Stata Press is pleased to announce the release of Multilevel and Longitudinal Modeling Using Stata, Volumes I and II, Fourth Edition by Sophia Rabe-Hesketh and Anders Skrondal. This book debuted on the top 10 list for Kindle’s new releases for Probability & Statistics and consistently stayed there for weeks. This book was also on the top 10 list for Kindle’s new releases in Mathematics, competing with many other books.

Rabe-Hesketh and Skrondal’s latest book is a complete resource for learning to model data in which observations are grouped—whether those groups are formed by a nesting structure, such as children nested in classrooms, or formed by repeated observations on the same individuals. This text introduces random-effects models, fixed-effects models, mixed-effects models, marginal models, dynamic models, and growth-curve models, all of which account for the grouped nature of these types of data. As Rabe-Hesketh and Skrondal introduce each model, they explain when the model is useful, its assumptions, how to fit and evaluate the model using Stata, and how to interpret the results. With this comprehensive coverage, researchers who need to apply multilevel models will find this book to be the perfect companion.

This is an ideal textbook for introducing concepts, assumptions, and good practices in multilevel modeling, as well as teaching analysis skills in Stata. It provides examples from a variety of disciplines, and it has extensive end-of-chapter exercises that allow students to practice newly learned material.

The book comprises two volumes. Volume I focuses on linear models for continuous outcomes, while volume II focuses on generalized linear models for binary, ordinal, count, and other types of outcomes. In both volumes, readers will find extensive applications of multilevel and longitudinal models.

The fourth edition of Multilevel and Longitudinal Modeling Using Stata has been thoroughly revised and updated. In it, you will find new material on Kenward–Roger degree-of-freedom adjustments for small sample sizes, difference-in-differences estimation for natural experiments, instrumental-variables estimation to account for level-one endogeneity, and Bayesian estimation for crossed-effects models. In addition, you will find new discussions of meologit, cmxtmixlogit, mestreg, menbreg, and other commands introduced in Stata since the third edition of the book.

In summary, Multilevel and Longitudinal Modeling Using Stata, Fourth Edition is the most complete, up-to-date depiction of Stata’s capacity for fitting models to multilevel and longitudinal data. Readers will also find thorough explanations of the methods and practical advice for using these techniques. This text is a great introduction for researchers and students wanting to learn about these powerful data analysis tools.

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