Better Ways to Compare Schools?

Abstract

The term school effectiveness is found throughout the educational literature, and there is even an annual international conference devoted solely to this topic. The laudable aim of discovering which factors are associated with the success or failure of schools clearly is popular with policy makers and administrators as well as researchers. Much of the activity in this area is concerned with achievement testing, and some of the most interesting work is based on the use of multilevel models. These models are interesting because they attempt to remain faithful to the hierarchical structure of educational data where, for example, pupils are nested within schools and schools within boards or authorities. Applications of these models have been successful in studying the way in which group differences (e.g., between ethnic minorities) vary from school to school; the way it is possible to measure value added, or progress, during schooling by using suitable measures of intake achievement; and the way achievement changes over time using repeated measurements on students. Other work has looked at how schools change over time using repeated measurements of cohorts of students. The application of these models to multivariate data has shown how multiple matrix designs can be modeled efficiently and how efficient estimates can be obtained for generalizability models. A detailed account of these models with applications can be found in Raudenbush and Bryk (1986) and Goldstein (1987). Some of the current development work is concerned with the formulation of multilevel loglinear models, multilevel time series models, multilevel structural equation models, and random cross classifications, and publications in these areas are beginning to appear. All of this is exciting because it provides us with a general tool for the valid analysis of educational data and promises to yield new insights. Indeed, it promises this for all observational data in the human sciences and especially in areas such as complex sample surveys, where direct modeling of the data structure is now possible. In education, the intelligent application of these models promises a deeper understanding of the processes of schooling and the determinants of achievement. With suitably large samples of schools, it should be possible to study how both student and school characteristics play