At the intermediate level, you learn to minimize your mistakes in model specification and to establish more sophisticated modeling techniques. The following topics in the Details: CALIS Procedure section or elsewhere can help:

• The section Naming Variables and Parameters summarizes the naming rules and conventions for variable and parameter names in specifying models.

• The section Setting Constraints on Parameters covers various techniques of constraining parameters in model specifications.

• The section Automatic Variable Selection discusses how PROC CALIS treats variables in the models and variables in the data sets. It also discusses situations where the VAR statement specification is deemed necessary.

• The section Computational Problems discusses computational problems that occur quite commonly in structural equation modeling. It also discusses some possible remedies of the computational problem.

• The section Missing Values describes the default treatment of missing values.

• The statements REFMODEL and RENAMEPARM are useful when you need to make references to well-defined models when specifying a "new" model. See Example 25.25 for an application.

Revisit topics and examples covered at the basic level, as needed, to help you better understand the topics at the intermediate level.

You can also study the following more advanced examples:

• Example 25.23, Higher-Order and Hierarchical Factor Models is an advanced example for confirmatory factor analysis. It involves the specifications of higher-order and hierarchical factor models. Because higher-order factor models cannot be specified by the FACTOR modeling language, you need to use the LINEQS model specification instead. A second-order factor model and a bifactor model are fit. Linear constraints on parameters are illustrated by using the PARAMETERS statement and SAS programming statements. Relationships between the second-order factor model and the bifactor model are numerically illustrated.

• Example 25.24, Linear Relations among Factor Loadings is an advanced example of a first-order confirmatory factor analysis that uses the FACTOR modeling language. In this example, you learn how to use the PARAMETERS statement and SAS programming statements to set up dependent parameters in your model. You also learn how to specify the correlation structures for a specific confirmatory factor model.

• Example 25.25, Multiple-Group Model for Purchasing Behavior is a sophisticated example of analyzing a path model. The PATH modeling language is used. In this example, a two-group analysis of mean and covariance structures is conducted. You learn how to use the REFMODEL statement to reference properly defined models and the SIMTESTS statement to test a priori simultaneous hypotheses.

• Example 25.26, Fitting the RAM and EQS Models by the COSAN Modeling Language introduces the COSAN modeling language by connecting it with general RAM and EQS models. The model matrices of the RAM or EQS model are described. You specify these model matrices and the associated parameters in the COSAN modeling language.

• Example 25.27, Second-Order Confirmatory Factor Analysis constructs the covariance structure model of the second-order confirmatory factor model. You define the model matrices by using the COSAN modeling language.

• Example 25.28, Linear Relations among Factor Loadings: COSAN Model Specification shows how you can set linear constraints among model parameters under the COSAN model.

• Example 25.29, Ordinal Relations among Factor Loadings shows how you can set ordinal constraints among model parameters under the COSAN model.

• Example 25.30, Longitudinal Factor Analysis defines the covariance structures of a longitudinal factor model and shows how you can specify the covariance structure model with the COSAN modeling language.