SAGE Handbook of Multilevel Modeling
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B01=Brian D. Marx
B01=Jeffrey S. Simonoff
B01=Marc A. Scott
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estimation
fixed and random effects
inference
Language_English
latent class models
model selection
Multilevel modeling
notation
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robust methods
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Product details
- ISBN 9780857025647
- Weight: 1370g
- Dimensions: 174 x 246mm
- Publication Date: 02 Sep 2013
- Publisher: SAGE Publications Ltd
- Publication City/Country: GB
- Product Form: Hardback
- Language: English
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In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling.
The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field.
The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field.
- Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference.
- Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models.
- Part III includes discussion of missing data and robust methods, assessment of fit and software.
- Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines.
Jeffrey S. Simonoff is Professor of Statistics at the NYU Stern School of Business. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute. He is author or coauthor of roughly 100 articles and five books on the theory and applications of statistics. Brian D. Marx is a Professor of Statistics at Louisiana State University. His main research interests include smoothing, ill-conditioned regression problems, high-dimensional chemometric applications; and he has numerous publications on these topics. He is past president of the Statistical Modelling Society, and is currently member of the Executive Committee of this same international professional society. He is coauthor of the book Regression: Models, Methods, and Applications, as well as, the co-editor of the Sage Handbook on Multilevel Modelling.
