This book provides a hands-on guide to performing meta-analyses using R. It is written for applied researchers, students, and data scientists who want to learn how to conduct meta-analyses in R. The book covers the fundamentals of meta-analysis, including effect sizes, pooling effect sizes, heterogeneity, and publication bias. It also includes advanced methods such as multilevel meta-analysis, structural equation modeling, network meta-analysis, and Bayesian meta-analysis. The book also covers practical aspects of meta-analysis, such as data preparation, coding, and reporting. The authors emphasize the importance of understanding the limitations and biases of meta-analysis and provide guidance on how to avoid common pitfalls. The book is written in a clear and accessible style, with practical examples and exercises to help readers apply the concepts in real-world scenarios. The book also includes a companion R package, {dmetar}, which provides functions for conducting meta-analyses in R. The authors also provide resources for further learning and reference materials for advanced topics. The book is intended to be read in a linear fashion, starting with the basics of R and meta-analysis, and then moving on to more advanced topics. The authors also provide a list of symbols and a glossary of terms to help readers understand the concepts. The book is written for a general audience, with no prior knowledge of R or statistical analysis required. The authors believe that meta-analysis is a powerful tool for synthesizing research evidence and making informed decisions in various fields. The book aims to help readers understand the principles of meta-analysis and how to apply them in their own research. The authors also emphasize the importance of reproducibility and transparency in meta-analysis, and provide guidance on how to report results effectively. The book is structured into four parts: Getting Started, Meta-Analysis in R, Advanced Methods, and Helpful Tools. Each part includes chapters that cover specific topics in meta-analysis, with practical examples and exercises. The authors also provide a list of references and a glossary of terms to help readers understand the concepts. The book is written in a clear and accessible style, with practical examples and exercises to help readers apply the concepts in real-world scenarios. The authors believe that meta-analysis is a powerful tool for synthesizing research evidence and making informed decisions in various fields. The book aims to help readers understand the principles of meta-analysis and how to apply them in their own research. The authors also emphasize the importance of reproducibility and transparency in meta-analysis, and provide guidance on how to report results effectively. The book is intended to be read in a linear fashion, starting with the basics of R and meta-analysis, and then moving on to more advanced topics. The authors also provide a list of symbols and a glossary of terms to help readers understand the concepts. The book is written for a general audience, with no prior knowledge of R or statistical analysis required. The authors believe that meta-analysis is a powerful tool for synthesizing research evidence andThis book provides a hands-on guide to performing meta-analyses using R. It is written for applied researchers, students, and data scientists who want to learn how to conduct meta-analyses in R. The book covers the fundamentals of meta-analysis, including effect sizes, pooling effect sizes, heterogeneity, and publication bias. It also includes advanced methods such as multilevel meta-analysis, structural equation modeling, network meta-analysis, and Bayesian meta-analysis. The book also covers practical aspects of meta-analysis, such as data preparation, coding, and reporting. The authors emphasize the importance of understanding the limitations and biases of meta-analysis and provide guidance on how to avoid common pitfalls. The book is written in a clear and accessible style, with practical examples and exercises to help readers apply the concepts in real-world scenarios. The book also includes a companion R package, {dmetar}, which provides functions for conducting meta-analyses in R. The authors also provide resources for further learning and reference materials for advanced topics. The book is intended to be read in a linear fashion, starting with the basics of R and meta-analysis, and then moving on to more advanced topics. The authors also provide a list of symbols and a glossary of terms to help readers understand the concepts. The book is written for a general audience, with no prior knowledge of R or statistical analysis required. The authors believe that meta-analysis is a powerful tool for synthesizing research evidence and making informed decisions in various fields. The book aims to help readers understand the principles of meta-analysis and how to apply them in their own research. The authors also emphasize the importance of reproducibility and transparency in meta-analysis, and provide guidance on how to report results effectively. The book is structured into four parts: Getting Started, Meta-Analysis in R, Advanced Methods, and Helpful Tools. Each part includes chapters that cover specific topics in meta-analysis, with practical examples and exercises. The authors also provide a list of references and a glossary of terms to help readers understand the concepts. The book is written in a clear and accessible style, with practical examples and exercises to help readers apply the concepts in real-world scenarios. The authors believe that meta-analysis is a powerful tool for synthesizing research evidence and making informed decisions in various fields. The book aims to help readers understand the principles of meta-analysis and how to apply them in their own research. The authors also emphasize the importance of reproducibility and transparency in meta-analysis, and provide guidance on how to report results effectively. The book is intended to be read in a linear fashion, starting with the basics of R and meta-analysis, and then moving on to more advanced topics. The authors also provide a list of symbols and a glossary of terms to help readers understand the concepts. The book is written for a general audience, with no prior knowledge of R or statistical analysis required. The authors believe that meta-analysis is a powerful tool for synthesizing research evidence and