Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Advanced Models and on the menu, select Structural Equation to display the Structural Equation Modeling Startup Panel.

Classic menus. On the Statistics - Advanced Linear/Nonlinear Models submenu, select Structural Equation Modeling to display the Structural Equation Modeling Startup Panel.

Structural equation modeling (SEPATH) is a very general, very powerful multivariate analysis technique that includes specialized versions of a number of other analysis methods as special cases. Major applications of structural equation modeling include causal modeling or path analysis, confirmatory factor analysis, second order factor analysis, regression models, covariance structure models, and correlation structure models.

SEPATH is a complete implementation that includes numerous advanced features: STATISTICA can analyze correlation, covariance, and moment matrices (structured means, models with intercepts). Simple or complex factor or path models can be specified via dialogs, a simple path-language, or via step-by-step wizards. The SEPATH module will compute, using constrained optimization techniques, the appropriate standard errors for standardized models, and for models fitted to correlation matrices. The results options include a comprehensive set of diagnostic statistics including the standard fit indices as well as noncentrality-based indices of fit, reflecting the most recent developments in the area of structural equation modeling. You can fit models to multiple samples (groups) and specify for each group fixed, free, or constrained (to be equal across groups) parameters. When analyzing moment matrices, these facilities allow you to test complex hypotheses for structured means in different groups. The module includes powerful Monte Carlo simulation options: you can generate (and save) data files for predefined models, based on normal or skewed distributions. Bootstrap estimates can be computed, as well as distributions for various diagnostic statistics, parameter estimates, etc. over the Monte Carlo trials. Numerous flexible graphing options are available to visualize the results (e.g., distributions of parameters) from Monte Carlo runs.

See also the Factor Analysis module for maximum likelihood factor analysis; the General Linear Models (GLM) module includes various facilities for testing custom-hypotheses, for example, to test for the equality of parameter estimates in a linear model.