DEVELOPING MULTILEVEL MODELS FOR REALISTICALLY COMPLEX SOCIAL SCIENCE DATA

The project developed new methodology and associated training materials in the following areas of multilevel modelling: structural equation models, measurement errors and multivariate mixed response types at more than one level of the data hierarchy. The models developed under the project were estimated using Markov Chain Monte Carlo (MCMC) estimation.

The methodology builds upon that already implemented in MLwiN which is described in the MLwiN manuals. The training materials are written in MATLAB. and are available as free-standing programs. They are designed to interface with MLwiN in terms of data transfer but have their own graphical user interfaces for setting up models and displaying results. There is a set of training materials (PDF, 791kB). which provides an introduction to the methodology and a guide to using the software.

Applications are to a variety of problems, including flexible prediction models, multiple imputation for missing data in multilevel models, and misclassification errors in social status data.

Three repeated 1-day workshops were held in Bristol, London and Birmingham, June/July 2007.

The ESRC has rated this project as outstanding. The outstanding grade indicates that a project has fully met its objectives and has provided an exceptional research contribution well above average or very high in relation to the level of award. Go to ESRC award details.

The methodology builds upon that already implemented in MLwiN version 2.02 which is described in the MLwiN manuals. The training materials are written in MATLAB and are available as free-standing programs. They are designed to interface with MLwiN in terms of data transfer but have their own graphical user interfaces for setting up models and displaying results.