The definitive one-stop resource on structural equation modeling (SEM) from leading methodologists is now in a significantly revised second edition, with 60% new material. Twenty-three new chapters cover model selection, bifactor models, item parceling, multitrait-multimethod models, exploratory SEM, mixture models, SEM with small samples, and more. The book moves from fundamental SEM topics (causality, visualization, assumptions, estimation, model fit, and managing missing data); to major model types focused on unobserved causes of covariance between observed variables; to more complex, specialized applications. Chapters provide conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with the reader's data. The expanded companion website presents full data sets, code, and output for many of the chapters, as well as bonus selected chapters from the prior edition. New to This Edition • Chapters on additional topics not mentioned above: SEM-based meta-analysis, dynamic SEM, machine-learning approaches, and more. • Chapters include computer code associated with example analyses (in Mplus and/or the R package lavaan), along with written descriptions of results.