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Record Nr. |
UNINA9910828881503321 |
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Autore |
Wang Jichuan |
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Titolo |
Multilevel models [[electronic resource] ] : applications using SAS / / Jichuan Wang, Haiyi Xie, James H. Fischer |
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Pubbl/distr/stampa |
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Berlin, : De Gruyter |
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Boston, : Higher Education Press, c2012 |
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ISBN |
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Descrizione fisica |
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1 online resource (274 p.) |
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Classificazione |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Social sciences - Research - Mathematical models |
Multilevel models (Statistics) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Frontmatter -- Preface / Wang, Jichuan / Xie, Haiyi / Fisher, James H. -- Contents -- Chapter 1. Introduction -- Chapter 2. Basics of linear multilevel models -- Chapter 3. Application of two-level linear multilevel models -- Chapter 4. Application of multilevel modeling to longitudinal data -- Chapter 5. Multilevel models for discrete outcome measures -- Chapter 6. Other applications of multilevel modeling and related issues -- References -- Index |
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Sommario/riassunto |
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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 |
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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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