This paper introduces a framework of techniques that separately have in the past been developed and used on survival data but are now proposed to be separate parts of a sequence of stages in a much larger modelling process. By integrating these methods to¬-gether, it is proposed that the survival data undergoes a much more thorough investiga¬tion and as a result yields a greater in-depth reporting of the survival distributions, and the covariates that best characterise the survival distribution behaviours in a manner that retains the simple straightforward way in which these methods are clear and easy to use. The framework is applied to a specific data set to study the Length of Studies (LoS) of students at a Greek university enrolled on courses until the event of interest occurs, that is, the depar¬tu¬re from the course due to graduation, dropout or extensive period of study time. Results conclude that the approaches applied in this framework offer a more in-depth insight into student behaviour highlighting also the most influential characteristics.