Predicting medical student performance from attributes at entry: a latent class analysis

Objectives The objectives of this study were two-fold. Firstly, we aimed to model 'typologies' of student examination performance by grouping students into like categories based on measures of prior academic achievement (particularly in the science subjects) and interview rating at time of entry to a medical degree course, and outcome measures of subsequent performance across the course. Secondly, we aimed to illustrate and evidence the utility of the latent class analysis (LCA) clustering technique to provide meaningful information on the effectiveness of a student selection process with respect to the likelihood of poor examination performance. Methods For this retrospective study, anonymised data on two sequential cohorts of students who graduated from a 5-year Bachelor of Medicine, Bachelor of Surgery degree course were analysed using LCA. In order to triangulate the findings, the same data were analysed using the more conventional approach of logistic regression. Results The LCA identified three distinct classes or typologies of student examination performance using measures of prior academic achievement and interview rating at time of course entry. Measures of prior academic achievement and score on a structured admissions interview made significant contributions to the model's ability to discriminate between typologies. Strong prior academic achievement, especially in chemistry, and high interview score were positively related to the likelihood of successful test performance. These findings were supported by the logistic regression analysis. Conclusions The LCA clustering technique provided meaningful information on the performance of a selection process. As a complementary tool to existing methods used in this area of research, LCA has the potential to empirically inform the selection process.