Multilevel Modeling Applications in STAT IBM SPSS SAS R and HLMTM 1st edition by David Garson ISN 9781544319285 1544319282

It seems like you’re working with a book or guide on Multilevel Modeling (MLM), which covers the fundamentals of multilevel analysis, statistical techniques, and the application of MLM across various software platforms. Below is a highlighted overview of each chapter, emphasizing the main topics and approaches covered.

Chapter 1: Introduction to Multilevel Modeling

• Overview: Introduction to MLM concepts and why they are important in analyzing hierarchical or nested data.
• What Multilevel Modeling Does: Explains the utility of MLM for understanding data that involves more than one level (e.g., individual vs. group).
• Types of Multilevel Data: Overview of different multilevel data structures (e.g., students within schools, patients within hospitals).
• Common Types of Multilevel Models:
  • Null Unconditional Random Intercept Model
  • Conditional Random Intercept Model
  • Conditional Random Coefficients Model
  • Random Intercept Regression Model
  • Random Intercept ANCOVA Model
  • Random Coefficients ANCOVA Model
  • Mediation and Moderation Models in Multilevel Analysis
• Statistical Packages: Overview of alternative software options for MLM.
• Multilevel Modeling vs. GEE: Comparison with Generalized Estimating Equations.

Chapter 2: Assumptions of Multilevel Modeling

• Model Specification: How to specify models correctly for MLM.
• Random Sampling & Sample Size: Key considerations when collecting multilevel data.
• Balanced vs. Unbalanced Designs: How data structure impacts the analysis.
• Linearity and Nonlinearity: Dealing with assumptions of linearity.
• Independence and Missing Data: Addressing common data issues.
• Multicollinearity: Methods for detecting and handling multicollinearity in MLM.
• Covariance Structures: Different assumptions for random effects and repeated measures.

Chapter 3: The Null Model

• Testing the Need for Multilevel Modeling: Using the Intraclass Correlation Coefficient (ICC) to determine whether MLM is necessary.
• Variance Components: How variance is partitioned across levels.
• Likelihood Ratio Tests: A method for comparing models.
• Software: Step-by-step guide to running the Null Model in SPSS, Stata, SAS, HLM 7, and R.

Chapter 4: Estimating Multilevel Models

• Fixed vs. Random Effects: Understanding the difference and when to use each.
• Why Not OLS Regression or GLM?: Explains the shortcomings of traditional regression models for multilevel data.
• Estimation Methods:
  • Maximum Likelihood Estimation (MLE)
  • Restricted Maximum Likelihood Estimation (REML)
  • Full Information Maximum Likelihood (FIML)
• Robust Standard Errors: Using robust methods to account for clustering.

Chapter 5: Goodness of Fit and Effect Size in Multilevel Models

• Goodness of Fit Measures: Likelihood Ratio Tests, AIC, and BIC for model evaluation.
• Effect Size: Proportional reduction in variance, ICC, and Cohen’s d.
• Endogeneity: Discussing how endogeneity affects model interpretation.

Chapter 6: The Two-Level Random Intercept Model

• Model Explanation: Introduction to random intercept models where intercepts vary across groups.
• Practical Examples: Implementing the model in SPSS, Stata, SAS, HLM 7, and R.

Chapter 7: The Two-Level Random Coefficients Model

• Random Slopes: Introduction to models where coefficients (e.g., slopes) vary across groups.
• Model Estimation: Step-by-step guidance using different software platforms.

Chapter 8: The Three-Level Unconditional Random Intercept Model with Longitudinal Data

• Longitudinal Data: Analyzing data with three levels (e.g., individuals within groups, groups within larger units) over time.
• Software Implementation: Methods for SPSS, Stata, SAS, HLM 7, and R.

Chapter 9: Repeated Measures and Heterogeneous Variance Models

• Modeling Time: How to model repeated measures and heterogeneous variances in MLM.
• Software Guides: Implementing models in different software packages.

Chapter 10: Residual and Influence Analysis for a Three-Level RC Model

• Residual Analysis: Identifying and diagnosing model issues with residuals.
• Influence Measures: Detecting outliers or influential points that may affect model estimates.

Chapter 11: Cross-Classified Linear Mixed Models

• Cross-Classified Models: When data is nested within more than one grouping structure (e.g., students in schools that also belong to neighborhoods).
• Model Estimation: Implementing models in SPSS, Stata, SAS, HLM 7, and R.

Chapter 12: Generalized Linear Mixed Models

• Estimation Methods: Overview of GLMMs, useful for non-normal data (e.g., binary or count data).
• Software Implementation: Step-by-step guides for using GLMM in various software.