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.
