*First Bayesian SEM book specifically for social science researchers, not advanced statisticians; a strong background in calculus is not needed. *Engaging, worked-through examples help highlight problems that can arise during estimation, explore solutions, and provide guidance for writing up findings. *User-friendly features include take-home points, tips, warnings, sample data analysis plans, and more. *Each chapter contains excerpts of annotated code in both Mplus and R; the companion website supplies datasets, code, and output for all of the book's examples.