Classification of state schools in Sri lanka: a multivariate approach

Abstract

Sri Lankan education system, especially in respect of the state schools is ‘currently at a stage of transformation. It is imperative that the key-personnel directly engaged in the transformation process are possessed with a comprehensive knowledge of the compositional aspects of all schools in the country. With this view in mind, I carried out this analysis to evaluate the present’ status of schools and introduce a new classification for schools. However, because of the time limitation | restricted this study only to Galle District schools. A random sample of 252 schools was selected for this analysis. Three multivariate statistical techniques were used in the analysis. Cluster analysis was carried out to identify similar schools and thereby to establish grouping of schools. The validation of the existence of the groups was carried out using the technique of multivariate analysis of variance (MANOVA). In addition a discriminant analysis was performed to formulate the distinctive functions for each identified group. Based on these functions any new school can be classified into the group that it would typically belong to. Finally, the variables used in the study were evaluated for the importance in classifying schools to groups using the MANOVA technique. Six broad groups were identified from the analysis. These six groups were such that they were placed in a ranking order, showing a gradual decline in standards. In addition each group was distinguishable by its markedly different levels of available facilities and performance. One of the ‘notable results of this analysis was that only 7 schools out of 20 National schools (best type of school recognized by the Ministry) fell within the highest rank group identified in this study. The left-out 14 National schools had no empirically valid basis to fall into the group the other seven belonged to. Extremely low Wilks’ Lambda value showed a considerable difference among the groups. Furthermore, six discriminant functions were formulated to assign new schools into groups. The techniques applied and the outcome generated in this study can be extended to all 10,358 schools in Sri Lanka and thus to evaluate their status. The techniques are both adaptive and illustrative. Proper identification of groups would not only enlarge the information base made available for policy-makers and responsible personnel to facilitate both viable planning and sound decision - making, but, also, eventually benefit the children at large.