Categorical Data Analysis and Multilevel Modeling Using R

Categorical Data Analysis and Multilevel Modeling Using R is a practical guide to regression techniques for analyzing binary, ordinal, nominal, and count response variables using R software. It offers a unified framework for single-level and multilevel modeling and demonstrates how to conduct the analysis, interpret the models, and present the results for publication.

Format: Paperback / softback
Length: 744 pages
Publication date: 10 May 2022
Publisher: SAGE Publications Inc

Categorical Data Analysis and Multilevel Modeling Using R offers a comprehensive guide to regression techniques for analyzing binary, ordinal, nominal, and count response variables using the R software. Authored by Xing Liu, this book provides a unified framework for both single-level and multilevel modeling of categorical and count response variables, encompassing both frequentist and Bayesian approaches. Each chapter demonstrates step-by-step procedures for conducting the analysis using R, interpreting the models, and presenting the results for publication. Additionally, a companion website offers datasets and R commands used in the book, along with solutions for the end-of-chapter exercises on the instructor's site. This resource is an invaluable tool for researchers and practitioners seeking to leverage the power of R for categorical data analysis and multilevel modeling.
Categorical Data Analysis and Multilevel Modeling Using R is a comprehensive guide that provides practical insights into regression techniques for analyzing binary, ordinal, nominal, and count response variables using the R software. Authored by Xing Liu, this book offers a unified framework for both single-level and multilevel modeling of categorical and count response variables, encompassing both frequentist and Bayesian approaches.

The book is organized into chapters, each dedicated to a specific aspect of categorical data analysis and multilevel modeling. Each chapter begins with an introduction that provides an overview of the topic and highlights the key concepts that will be covered. The subsequent sections provide detailed explanations of the relevant R functions, methods, and techniques, accompanied by examples and code snippets to illustrate their application.

One of the key strengths of this book is its comprehensive coverage of both single-level and multilevel modeling approaches. The author provides a clear distinction between these two approaches and explains when each is most appropriate. Single-level modeling involves analyzing data at the individual level, while multilevel modeling considers the hierarchical structure of the data, such as nested variables or repeated measures.

Throughout the book, the author emphasizes the importance of understanding the underlying assumptions and principles of each modeling approach. This helps readers to make informed decisions about which approach to use in different situations and to interpret the results of the analysis correctly.

In addition to providing a theoretical foundation, the book also offers practical guidance on how to conduct categorical data analysis and multilevel modeling using R. Each chapter includes step-by-step instructions on how to load data, select appropriate models, and interpret the results. The author also provides tips and tricks for optimizing the performance of the analysis and addressing common challenges that may arise.

Furthermore, the book includes a companion website that provides additional resources for students and practitioners. The website includes datasets and R commands used in the book, along with solutions for the end-of-chapter exercises. This resource is particularly useful for those who want to gain hands-on experience with the techniques covered in the book.

In conclusion, Categorical Data Analysis and Multilevel Modeling Using R is a valuable resource for researchers and practitioners interested in analyzing categorical data and multilevel models. Authored by a leading expert in the field, the book provides a comprehensive and practical guide to the subject, covering both theoretical foundations and practical applications. With its clear explanations, examples, and code snippets, this book is an essential tool for anyone seeking to leverage the power of R for categorical data analysis and multilevel modeling.

Weight: 1166g
Dimension: 188 x 231 x 40 (mm)
ISBN-13: 9781544324906