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Multilevel models : applications using SAS /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés Chino |
| Publicado: |
Berlin :
De Gruyter,
2011.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.