Models for Value-added Investigations of Teaching Styles Data

Abstract

This paper considers models of educational data where a valueadded analysis is required. These models are multilevel in nature and contain endogenous regressors. Multivariate models are considered so as to simultaneously model results from different subject areas. Path models and factor models are considered as types of model that can be used to overcome the problem of endogeneity. Estimation methods available in MLwiN and EQS are used. The use of a factor model with EQS is shown to give estimates of the effects of teaching styles that have smaller standard errors than any other method studied.