Structural equation modeling (SEM) is a very general statistical modeling technique widely used in the behavioral sciences . SEM provides a very general and convenient framework for statistical analysis that includes several traditional multivariate procedures, such as factor analysis, path analysis, regression analysis, discriminant analysis, and canonical correlation as special cases. The basic idea differs from the usual statistical approach of modeling individual observations. In multiple regression or ANOVA the regression coefficients or parameters of the model are based on the minimization of the sum of squared differences between the predicted and observed dependent variables. SEM approaches the data from a different perspective. Instead of considering variables individually, the emphasis is on the covariance structure. Parameters are estimated in structural equation modeling by minimizing the difference between the observed covariances and those implied by a structural or path model. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables, each of which is predicted to 'tap into' the latent variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which in theory allows the structural relations between latent variables to be accurately estimated. Structural equation models are usually represented by a set of matrix equations and visualized by graphical path diagrams.