Description
Book SynopsisMultilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives.
New to this edition:
- An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals;
- Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches;
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Trade Review
"Developing a basic modeling strategy that researchers can follow to investigate multilevel data structures can be challenging. Heck and Thomas have once again presented a must-have reference book to get the job done. This edition’s use of four different software packages and additional easy-to-follow illustrative examples enhance what was already a superb resource for both students and researchers." – George A. Marcoulides, University of California, Santa Barbara, USA
Table of Contents
Preface
1. Introduction
2. Getting Started with Multilevel Analysis
3. Multilevel Regression Models
4. Extending the Two-Level Regression Model
5. Methods for Examining Individual and Organizational Change
6. Multilevel Models with Categorical Variables
7. Multilevel Structural Equation Variables
8. Multilevel Latent Growth and Mixture Models
9. Data Consideration in Examining Multilevel Models
