Structural equation modeling (SEM) is an important and widespread analysis tool in psychology and related areas in the social sciences. A Web-of-Science search for “SEM” returns 2,058 publications from 2014 in psychology and the social sciences alone. In addition, the increasing popularity and usage of R (R Core Team, 2015) have led to a quick utilization of many R packages for SEM; lavaan (Rosseel, 2012) being one of the most popular among them. The package has been cited in 3311 publications and has undoubtedly been used for analyses and preparation in countless others. Many researchers with different skills and statistical backgrounds search for introductory books to SEM using R, such as Beaujean’s Latent Variable Modeling Using R—A Step-by-Step-Guide (Beaujean, 2014). Some may consider switching from commercial software such as Mplus (Muthén & Muthén, 1998–2012) and already have a firm grasp of the concepts behind SEM, while others may wish to use it as a textbook in graduate courses. According to the abstract, the book was primarily written as supplementary material for SEM courses, with a special focus on the practical usage of lavaan and related packages. The book consists of nine chapters and discusses popular fields of applications for structural equation models, such as mediation analysis, multiple group comparisons, longitudinal data analysis, dichotomous observed variables, missing data analysis, Monte Carlo simulation studies, and hierarchical (i.e., second-order) confirmatory factor analysis. The book is written very concisely and covers the entire material in just 205 pages. For that purpose, the author does not go into the theoretical underpinnings of SEM, but rather stays on a broad conceptual level, which makes the text easy to read.