Overview of Structural Equation Models
The Structural Equation Models (SEM) platform enables you to fit a wide variety of models that can be used to test theories of relationships among variables. The variables in the models can be observed (manifest variables) or unobserved (latent variables). Structural equation modeling is popular in the social and behavioral sciences.
By default, the platform specifies a model with means and variances for all variables. The platform then provides a model-building interface that enables you to see multiple views of the model while it is being built. It also provides some model details during the model construction process that alert you to untenable models prior to running the model.
After you fit one or more models, you can compare the fitted models and two baseline models in the Model Comparison report. The baseline models are an unrestricted model and an independence model. The unrestricted model is a fully saturated model, which fits all means, variances, and covariances of the specified Model Variables without imposing any structure on the data. The independence model fits all means and variances of the specified Model Variables. All covariances among the specified Model Variables are fixed to zero, which leads to a highly restrictive model.
The SEM platform uses the full information maximum likelihood (Finkbeiner 1979) method. This enables you to fully use all available information from the data even when there is a high proportion of observations with random missing values.
For more information about structural equation modeling, see the CALIS Procedure chapter in SAS Institute Inc. (2024a), Bollen (1989), and Kline (2016).
Note: All models in the Structural Equation Models platform are estimated with a mean structure, which means that a Constant term is included. If you do not want to place a structure on the means of the observed variables, then the means should be freely estimated as in the default model specification.