Browsing by Subject "accelerated failure time models"

In this thesis, we model the graduation of Mathematics and Statistics students at the University of Helsinki. The interest is in the graduation and drop-out times of bachelor’s and master’s degree program students. Our aim is to understand how studies lead up to graduation or drop-out, and which students are at a higher risk of dropping out. As the modeled quantity is time-to-event, the modeling is performed with survival analysis methods. Chapter 1 gives an introduction to the subject, while in Chapter 2 we explain our objectives for the research. In Chapter 3, we present the available information and the possible variables for modeling. The dataset covers a 12-year period from 2010/11 to 2021/22 and includes information for 2268 students in total. There were many limitations, and the depth of the data allowed the analysis to focus only on the post-2017/18 bachelor’s program. In Chapter 4, we summarize the data with visual presentation and some basic statistics of the follow-up population and different cohorts. The statistical methods are presented in Chapter 5. After introducing the characteristic concepts of time-to-event analysis, the main focus is on two alternative model choices; the Cox regression and the accelerated failure time models. The modeling itself was conducted with programming language R, and the results are given in Chapter 6. In Chapter 7, we introduce the main findings of the study and discuss how the research could be continued in the future. We found that most drop-outs happen early, during the first and second study year, with the grades from early courses such as Raja-arvot providing some early indication of future success in studies. Most graduations in the post-2017/18 program occur between the end of the third study year and the end of the fourth study year, with the median graduation time being 3,2 years after enrollment. Including the known graduation times from the pre-2017/18 data, the median graduation time from the whole follow-up period was 3,8 years. Other relevant variables in modeling the graduation times were gender and whether or not a student was studying in the Econometrics study track. Female students graduated faster than male students, and students in the Econometrics study track graduated slower than students in other study tracks. In future continuation projects, the presence of more specific period-wise data is crucial, as it would allow the implementation of more complex models and a reliable validation for the results presented in this thesis. Additionally, more accuracy could be attained for the estimated drop-out times.