Logistic Regression in Determining Affecting Factors Student Success in an Introductory Statistics Subject

  • NUR SYUHADA MUHAMMAT PAZIL Universiti Teknologi MARA Cawangan Melaka
  • NORWAZIAH MAHMUD Universiti Teknologi MARA, Cawangan Perlis
  • NURIDAWATI BAHAROM Universiti Teknologi MARA, Cawangan Perlis
  • SITI HAFAWATI JAMALUDDIN Universiti Teknologi MARA, Cawangan Perlis


This study aims to find the best model for predicting students’ success based on a binary logistic regression. This analysis was also used to determine the factor that affects student success in Statistics Subjects. Five different data partitioning sets were used. The results indicate that the data with a partitioning set of 70% for the estimation set and 30% for the evaluation set is the best fit model using six independent variables. The predictors under investigation were assessment achievements such as test 1, test 2, quiz, assignment, group project, and final test marks. The outcome showed a significant difference in test 2 and the final test marks in determining the factor affecting the subject's result. Besides, the overall model explained further that 95.8% of the sample was classified correctly. This study was carried out using SPSS software and excel. In order to determine the significant variables, further research can be done using the linear regression analysis method.


Adnan, N. I. M., Wahid, S. N. S., Ujang, S., Yacob, N. A., & Zaini, A. A. (2021). Open And Distance Learning Preparedness Factors Among Academicians In Uitm (Pahang) Using Logistic Regression. AIP Conference Proceedings, 2355(May). https://doi.org/10.1063/5.0053194
Alija, S. (2015). Application of ordinal logistic regression in the study of students ’ achievement in external testing. Bulletin of the Transilvania University of Brasov, 8(57), 1–6.
Baars, G. J. A., Stijnen, T., & Splinter, T. A. W. (2017). A model to predict student failure in the first year of the undergraduate medical curriculum. Health Professions Education, 3(1), 5–14. https://doi.org/10.1016/j.hpe.2017.01.001
Chatterjee, S., & Hadi, A. S. (2006). Regression analysis bye example: fourth edition. In Regression Analysis by Example: Fourth Edition. https://doi.org/10.1002/0470055464
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic regression. In Wiley Series in Probability and Statistics Wiley series in probability and statistics: Vol. 3rd ed. http://search.lib.virginia.edu/catalog/ocn830163779
Hu, S., & Hu, Y. (2021). Research on the influence of students gender on students examination scores based on logistic regression model. Journal of Physics: Conference Series, 1955(1). https://doi.org/10.1088/1742-6596/1955/1/012112
Jeslet, D. S., Komarasamy, D., & Hermina, J. J. (2021). Student result prediction in Covid-19 lockdown using machine learning techniques. Journal of Physics: Conference Series, 1911(1). https://doi.org/10.1088/1742-6596/1911/1/012008
Manieri, E., de Lima, M., & Ghosal, N. (2015). Testing for success: A logistic regression analysis to determine which pre-admission exam best predicts success in an associate degree in nursing program. Teaching and Learning in Nursing, 10(1), 25–29. https://doi.org/10.1016/j.teln.2014.08.001
Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691–692. https://doi.org/10.1093/biomet/78.3.691
Shedriko. (2021). Binary logistic regression in determining affecting factors student graduation in a subject. Jurnal Teknologi dan Open Source, 4(1), 114–120. https://doi.org/10.36378/jtos.v4i1.1401
Suliman, N. A., Abidin, B., Manan, N. A., & Razali, A. M. (2014). Predicting students’ success at pre-university studies using linear and logistic regressions. AIP Conference Proceedings, 1613(Soric 2013), 306–316. https://doi.org/10.1063/1.4894355
Wambuguh, O., & Yonn-Brown, T. (2013). Regular lecture quizzes scores as predictors of final examination performance: A test of hypothesis using logistic regression analysis. International Journal for the Scholarship of Teaching and Learning, 7(1), 1-10. https://doi.org/10.20429/ijsotl.2013.070107
Wang, J., Ge, L., Li, F., Liu, X., Zeng, G., & He, X. (2021). Analysis of influencing factors and teaching reform of nuclear professional English based on logistic regression. Journal of Physics: Conference Series, 1774(1). https://doi.org/10.1088/1742-6596/1774/1/012023
Wanvarie, S., & Sathapatayavongs, B. (2007). Logistic regression analysis to predict Medical Licensing Examination of Thailand (MLET) Step1 success or failure. Annals of the Academy of Medicine Singapore, 36(9), 770–773.
How to Cite
MUHAMMAT PAZIL, NUR SYUHADA et al. Logistic Regression in Determining Affecting Factors Student Success in an Introductory Statistics Subject. Jurnal Intelek, [S.l.], v. 18, n. 1, p. 9-16, jan. 2023. ISSN 2231-7716. Available at: <https://myjms.mohe.gov.my/index.php/intelek/article/view/20133>. Date accessed: 25 june 2024. doi: https://doi.org/10.24191/ji.v18i1.20133.