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Full Description
Elements of Structural Equation Models (SEMs) blends theoretical foundations with practical applications, serving as both a learning tool and a lasting reference. Synthesizing material from diverse sources, including the author's own contributions, it provides a rigorous yet accessible guide for graduate students, faculty, and researchers across social, behavioral, health, and data sciences. The book covers essential SEM concepts - model assumptions, identification, estimation, and diagnostics - while also addressing advanced topics often overlooked, such as Bayesian SEMs, model-implied instrumental variables, and categorical variables. Readers will gain insights into missing data, longitudinal models, and comparisons with Directed Acyclic Graphs (DAGs). By presenting complex technical content in a clear, structured way, this authoritative resource deepens readers' understanding of SEMs, making it an indispensable guide for both newcomers and experts seeking a definitive treatment of the field.
Contents
1. Introduction; 2. Building blocks and tools; 3. Multiple regression as a structural equation model; 4. Simultaneous equation models; 5. Confirmatory factor analysis; 6. Latent variable structural equation models; 7. Structural equation models with categorical indicators and outcomes; Afterword.
