Introduction to Structural Equation Modeling with Latent Variables

The main simplification in the path diagram is to drop all the error terms in the model. Instead, error variances are treated as residual (or partial) variances for the endogenous variables in the model or path diagram. Hence, in the path diagrams for RAM models, error variances are also represented by double-headed arrows directly attached to the endogenous variables, which is the same way you represent variances for the exogenous variables. The RAM model convention leads to a simplified representation of the path diagram for the spleen data, as shown in Figure 17.14.

Figure 17.14: Simplified Path Diagram: Spleen

Another simplification done in Figure 17.14 is the omission of the parameter labeling in the path diagram. This simplification is not a part of the RAM notation. It is just a convention in PROC CALIS that you can omit the unconstrained parameter names without affecting the meaning of the model. Hence, the parameter names beta, v_disturb, and v_factnucl are no longer necessary in the simplified path diagram Figure 17.14. As you can see, this convention makes the task of model specification considerably simpler and easier.

The following statements show the specification of the simplified path diagram in Figure 17.14:

proc calis data=spleen;
   path
      sqrtrose <=== factrose   = 1.0,
      sqrtnucl <=== factnucl   = 1.0,
      factrose <=== factnucl   ;
   pvar
      sqrtrose = 0.25,      /* error variance for sqrtrose */
      sqrtnucl = 0.25,      /* error variance for sqrtnucl */
      factrose,             /* disturbance/error variance for factrose */
      factnucl;             /* variance of factnucl */
run;

The PATH statement invokes the PATH modeling language of PROC CALIS. In the PATH modeling language, each entry of specification corresponds to either a single- or double- headed arrow specification in the path diagram shown in Figure 17.14, as explained in the following:

The estimation results are shown in Figure 17.15. Essentially, these are exactly the same estimation results as those that result from the LINEQS modeling language for the just-identified model in section Illustration of Model Identification: Spleen Data.

Notice in Figure 17.15 that the path coefficient for path "factrose <=== factnucl" is given a parameter name _Parm1, which is generated automatically by PROC CALIS. This is the same beta parameter of the LINEQS model in the section Illustration of Model Identification: Spleen Data. Also, the variance parameters _Parm2 and _Parm3 in Figure 17.15 are the same v_disturb and v_factnucl parameters, respectively, in the preceding LINEQS model.

In PROC CALIS, using parameter names to specify free parameters is optional. Parameter names are generated for free parameters by default. Or, if you choose parameter names for your own convenience, you can do so without changing the model specification. For example, you can specify the preceding PATH model equivalently by adding the desired parameter names, as shown in the following statements:

proc calis data=spleen;
   path
      sqrtrose <=== factrose   = 1.0,
      sqrtnucl <=== factnucl   = 1.0,
      factrose <=== factnucl   = beta;
   pvar
      sqrtrose = 0.25,       /* error variance for sqrtrose */
      sqrtnucl = 0.25,       /* error variance for sqrtnucl */
      factrose = v_disturb,  /* disturbance/error variance for factrose */
      factnucl = v_factnucl; /* variance of factnucl */
run;

A path diagram provides you an easy and conceptual way to represent your model, while the PATH modeling language in PROC CALIS offers you an easy way to input your path diagram in a non-graphical fashion. This is especially useful for models with more complicated path structures. See the section A Combined Measurement-Structural Model for a more elaborated example of the PATH model application.

The section Some Measurement Models provides examples of the PATH model applied to classical test theory.