Modelling Binary Logistic Regression For Time Management On The Students’ Academic Performance

  • Norziha Che Him Ts. Dr.
  • Nur ‘Aliza Azmi
  • Suliadi Firdaus Sufahani Ts. Dr.
  • Yusliandy Yusof Mr
Keywords: Time Management, Academic Performance, Binary Logistic Regression, Cumulative Grade Point Averag, Akaike Information Criterion

Abstract

Academic performance of students can be measured by using Cumulative Grade Point Average (CGPA) to look on how well the performance of the students in accomplish their continuous assessment in their programme. Overall, this study’s purpose is to determine the effect of time management on the students’ academic performance in Universiti Tun Hussein Onn Malaysia (UTHM), Pagoh Campus. A total of 357 questionnaires have been distributed to students in UTHM Pagoh Campus by using stratified random sampling. Response variable is the CGPA and the explanatory variables are the time management. Result shows that three most activities that involved by student known as academic, social and co-curriculum, meanwhile, three least activities by student are part-time, co-curriculum and social. Chi-Square Test of Independence presents four explanatory variables with p-value > 0.05 which are , ,  and  that indicated the null hypothesis accepted with the conclusion there is no significant association between explanatory variables to the response variable. Meanwhile, the explanatory variables such as , , , , , , , , ,  and  recorded the p-value <  0.05 as indication of there is a significant association with the response variable. Finally, two logistics regression models known as Model A and Model B have been developed, but only Model A has been chosen as a best model with the lowest value of Akaike Information Criterion (AIC).

Published
07-02-2021
Section
Articles