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Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the bio-medical sciences. The models used for this type of data are linear and nonlinear regression models that account for observed and unobserved heterogeneity at the various levels in the data.
This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. The authors of the chapters are the leading experts in the field.
Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is useful for empirical researchers in these fields. Prior knowledge of multilevel analysis is not required, but abasic knowledge of regression analysis, (asymptotic) statistics, and matrix algebra is assumed.
This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. The authors of the chapters are the leading experts in the field.
Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is useful for empirical researchers in these fields. Prior knowledge of multilevel analysis is not required, but abasic knowledge of regression analysis, (asymptotic) statistics, and matrix algebra is assumed.
From the reviews:
"HMA would be appropriate for readers with sufficient background to start to familiarize themselves with the developments and issues in multilevel analysis. One satisfying quality of HMA is that it gives attention to all aspects of multilevel analysis: model (mis)specification, inference (both theory and computational issues), and design. HMA is also a nice balance between theory and practical advice for application. The high quality of the writing and typesetting of HMA is consistent with that of the contributing authors and publisher." (Psychometrika. 2008)
"The Handbook of Multilevel Analysis is yet another endeavor to bring together different issues related to multilevel modeling. ... The handbook includes 12 chapters, and these chapters discuss many useful topics on multilevel analysis ... . For researchers who want to develop computer programs, the book may provide mathematical foundations and algorithms that can be used for such a purpose. ... This book will serve the needs of researchers who would like to gain a greater mathematical appreciation for multilevel modeling." (Cody Ding, PsycCRITIQUES, Vol. 54 (3), January, 2009)
"This book ... presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analysed mathematically, and illustrated by empirical examples. ... Given the omnipresence of multilevel data in social, behavioral, and biomedical sciences, this book is very useful for empirical researchers in these fields." (T. Postelnicu, Zentralblatt MATH, Vol. 1148, 2008)
"This book presents some of the most recent developments in multilevel data analysis. ... The book contains also a large amount of cross-referencing and several empirical applications in the context of social, behavioural and health sciences. This book may represent a useful tool for researchers interested in analyzing data with multilevel structures ... ."(Statistica, Vol. LXVII (4), 2007)
This volume is very useful for researchers wanting either an introduction to the use of random effects models in the social sciences or a presentation of recent advances. It would be suitable for research workers in the social sciences as well as the statisticians working in that field The quality of the writing is uniformly high..the notation is carefully chosen This volume will prove to be a very valuable reference. (Journal of the American Statistical Association, Vol. 105, No. 489)
"HMA would be appropriate for readers with sufficient background to start to familiarize themselves with the developments and issues in multilevel analysis. One satisfying quality of HMA is that it gives attention to all aspects of multilevel analysis: model (mis)specification, inference (both theory and computational issues), and design. HMA is also a nice balance between theory and practical advice for application. The high quality of the writing and typesetting of HMA is consistent with that of the contributing authors and publisher." (Psychometrika. 2008)
"The Handbook of Multilevel Analysis is yet another endeavor to bring together different issues related to multilevel modeling. ... The handbook includes 12 chapters, and these chapters discuss many useful topics on multilevel analysis ... . For researchers who want to develop computer programs, the book may provide mathematical foundations and algorithms that can be used for such a purpose. ... This book will serve the needs of researchers who would like to gain a greater mathematical appreciation for multilevel modeling." (Cody Ding, PsycCRITIQUES, Vol. 54 (3), January, 2009)
"This book ... presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analysed mathematically, and illustrated by empirical examples. ... Given the omnipresence of multilevel data in social, behavioral, and biomedical sciences, this book is very useful for empirical researchers in these fields." (T. Postelnicu, Zentralblatt MATH, Vol. 1148, 2008)
"This book presents some of the most recent developments in multilevel data analysis. ... The book contains also a large amount of cross-referencing and several empirical applications in the context of social, behavioural and health sciences. This book may represent a useful tool for researchers interested in analyzing data with multilevel structures ... ."(Statistica, Vol. LXVII (4), 2007)
This volume is very useful for researchers wanting either an introduction to the use of random effects models in the social sciences or a presentation of recent advances. It would be suitable for research workers in the social sciences as well as the statisticians working in that field The quality of the writing is uniformly high..the notation is carefully chosen This volume will prove to be a very valuable reference. (Journal of the American Statistical Association, Vol. 105, No. 489)