Cover -- Title -- Copyright -- Contents -- Preface -- 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