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Multilevel Modeling in Plain Language

Comment from the Stata technical group

Multilevel Modeling in Plain Language by Karen Robson and David Pevalin is a unique book on multilevel modeling. As the authors point out in their introduction,

This book is for people who want to learn about this technique but are not at all interested in learning all the statistical equations and strange notations that are typically associated with teaching materials in this area. That is not to say we are flagrantly trying to promote bad research, because we are not. We are trying to demystify these types of approaches for people who are intimidated by technical language and mathematical symbols.

To this end, the book provides a conceptual understanding of multilevel models and the motivation for using them. The book includes many examples, all worked using Stata.

The first chapter provides an overview of the types of data for which multilevel models are often used. The authors then present arguments for choosing a multilevel model over another model, such as linear regression, when modeling such data. The second and third chapters introduce random-intercept and random-coefficient models. These chapters demonstrate how to use Stata's xtreg and mixed commands to fit models. They also discuss topics such as model building, model fit, and diagnostics. Here Robson and Pevalin demonstrate fitting two-level linear models, but the concepts presented can easily be extended to more complex models. The fourth chapter focuses on how to present the results of multilevel models—both in tables and in graphs created by the marginsplot command.

This book is a great resource for anyone looking for a nontechnical introduction to multilevel modeling and to fitting these models using Stata.

Table of contents

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