Quantitative models for the analysis of educational policy

Abstract

The subject matter of this thesis is the application of quantitative methods in the management of education. All levels of education are examined: special education, primary schools, secondary schools, post-compulsory education, and universities. Its objective is to highlight the way in which the understanding of a problem, or a system depends on the methods used to analyse it, it is argued that important social and management implications may be hidden behind the selection of a particular mathematical model. The main body of the thesis is formed by a set of independent but related research papers. The city of Southampton figures prominently in this research, reflecting the way in which work has evolved. The problems of educational management have been studied at the local level as and when they have arisen. Local analyses have, however, made it possible to develop a general overview of the problems of education management and of the way in which educational problems are related to other social problems. A series of original contributions have been made, amongst them are: 1.- A socio-demographic geographical study of children with Moderate Learning Difficulties. For the first time the number of children for whom separate special provision is made has been placed in the context of the relevant school age population of the area in which the children live. This makes it possible to separate, for the first time, demographic and policy effects. 2.- A population census based study of the demand for school places in a particular area. This made it possible to compare the number of children in a particular area with the number of children who attend the school. Social and demographic trends are identified and statements about the future of a particular school can be made. 3.- A new way of looking at out-of-catchment area transfers. 4.- A new measure of the attractiveness of a particular school. 5.- A comprehensive study of the relationship between the socio-economic geographical characteristics of an area, the attractiveness of a school, and academic results. 6.- A new methodology, based on Multidimensional Scaling techniques (MDS), that can be used to compare different areas on the basis of Census information and to explore the social problems that they face. 7.- A new methodology for clustering geographical areas based on MDS. This methodology makes statistical results accessible to a non-specialist, and makes it possible to explain in non-technical terms what members of a particular cluster have in common and in what way they differ from members of another cluster. This methodology is illustrated by means of an example which relates to the possible social impact of a change in taxation policy. 8.- The use of the 1981 population Census to calculate staying-on rates at the age of 16. 9.- A comprehensive study of the demand for school places in non-compulsory education from the different areas of a city, highlighting the link between educational policy, social structure, and demand for post-compulsory education. 10.- An econometric model for demand and supply of university places. This model incorporates a new interpretation for a partial adjustment mechanism. The model is no longer appropriate because of changes in the way in which university education is funded, but it was useful as a starting point for the analysis of the consequences of educational policy. 11.- The use of published UCCA information to explore university admissions policies. It was found that the 1981 cuts in university expenditure discriminated against applicants from poorer backgrounds and in favour of overseas applicants. 12.- A comprehensive analysis of the effect of the 1981 educational expenditure cuts in a particular university. 13.- A new way of looking at the popularity of a university or a university department. 14.- A new way of looking at academic differences between the entrants to different disciplines at a university. 15.- A MDS analysis of the 1986 research ratings of university departments which shows the way in which the perceived academic excellence is related to past funding policy. A general conclusion that can be obtained from the quantitative analyses described here is that children from poorer backgrounds are at a disadvantage through all their education process. Policy decisions have, on the whole, aggravated this situation.

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