Multilevel Analysis Techniques and Applications 3rd edition by Joop Hox, Mirjam Moerbeek, Rens van de Schoot ISBN 1317308670 9781317308676
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ISBN 10: 1317308670
ISBN 13: 9781317308676
Author: Joop Hox, Mirjam Moerbeek, Rens van de Schoot
Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines including psychology, education, public health, and sociology. Readers are introduced to a general framework on multilevel modeling which covers both observed and latent variables in the same model, while most other books focus on observed variables. In addition, Bayesian estimation is introduced and applied using accessible software.
Multilevel Analysis Techniques and Applications 3rd Table of contents:
1. Introduction to Multilevel Analysis
1.1 Aggregation and Disaggregation
1.2 Why Do We Need Special Multilevel Analysis Techniques?
1.3 Multilevel Theories
1.4 Estimation and Software
2. The Basic Two-Level Regression Model
2.1 Example
2.2 An Extended Example
2.3 Three- and More Level Regression Models
2.4 Notation and Software
3. Estimation and Hypothesis Testing in Multilevel Regression
3.1 Which Estimation Method?
3.2 Bayesian Methods
3.3 Bootstrapping
3.4 Significance Testing and Model Comparison
3.5 Software
4. Some Important Methodological and Statistical Issues
4.1 Analysis Strategy
4.2 Centering and Standardizing Explanatory Variables
4.3 Interpreting Interactions
4.4 How Much Variance Is Explained?
4.5 Multilevel Mediation and Higher-Level Outcomes
4.6 Missing Data in Multilevel Analysis
4.7 Software
5. Analyzing Longitudinal Data
5.1 Introduction
5.2 Fixed and Varying Occasions
5.3 Example with Fixed Occasions
5.4 Example with Varying Occasions
5.5 Advantages of Multilevel Analysis for Longitudinal Data
5.6 Complex Covariance Structures
5.7 Statistical Issues in Longitudinal Analysis
5.8 Software
6. The Multilevel Generalized Linear Model for Dichotomous Data and Proportions
6.1 Generalized Linear Models
6.2 Multilevel Generalized Linear Models
6.3 Example: Analyzing Dichotomous Data
6.4 Example: Analyzing Proportions
6.5 The Ever-Changing Latent Scale: Comparing Coefficients and Explained Variances
6.6 Interpretation
6.7 Software
7. The Multilevel Generalized Linear Model for Categorical and Count Data
7.1 Ordered Categorical Data
7.2 Count Data
7.3 Explained Variance in Ordered Categorical and Count Data
7.4 The Ever-Changing Latent Scale, Again
7.5 Software
8. Multilevel Survival Analysis
8.1 Survival Analysis
8.2 Multilevel Survival Analysis
8.3 Multilevel Ordinal Survival Analysis
8.4 Software
9. Cross-Classified Multilevel Models
9.1 Introduction
9.2 Example of Cross-Classified Data: Pupils Nested Within (Primary and Secondary Schools)
9.3 Example of Cross-Classified Data: (Sociometric Ratings) in Small Groups
9.4 Software
10. Multivariate Multilevel Regression Models
10.1 The Multivariate Model
10.2 Example of Multivariate Multilevel Analysis: Multiple Response Variables
10.3 Example of Multivariate Multilevel Analysis: Measuring Group Characteristics
11. The Multilevel Approach to Meta-Analysis
11.1 Meta-Analysis and Multilevel Modeling
11.2 The Variance-Known Model
11.3 Example and Comparison with Classical Meta-Analysis
11.4 Correcting for Artifacts
11.5 Multivariate Meta-Analysis
11.6 Software
12. Sample Sizes and Power Analysis in Multilevel Regression
12.1 Sample Size and Accuracy of Estimates
12.2 Power Analysis
12.3 Methods for Randomized Controlled Trials
12.4 Methods for Observational Studies
12.5 Methods for Meta-Analysis
12.6 Software for Power Analysis
13. Assumptions and Robust Estimation Methods
13.1 Introduction
13.2 Example Data and Some Issues with Non-Normality
13.3 Checking Assumptions: Inspecting Residuals
13.4 The Profile Likelihood Method
13.5 Robust Standard Errors
13.6 Multilevel Bootstrapping
13.7 Bayesian Estimation Methods
13.8 Software
14. Multilevel Factor Models
14.1 Introduction
14.2 The Within and Between Approach
14.3 Full Maximum Likelihood Estimation
14.4 An Example of Multilevel Factor Analysis
14.5 Standardizing Estimates in Multilevel Structural Equation Modeling
14.6 Goodness of Fit in Multilevel Structural Equation Modeling
14.7 Software
15. Multilevel Path Models
15.1 Example of a Multilevel Path Analysis
15.2 Statistical and Software Issues
16. Latent Curve Models
16.1 Introduction
16.2 Example of Latent Curve Modeling
16.3 A Comparison of Multilevel Regression Analysis and Latent Curve Modeling
16.4 Software
Appendix A: Checklist for Multilevel Reporting
Appendix B: Aggregating and Disaggregating
Appendix C: Recoding Categorical Data
Appendix D: Constructing Orthogonal Polynomials
Appendix E: Data and Stories
References
Index
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Joop Hox,Mirjam Moerbeek,Rens van de Schoot,Multilevel Analysis
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