This text describes techniques developed in the early 1990s which introduce a more systematic approach to the statistical modelling and analysis of hierarchically structured data. The book discusses, and expands on, the core set of established techniques, experience and software packages, which are becoming more commonly used in areas such as education, epidemiology, geography, child growth and household surveys. The second edition aims to integrate existing methodological developments, within a consistent terminology and notation, and avoids undue statistical complexity. Methodological derivations are provided in the appendices and examples and diagrams are used to illustrate the applications of techniques. It explains a number of new developments, especially in the areas of discrete response data, time series models, random cross classifications, errors of measurement, missing data and nonlinear models. This book is suitable for postgraduate courses and as a general reference tool and as such is extensively referenced.
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This book provides the best general introduction to this important area of statistics. (Short Book Reviews 2003-08-01)
Because of the lucid style of writing interspersed with illustrative analysis of data sets, the book will be a valuable asset to anyone interested in the analysis of multileve highly structured data. (Ravi Sreenivasan 2004-05-10)
This book is well-organised and written in a lucid style. At times it is demanding, as it must be given the nature of the material. The tables and figues are carefully produced. Perhaps the most impressive feature of this book is its breadth of coverage, both in terms of different kinds of multilevel models, and in terms of different statistical methodologies and algorithms apllied to fit and interpret such models. Anyone with serious interests in hierarchical or multilevel modelling really ought to have this book on their shelf.
(Statistics in Medicine Volume 24 Number 13 2005-07-01)
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.
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