Statistical model on student performance in UTHM by using non-parametric, semi-parametric and parametric survival analysis

Student performance defined as students who are capable to success during their studies. This study explored the use of survival analysis to investigate the performance of Bachelor’s degree students in Universiti Tun Hussein Onn Malaysia (UTHM). The data was collected from the Academic Management Office (AMO), UTHM. The main objective of this study is to estimate the survival rates of students with different entrance qualifications. The study also aim to identify the covariates that dominate the student performance, investigate the performance of Cox model based on the violation of the Proportional Hazard (PH) assumption, compare the model performance by using the survival and Accelerated Failure Time (AFT) models and estimate the time ratio (TR) of student performance in accordance to the selected best model. The survival analysis considered the survival approach such as the Kaplan-Meier (KM) method in the non-parametric method, Cox model in semi-parametric model and survival and AFT models in parametric model. The results revealed that students with STPM-entrance qualification had the highest survival rate compared to Diploma and Matriculation holders. The Cox model in the semi-parametric model identified the GPA, entrance qualification and course as the significant covariates to be included in the study. Faculty covariate was excluded since the p-value insignificant at 90% significance level. The result provided by the Cox model violated the PH assumptions. Then, the performance of the Cox model is less accurate. The invalidation performance of Cox model prompted the need to conduct other parametric survival and AFT models to produce more precise results. As a conclusion, the Log-normal AFT model is the best alternative model to estimate student performance in UTHM and other similar higher educational institution

The post hoc procedure in survival analysis for undergraduate students performance

Survival analysis is a term used to describe the analysis of data in the form of times from a welldefined time origin until the occurrence of any specific event. In an academic research, the time origin often corresponds to the recruitment of an individual into an experimental study. There are the unmeasured chance of finding a falsely significant difference between two or more groups. Compared more than two groups simultaneously increased the chance of making type 1 error. This paper proposed survival analysis with multiple comparison studies to came up with this issue which is to identify the best undergraduate student performance based on the three certificates of qualification which Diploma, Matriculation and STPM. The undergraduate student achievement data are taken to explain this methodology. Kaplan-Meier plotted with survival comparison test, Log-rank test is used to elaborate the application of the Scheffe test. The result reveals that the undergraduates’ students from STPM have performed better in Degree. The Kaplan-Meier curve shows a significant difference in survival plot among three certificates of qualification. However, p-value adjusted by Scheffe test for paired Matriculation and Diploma was found an insignificant difference. So, this study shows the importance of p-value adjustment with Scheffe test in comparing more than two groups to draw a right conclusion