Stressing causation as a functional relationship between variables, this book presents the fundamentals of structural equation modeling, including concepts from Bayesian graph theory, path tracing rules for variances and covariances, equivalent models, and instrumental variables. It provides applications to confirmatory factor analysis, multilevel models, longitudinal models, and nonrecursive models. In addition, the author discusses parameter estimation beyond the basics of nonlinear optimization algorithms; addresses polychoric and polyserial correlation; and offers an extensive survey of various goodness-of-fit indices, evaluating their limitations and strengths.