Georges Van Landeghem

A New Study on Educational Effectiveness in Secondary Schools in Flanders: An Introduction

As an introduction to the articles of Opdenakker, Van Damme, De Fraine, Van Landeghem, and Onghena (2002) and Van Landeghem, Van Damme, Opdenakker, De Fraine, and Onghena (2002) in this issue, we give some background information on a new study on educational effectiveness in secondary schools, and on the variables measured in that study that are relevant to the 2 articles mentioned. We conclude with some information on the system of secondary education in Flanders.

The effect of schools and classes on mathematics achievement

In this article, the effects of secondary schools, teachers, and classes on mathematics achievement are explored. The results of multilevel analyses indicate important recruitment differences between schools and classes within schools. After controlling for this, the group composition at the class level remained very important for the explanation of differences in mathematics achievement. Also the learning climate in the class, which correlates positively with group composition, explained an additional part of the variance. The effect of other educational process variables disappeared when group composition variables were entered into the model. At the school level, the proportion of girls was positively related to mathematics achievements. Indications of differential effectiveness of classes and schools and of heteroscedasticity related to SES, average class SES, average class SES, sex, and learning climate were found.

The Effect of Schools and Classes on Language Achievement.

This study addresses the effects of secondary schools and classes on language achievement in Flanders, Belgium. The results of a three-level analysis (students within classes within schools) indicate that the group composition at the class level is very important. In classes with a high average initial cognitive ability or a large proportion of girls, the language achievement is higher. These compositional effects are discussed with reference to type ‘A’ and type ‘B’ effects. The analyses show that group composition is more important than educational practices in accounting for differences in language achievement. With whom one is taught has a larger impact than how one is taught. Indications of differential effectiveness of classes related to prior achievement were found, with greater variations in effectiveness between classes for pupils of low prior achievement.

The Effect of Schools and Classes on Noncognitive Outcomes

This article reports on the effects of individual background characteristics, classes, and schools on 4 noncognitive outcomes at the end of the 1st cycle secondary education in Flanders. The outcomes are: the degree to which the student feels at home in the school environment, the extent to which the student does his/her best for the school work, the academic self-concept, and the social integration in the class. Firstly, the raw class and school effects are estimated. Secondly, a broad set of background variables is introduced to isolate the net class and school effects. Thirdly, class and school composition variables are introduced. Finally, the additional predictive power of a number of other student, class, and school characteristics is documented.

The Consequence of Ignoring a Level of Nesting in Multilevel Analysis: A Comment.

This short contribution is a comment on M. Moerbeeks exploration of consequences of ignoring a level of clustering in a multilevel model, which was published in the first issue of the 2004 volume of Multivariate Behavioral Research. After having recapitulated the framework and extended the results of Moerbeeks study, we formulate two critical notes. First, we point at the incompleteness of the conclusions drawn by Moerbeek from the analytical work. The second note is concerned with the limitations of the framework itself.

Separability in the fixed part of multilevel models

Separability in ordinary regression is achieved by partitioning the set of explanatory variables into mutually orthogonal subsets. The coefficient vector of each subset is separate: its estimate depends only on the response and on the explanatory variable scores of the subset. The feasibility of formulating multilevel models with subsets of separate parameters in the fixed part is discussed. Generic sufficient conditions for separability and a series of rules and examples are provided. The search for instances of separability rests on an analysis of the covariance matrix of the multilevel model. Its structure, in terms of its spectral decomposition, explains the role of within-cluster centered and orthogonalized variables in multilevel models.