Modelling strategies for value-added multilevel models are examined. These types of models typically include an endogenous variable and this causes difficulties for the standard estimation techniques that are commonly used to analyse multilevel models. Two alternative estimation strategies are...

This paper combines two estimation procedures: Iterative Generalized Least Squares as used in the sofware MLwiN; Gibbs Sampling as employed in the software BUGS to Produce a modelling strategy that respects the hierachical nature of the Teaching Styles data and also allows for the endogeneity...

In this paper we examine educational data whch has a cross-classified structure. A cross-classified value-added multilevel model is proposed for these data and the problem of estimation are discussed in relation to the probllem of an endogenous regressor.

The estimation of parameters of lagged multilevel models is considered. This type of model is used in many application areas, including psychology and education, where changes in test results over time can be modelled. Standard estimation techniques are shown to give inconsistent results for...