| Description |
1 online resource (274 pages) |
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text file |
| 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. |
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2.8 Model fit, hypothesis testing, and model comparisons2.8.1 Model fit; 2.8.2 Hypothesis testing; 2.8.3 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 level-1 random; 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. |
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4.2 Limitations of traditional approaches for modeling longitudinal data4.3 Advantages of multilevel modeling for longitudinal data; 4.4 Formulation of growth models; 4.5 Data and variable description; 4.6 Linear growth models; 4.6.1 The shape of average outcome change over time; 4.6.2 Random intercept growth models; 4.6.3 Random intercept-slope growth models; 4.6.4 Intercept and slope as outcomes; 4.6.5 Controlling for individual background variables in models; 4.6.6 Coding time score; 4.6.7 Residual variance/covariance structures; 4.6.8 Time-varying covariates; 4.7 Curvilinear growth models. |
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4.7.1 Polynomial growth model4.7.2 Dealing with collinearity in higher order polynomial growth model; 4.7.3 Piecewise (linear spline) growth model; 5 Multilevel models for discrete outcome measures; 5.1 Introduction to generalized linear mixed models; 5.1.1 Generalized linear models; 5.1.2 Generalized linear mixed models; 5.2 SAS Procedures for multilevel modeling with discrete outcomes; 5.3 Multilevel models for binary outcomes; 5.3.1 Logistic regression models; 5.3.2 Probit models; 5.3.3 Unobserved latent variables and observed binary outcome measures. |
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5.3.4 Multilevel logistic regression models5.3.5 Application of multilevel logistic regression models; 5.3.6 Application of multilevel logit models to longitudinal data; 5.4 Multilevel models for ordinal outcomes; 5.4.1 Cumulative logit models; 5.4.2 Multilevel cumulative logit models; 5.5 Multilevel models for nominal outcomes; 5.5.1 Multinomial logit models; 5.5.2 Multilevel multinomial logit models; 5.5.3 Application of multilevel multinomial logit models; 5.6 Multilevel models for count outcomes; 5.6.1 Poisson regression models. |
| Note |
5.6.2 Poisson regression with over-dispersion and a negative binomial model. |
| Summary |
This book covers a broad range of topics about multilevel modeling. The goal is to help readersto understand the basic concepts, theoretical frameworks, and application methods of multilevel modeling. Itis at a level also accessible to non-mathematicians, focusing on the methods and applications of various multilevel models and using the widely used statistical software SAS®. Examples are drawn from analysis of real-world research data. |
| Bibliography |
Includes bibliographical references and index. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
SAS (Computer file)
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SAS (Computer file) |
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SAS (Computer file) |
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Social sciences -- Research -- Mathematical models.
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Social sciences -- Research -- Mathematical models. |
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Social sciences -- Research. |
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Multilevel models (Statistics)
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Multilevel models (Statistics) |
| Genre/Form |
Electronic books.
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Electronic books.
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| Added Author |
Xie, Haiyi.
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Fisher, James, 1814?-1886.
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| Other Form: |
Print version: Wang, Jichuan. Multilevel Models : Applications Using SAS. Berlin : De Gruyter, ©2011 9783110267594 |
| ISBN |
9783110267709 (electronic book) |
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3110267705 (electronic book) |
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9783110267594 (alkaline paper) |
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3110267594 (alkaline paper) |
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1306968860 |
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9781306968867 |
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3110267713 |
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