9783110267594-3110267594-Multilevel Models. Applications using SAS

Multilevel Models. Applications using SAS

ISBN-13: 9783110267594
ISBN-10: 3110267594
Author: Jichuan Wang, Haiyi Xie, James H. Fisher
Publication date: 2011
Publisher: de Gruyter
Format: Hardcover 264 pages
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Book details

ISBN-13: 9783110267594
ISBN-10: 3110267594
Author: Jichuan Wang, Haiyi Xie, James H. Fisher
Publication date: 2011
Publisher: de Gruyter
Format: Hardcover 264 pages

Summary

Acknowledged authors Jichuan Wang, Haiyi Xie, James H. Fisher wrote Multilevel Models. Applications using SAS comprising 264 pages back in 2011. Textbook and eTextbook are published under ISBN 3110267594 and 9783110267594. Since then Multilevel Models. Applications using SAS textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

Interest in multilevel statistical models for social science and public health studies has been aroused dramatically since the mid-1980s. New multilevel modeling techniques are giving researchers tools for analyzing data that have a hierarchical or clustered structure. Multilevel models are now applied to a wide range of studies in sociology, population studies, education studies, psychology, economics, epidemiology, and public health. This book covers a broad range of topics about multilevel modeling. The goal of the authors is to help students and researchers who are interested in analysis of multilevel data to understand the basic concepts, theoretical frameworks and application methods of multilevel modeling. The book is written in non-mathematical terms, focusing on the methods and application of various multilevel models, using the internationally widely used statistical software, the Statistics Analysis System (SAS). Examples are drawn from analysis of real-world research data. The authors focus on twolevel models in this book because it is most frequently encountered situation in real research. These models can be readily expanded to models with three or more levels when applicable. A wide range of linear and non-linear multilevel models are introduced and demonstrated.

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