Throughout the social, medical and other sciences the importance ofunderstanding complex hierarchical data structures is wellunderstood. Multilevel modelling is now the accepted statisticaltechnique for handling such data and is widely available incomputer software packages. A thorough understanding of thesetechniques is therefore important for all those working in theseareas. This new edition of Multilevel Statistical Models bringsthese techniques together, starting from basic ideas andillustrating how more complex models are derived. Bayesianmethodology using MCMC has been extended along with new material onsmoothing models, multivariate responses, missing data, latentnormal transformations for discrete responses, structural equationmodeling and survival models.
Key Features:
* Provides a clear introduction and a comprehensive account ofmultilevel models.
* New methodological developments and applications areexplored.
* Written by a leading expert in the field of multilevelmethodology.
* Illustrated throughout with real-life examples, explainingtheoretical concepts.
This book is suitable as a comprehensive text for postgraduatecourses, as well as a general reference guide. Appliedstatisticians in the social sciences, economics, biological andmedical disciplines will find this book beneficial.