) A multilevel Bayesian analysis of university entrance eligibility for selected districts in Sri Lanka: Methods and Application to educational data

Abstract

Multilevel data structures are becoming a commonly encountered phenomenon in educational research. This type of data generates a number of statistical problems, of which clustering is particularly important. To solve the problems inherent in these, special statistical techniques are required. This study aimed to determine the factors affecting the university entrance eligibility of students from some selected districts in Sri Lanka, whilst capturing the layered structure of this educational data into pupil and school levels and determining how these layers interact and impact the dependent variable of interest. This study used university entrance eligibility of General Certificate of Education: Advanced Level (G.C.E) (A/L) student records in 3 districts of Sri Lanka. The response variable is university entrance eligibility of students, which is a binary variable. Thus a two level binary logistic model was fitted using the Bayesian Markov Chain Monte Carlo (MCMC) method as this method has some advantages over other classical statistical methods. When determining the eligibility for university entrance, GCE A/L students find Science subjects more competitive than Arts and Commerce subjects. Students with a higher IQ level (as given with the data) and students with higher English ability stand a better chance. The chance is higher for students from national schools compared to provincial and private schools, and girls show more potential than boys. Students studying in English medium have a higher chance while those studying in Tamil medium have a lower chance compared to the students studying in Sinhala medium.