An Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches Using Mplus, Third Edition, 3rd Edition (Paperback) book cover

An Introduction to Multilevel Modeling Techniques

MLM and SEM Approaches Using Mplus, Third Edition, 3rd Edition

By Ronald H. Heck, Scott L. Thomas


460 pages

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Univariate and multivariate multilevel models are used to understand how to design studies and analyze data in this comprehensive text distinguished by its variety of applications from the educational, behavioral, and social sciences. Basic and advanced models are developed from the multilevel regression (MLM) and latent variable (SEM) traditions within one unified analytic framework for investigating hierarchical data. The authors provide examples using each modeling approach and also explore situations where alternative approaches may be more appropriate, given the research goals. Numerous examples and exercises allow readers to test their understanding of the techniques presented.

Changes to the new edition include:

-The use of Mplus 7.2 for running the analyses including the input and data files at 

-Expanded discussion of MLM and SEM model-building that outlines the steps taken in the process, the relevant Mplus syntax, and tips on how to evaluate the models.

-Expanded pedagogical program now with chapter objectives, boldfaced key terms, a glossary, and more tables and graphs to help students better understand key concepts and techniques.

-Numerous, varied examples developed throughout which make this book appropriate for use in education, psychology, business, sociology, and the health sciences.

-Expanded coverage of missing data problems in MLM using ML estimation and multiple imputation to provide currently-accepted solutions (Ch. 10).

-New chapter on three-level univariate and multilevel multivariate MLM models provides greater options for investigating more complex theoretical relationships(Ch.4).

-New chapter on MLM and SEM models with categorical outcomes facilitates the specification of multilevel models with observed and latent outcomes (Ch.8).

-New chapter on multilevel and longitudinal mixture models provides readers with options for identifying emergent groups in hierarchical data (Ch.9).

-New chapter on the utilization of sample weights, power analysis, and missing data provides guidance on technical issues of increasing concern for research publication (Ch.10).

Ideal as a text for graduate courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this book’s practical approach also appeals to researchers. Recommended prerequisites are introductory univariate and multivariate statistics.


"This is a great introductory text with worked examples to guide students and practitioners through analysis and interpretation of multilevel models. The text strikes an effective balance between technical language and applications to demonstrate important concepts." – Grant B. Morgan, Baylor University, USA

"Heck and Thomas provide an introduction to multilevel modeling that is not just comprehensive but also eminently readable. The new edition gives the reader to tackle some of the more recent sophisticated modeling approaches. It is an excellent choice for an instructor looking for a text that helps students to become facile with modeling choices and approaches." – Laura M. Stapleton, University of Maryland, USA

"Developing a basic modeling strategy that researchers can follow to investigate multilevel data structures can be challenging. The authors skillfully present a must-have reference book to get the job done. The easy-to-follow illustrative examples and the extensive software applications are excellent – a masterpiece!"– George A. Marcoulides, University of California, Santa Barbara, USA

"I used the second edition … in my courses and the improvements for the third edition would be exactly my suggestions. I was especially pleased with the decision to use Mplus. …The second edition was easy to read and follow—this was the reason that I assigned the book to the students … in my graduate SEM course. … I would use the third edition … in [the same] course." – G. Leonard Burns, Washington State University, USA

"[This] is the only textbook I know of that presents multilevel regression modeling and structural equation modeling as part of a combined framework. … The revisions … prepare readers to understand both procedures and to conduct both types of analyses. … The changes make the book a better teaching tool. … It would be an excellent choice for students who take my multilevel regression modeling course … [and] … my design of experiments course."Laura M. O’Dwyer, Boston College, USA

"The proposed changes will certainly make the book a better teaching text. … The narrative … provides much needed clarity to a difficult to understand topic. … I would purchase the book for my personal use and I would (and do) include it on a list of recommended resources on multilevel modelling. …This book is rather unique in the content it covers and the audience it serves. … It is meeting a need others are not." – Dick Carpenter, University of Colorado, USA

Table of Contents

1. Introduction 2. Getting Started with Multilevel Analysis 3. Multilevel Regression Models 4. Extending the Two-Level Regression Model 5. Defining Multilevel Latent Variables 6. Multilevel Structural Equation Models 7. Methods for Examining Individual and Organizational Change 8. Multilevel Models with Categorical Variables 9. Multilevel Mixture Models 10. Data Consideration in Examining Multilevel Models

About the Authors

Ronald H. Heck is professor of education at the University of Hawai‘i at Manoa.

Scott L. Thomas is professor and Dean of the School of Educational Studies at Claremont Graduate University.

About the Series

Quantitative Methodology Series

This series presents methodological techniques for investigators and students.

Each volume focuses on a specific method with the goal of providing an understanding and working knowledge of each method with a minimum of mathematical derivations.

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
EDUCATION / Statistics
PSYCHOLOGY / Research & Methodology