Bootstrapping measurement error in multilevel models

It is well-known that measurement error in dependent or independent variables can lead to misleading inferences in regression-type applications (including multilevel modelling). There have been developments which enable the question to be dealt with in some situations, but these are not widely available and deal with only a proportion of the possible measurement error mechanisms. This project will develop a methodology based on bootstrap resampling which would enable simple treatment of a wide range of measurement error mechanisms. This project is taking place in conjunction with the development of MLwiN, and it is aimed to operationalise the results in future releases.