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Multilevel models : applications using SAS / Jichuan Wang, Haiyi Xie, James H. Fischer.

By: Wang, Jichuan.
Contributor(s): Xie, Haiyi | Fischer, James H.
Material type: materialTypeLabelBookPublisher: Berlin ; Boston : [Beijing?] : De Gruyter ; Higher Education Press, c2012Description: ix, 264 p. : ill. ; 25 cm.ISBN: 9783110267594 (acidfree paper); 9783110267709 (eISBN); 9783110267594.Uniform titles: 多层统计分析模型 Uniform titles: Duo ceng tong ji fen xi mo xing Subject(s): Social sciences | Multilevel models (Statistics) | SAS (Computer file) | -- Research -- Mathematical modelsDDC classification: 005.55
Contents:
Preface; 1 Introduction; 1.1 Conceptual framework of multilevel modeling; 1.2 Hierarchically structured data; 1.3 Variables in multilevel data; 1.4 Analytical problems with multilevel data; 1.5 Advantages and limitations of multilevel modeling; 1.6 Computer software for multilevel modeling; 2 Basics of Linear Multilevel Models; 2.1 Intraclass correlation coefficient (ICC); 2.2 Formulation of two-level multilevel models; 2.3 Model assumptions; 2.4 Fixed and random regression coefficients; 2.5 Cross-level interactions; 2.6 Measurement centering; 2.7 Model estimation; 2.8 Model fit, hypothesis testing, and model comparisons2.8.1 Model fit2.8.2 Hypothesis testing2.8.3 Model comparisons; 2.9 Explained level-1 and level-2 variances; 2.10 Steps for building multilevel models; 2.11 Higher-level multilevel models; 3 Application of Two-level Linear Multilevel Models; 3.1 Data; 3.2 Empty model; 3.3 Predicting between-group variation; 3.4 Predicting within-group variation; 3.5 Testing random level-1 slopes; 3.6 Across-level interactions; 3.7 Other issues in model development; 4 Application of Multilevel Modeling to Longitudinal Data; 4.1 Features of longitudinal data; 4.2 Limitations of traditional approaches for modeling longitudinal data; 4.3 Advantages of multilevel modeling for longitudinal data; 4.4 Formulation of growth models; 4.5 Data description and manipulation; 4.6 Linear growth models4.6.1 The shape of average outcome change over time; 4.6.2 Random intercept grow.
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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 statistical software, the Statistics Analysis System (SAS (R)). 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.

Preface; 1 Introduction; 1.1 Conceptual framework of multilevel modeling; 1.2 Hierarchically structured data; 1.3 Variables in multilevel data; 1.4 Analytical problems with multilevel data; 1.5 Advantages and limitations of multilevel modeling; 1.6 Computer software for multilevel modeling; 2 Basics of Linear Multilevel Models; 2.1 Intraclass correlation coefficient (ICC); 2.2 Formulation of two-level multilevel models; 2.3 Model assumptions; 2.4 Fixed and random regression coefficients; 2.5 Cross-level interactions; 2.6 Measurement centering; 2.7 Model estimation; 2.8 Model fit, hypothesis testing, and model comparisons2.8.1 Model fit2.8.2 Hypothesis testing2.8.3 Model comparisons; 2.9 Explained level-1 and level-2 variances; 2.10 Steps for building multilevel models; 2.11 Higher-level multilevel models; 3 Application of Two-level Linear Multilevel Models; 3.1 Data; 3.2 Empty model; 3.3 Predicting between-group variation; 3.4 Predicting within-group variation; 3.5 Testing random level-1 slopes; 3.6 Across-level interactions; 3.7 Other issues in model development; 4 Application of Multilevel Modeling to Longitudinal Data; 4.1 Features of longitudinal data; 4.2 Limitations of traditional approaches for modeling longitudinal data; 4.3 Advantages of multilevel modeling for longitudinal data; 4.4 Formulation of growth models; 4.5 Data description and manipulation; 4.6 Linear growth models4.6.1 The shape of average outcome change over time; 4.6.2 Random intercept grow.

Includes bibliographical references (p. [247]-257) and index.

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