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Multilevel Statistical Models (Kendall's Library of Statistics) (3rd Revised edition) ISBN: 0340806559 ISBN13: 9780340806555 Multilevel Statistical Models (Kendall's Library of Statistics) (3rd Revised edition) By: Harvey Goldstein Publisher: Hodder Arnold Format: Hardback Our Price: 54.99

  Table of contents:

It is now generally recognised in many areas of the social, medical and other sciences that statistical data typically have complex hierarchical or multilevel structures in which individuals are grouped together in communities or institutions. This grouping affects their behaviour and multilevel modelling is now the accepted statistical technique for the analysis of this type of data. An understanding of these methods is vital for researchers in fields such as education, epidemiology, geography, child growth and social surveys, among others. This new edition brings the book fully up to date, explaining important new developments such as the use of Markov Chain Monte Carlo methods, bootstrapping and mulitvariate models. The book has been completely restructured for this third edition and extra space has been given to discussion of key issues such as missing data, measurement errors and multivariate models. Real-life examples are used throughout to illustrate clearly the theoretical concepts.


Contents:

An introduction to multilevel models
- the basic two-level model
- three-level models and more complex hierarchical structures
- multilevel models for discrete response data
- models for repeated measures data
- multivariate multilevel data
- multilevel factor analysis and structural equation models
- nonlinear multilevel models
- multilevel modelling in sample surveys
- multilevel event history models
- cross classified data structures
- multiple membership models
- measurement errors in multilevel models
- missing data in multilevel models
- software for multilevel modelling resources and further developments.


Brief Description:

Explaining important developments such as the use of Markov Chain Monte Carlo methods, bootstrapping and mulitvariate models, this book discusses key issues such as missing data, measurement errors and multivariate models. It uses real-life examples to illustrate the theoretical concepts.

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