Over the last four decades, the mixed effects model has gained prominence in social
research in education, health and fields whose data naturally have hierarchical
structures. The ability of the mixed effects model to handle unbalanced clustered
data as well as providing analysis of within groups and between groups variations
have been behind the growing inclination to the use of this model. Prior to its
development, the standard linear regression model had been immensely employed
in modeling effects or influence of selected factors on the observed phenomena.
Hierarchical data structures have subjects nested within groups, a fact that introduces
correlation between subjects of the same group and calls for the application
of an appropriate model that sufficiently explains the origin of variations.
In this study the linear mixed effects model is applied in the analysis of national
examination scores for pupils who sat for that examination in 2013. The number of
pupils in each of the sampled schools is variant. The imbalance in the data structure
inhibits the application of models like multivariate regression or the ANOVA
model and therefore stresses the choice for the linear mixed effects model. The
research project builds a suitable linear mixed effects model that explains the distribution
of the observed examination scores given pupils'individual characteristics
as well as school level characteristics.