba Basic Schools, Winneba, Ghana

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

  • Sections