Students’ persistence is the ability of students to survive in carrying out the study. In Universitas Terbuka (UT), there are no real dropped out student, but there are considered as non-active or non persistence students. Length of study time among UT’s students can be divided into binary data categories, which are valued as persistence (1) and non persistence (0). Logistic regression analysis is one type of statistical data analysis to be used for binary data. The purposes of writing this article are to identify the factors which influence the length of study time among students of the Department of Management, Faculty of Economics in UT, and to determine appropriate model in order to explain the relationship between the response variables (length of study time) with explanatory variables using logistic regression. The method used in this research is a case study with a number of samples as 2,936 college students. The result of the study shows that the factors influence the length of study time with alpha levels 0.05 are: age, the number of the courses taken, the employment status of the student, the participation in tutorials, the first semester achievement index, and the cumulative grade point.
A Bayesian nonparametric model for spatially distributed multivariate binary data with application to a multidrug-resistant tuberculosis (MDR-TB) study
