Book Review: W. H. FINCH, J. E. Bolin and K. Kelley: Multilevel Modeling Using R

Abstract

Multilevel modeling, also known as hierarchical or mixed effects modeling, is a statistical technique used for fitting nested or clustered data. Multilevel modeling aids in examining associations between variables measured at different levels of the data structure (Raudenbush & Bryk, 2002;Hox et al., 2018). In these models, residual components are introduced at each level of the structure. Multilevel modeling is a popular method of analysis and is widely used in social sciences (students nested within classrooms), natural sciences (burrowing habit across locations), physical sciences (distribution of light over the sky), and medicine (tracking drug doses over time). In recent years, there has been a lot of research on modeling clustered data for methods such as structural equation modeling (SEM), mediation analysis, mixture modeling, propensity score analysis to name a few. Due to the ubiquity of this method, all major statistical softwares offer implementations of multilevel modeling. Especially, R has become widely popular among researchers and teachers for fitting multilevel models. With the development of multilevel modeling in research and software, it is crucial that researchers acquaint themselves with the method and learn to implement it in R.There are several books on multilevel modeling that offer technical and non-technical treatment of the subject. Some books also offer guidance on fitting these models using different software. There are a couple of excellent books that include implementation of multilevel modeling in R-linear mixed effects models using R (Galecki & Burzykowski, 2013) and mixed effects models and extensions in Ecology with R (Zuur et al., 2009). Yet, this book is a welcome addition on the topic. The most important aspect of this book is the communication of the fundamentals of multilevel modeling and some of its advanced issues in a non-technical and easy to understand format. The comprehensive description of R functions and syntax makes it easier for a novice R user to fit multilevel models and understand the resulting output. The website associated with the first edition of the book (http://www.mlminr.com/) contains data sets used in the examples that can be downloaded for practice by the readers. The second edition of the book includes examples with newer packages, functions, model fit measures, and additional advanced topics, but the above-mentioned website has not been updated to accommodate the changes. However, the book contains all the necessary R codes and syntax to replicate the examples.Primary audience of the book: This book is mainly geared toward readers in need for an introduction to multilevel models as the book does not delve into advanced topics or technical details of the methodology. However, seasoned researchers and course instructors trying to include R implementations will also find this book to be useful. The book covers a broad range of themes and examples, making it easier for readers from varied background to follow the content.Structure of the book: The boo...