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Practical Multilevel Modeling Using R

Practical Multilevel Modeling Using R

December 2022 | 256 pages | SAGE Publications, Inc
Practical Multilevel Modeling Using R provides students with a step-by-step guide for running their own multilevel analyses. Detailed examples illustrate the conceptual and statistical issues that multilevel modeling addresses in a way that is clear and relevant to students in applied disciplines. Clearly annotated R syntax illustrates how multilevel modeling (MLM) can be used, and real-world examples show why and how modeling decisions can affect results. The book covers all the basics but also important advanced topics such as diagnostics, detecting and handling heteroscedasticity, power analysis, and missing data handling methods. Unlike other detailed texts on MLM which are written at a very high level, this text with its applied focus and use of R software to run the analyses is much more suitable for students who have substantive research areas but are not training to be methodologists or statisticians. At the end of the chapters, a Test Yourself section is also provided (with answers available on the password-protected instructor website at so that questions can be assigned for homework). A companion R package ("MLMusingR") is also available at which contains the datasets and helper functions used in the book.
1 Introduction
2 The unconditional means model
3 Adding predictors to a random intercepts model
4 Investigating cross-level interactions and random slope models
5 Understanding growth models
6 Centering in multilevel models
7 Multilevel modeling diagnostics
8 Multilevel logistic regression models
9 Modeling data structures with three (or more) levels
10 Missing data in multilevel models
11 Basic power analyses for multilevel models
12 Alternatives to multilevel models

A major strength of this book is its accessibility. Huang effortlessly bridges the divide between the sometimes-abstruse literature on advanced statistics and the needs of applied researchers who lack extensive quantitative training. The result is an approachable text that covers all the basics, but also does not shy away from important advanced topics such as diagnostics, detecting and handling heteroscedasticity, and missing data handling methods. This book would make not only a useful guide to the application of multilevel modeling, but could also serve as an excellent companion text for a course on multilevel modeling.

Kristopher J. Preacher
Vanderbilt University

This book is an excellent book for a graduate level multilevel modeling course that utilizes R; it perfectly combines the depth and breadth on this topic.

Wei Wang
The Graduate Center, City University of New York

Francis L. Huang

Francis Huang, Ph.D. is an Associate Professor at the University of Missouri (MU) in the Statistics, Measurement, and Evaluation in Education program in the Department of Educational, School, and Counseling Psychology of the College of Education. He teaches courses on multilevel modeling, program evaluation, and data management and is the co-director of the methodology branch of the Missouri Prevention Science Institute. Dr. Huang’s research has been funded by federal agencies such as the U.S. Department of Education and the National Institute of Justice. His research focuses on both methodological (e.g., analysis of clustered... More About Author

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ISBN: 9781071846124