Structural equation modeling (SEM) represents a methodological framework for developing and evaluating multivariate hypotheses about systems. Its emphasis, orientation, and approach are substantially different from univariate modeling (e.g., analysis of variance or the general linear model), which has served as the cornerstone of contemporary science. There is currently a substantial surge in interest in SEM because of a growing awareness that we have, up to this point, attempted to study systems primarily using methods (such as the univariate model) that were designed only for studying individual processes. Understanding systems requires the capacity to examine simultaneous influences and responses as well as direct and indirect effects. SEM has such capabilities. It also possesses many other traits that add to its utility. The goals of this workshop are to provide an overview of the rationale for working with structural equation models, provide an overview of some of the fundamental principles of SEM, and expose class members to modeling methods and issues. Students need a basic familiarity with statistics at a minimum. Students will be provided with a series of tutorials for developing their expertise.