The book "Doing Meta-Analysis with R – A Hands-On Guide" by Mathias Harrer, Pim Cuijpers, Toshi A. Furakawa, and David D. Ebert is a comprehensive resource for conducting meta-analyses using R. It is structured into 17 chapters and an appendix, covering topics from basic to advanced methods, including network meta-analysis and Bayesian approaches. The book is well-organized, with clear sections on getting started in R, conducting a meta-analysis, advanced models, and helpful tools. Each chapter includes detailed subsections, code examples, and visual elements to aid understanding. The text is highly readable, with text boxes highlighting key concepts and providing examples, functions, and self-assessment questions. The book provides a strong foundation for meta-analysis, with a focus on practical application and clear explanations of statistical concepts. It is suitable for a wide range of readers, from beginners to advanced users, and includes sufficient detail for those who may not be familiar with R. The book is praised for its clear, step-by-step approach, practical examples, and balanced coverage of both contemporary and foundational methods. It is an ideal resource for anyone looking to conduct meta-analyses, as it provides a thorough and accessible guide to the process. The book is well-referenced and includes a comprehensive bibliography, making it a valuable resource for further learning. Overall, the book is a strong, hands-on guide to conducting meta-analyses using R, with a clear structure, practical examples, and a focus on both theory and application.The book "Doing Meta-Analysis with R – A Hands-On Guide" by Mathias Harrer, Pim Cuijpers, Toshi A. Furakawa, and David D. Ebert is a comprehensive resource for conducting meta-analyses using R. It is structured into 17 chapters and an appendix, covering topics from basic to advanced methods, including network meta-analysis and Bayesian approaches. The book is well-organized, with clear sections on getting started in R, conducting a meta-analysis, advanced models, and helpful tools. Each chapter includes detailed subsections, code examples, and visual elements to aid understanding. The text is highly readable, with text boxes highlighting key concepts and providing examples, functions, and self-assessment questions. The book provides a strong foundation for meta-analysis, with a focus on practical application and clear explanations of statistical concepts. It is suitable for a wide range of readers, from beginners to advanced users, and includes sufficient detail for those who may not be familiar with R. The book is praised for its clear, step-by-step approach, practical examples, and balanced coverage of both contemporary and foundational methods. It is an ideal resource for anyone looking to conduct meta-analyses, as it provides a thorough and accessible guide to the process. The book is well-referenced and includes a comprehensive bibliography, making it a valuable resource for further learning. Overall, the book is a strong, hands-on guide to conducting meta-analyses using R, with a clear structure, practical examples, and a focus on both theory and application.