Hierarchical Linear Models: Applications and Data Analysis Methods (Advanced Quantitative Techniques

Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:

* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators

• Multilevel Analysis for Applied Research: It's Just Regression! (Methodology in the Social Sciences)
• Principles and Practice of Structural Equation Modeling, Fourth Edition (Methodology in the Social Sciences)
• Multilevel Analysis (Quantitative Methodology Series)
• Introduction to Mediation, Moderation, and Conditional Process Analysis, Second Edition: A Regression-Based Approach (Methodology in the Social Sciences)
• Multilevel Modeling in Plain Language
• Multilevel Analysis: An Introduction To Basic And Advanced Multilevel Modeling
• Multilevel Modeling (Quantitative Applications in the Social Sciences)
• Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
• Data Analysis Using Regression and Multilevel/Hierarchical Models
• Multilevel Modeling Using R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)