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Multilevel Models [electronic resource] : Applications Using SAS.

By: Wang, Jichuan.
Contributor(s): Xie, Haiyi | Fisher, J. (James).
Material type: TextTextPublisher: Berlin : De Gruyter, 2011Description: 1 online resource (274 p.).ISBN: 9783110267709 (electronic bk.); 3110267705 (electronic bk.).Subject(s): SAS (Computer file) | Computer science | Social Science | SAS (Computer file) | Social sciences -- Research -- Mathematical models | Multilevel models (Statistics) | COMPUTERS / Mathematical & Statistical SoftwareGenre/Form: Electronic books.Additional physical formats: Print version:: Multilevel Models : Applications Using SASDDC classification: 005.55 Online resources: EBSCOhost
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 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.
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.
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.
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.
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.
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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 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.

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.

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.

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.

5.6.2 Poisson regression with over-dispersion and a negative binomial model.

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.

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