The last component of the LISMOD specification is the structural model that describes the relationship between and , as shown in the following equation:
In this equation, is endogenous (dependent) and is exogenous (independent). Variables in can have effects among themselves. Their effects are specified in the matrix. The effects of on are specified in the matrix. Finally, the error term for the structural relationships is denoted by , which is independent of .
There are four model matrices assumed in the structural model. and are matrices for the effects of variables. In addition, matrix denotes the covariance matrix for the error term , and matrix denotes the covariance matrix of .
For the career aspiration data, you use the following MATRIX statements for the structural model:
matrix _beta_ [2,1],[1,2]; matrix _gamma_ [1,1 to 4], [2,3 to 6]; matrix _psi_ [2,1]; matrix _phi_ [1,1] = 6 * 1.0;
In Figure 17.52, initial _BETA_ and _GAMMA_ matrices are shown.
Figure 17.52: Career Aspiration Analysis 3: Initial Structural Equations
In Figure 17.52, the _BETA_ matrix contains information about the relationships among the variables (ETA= variables). Both the row and column variables of the _BETA_ matrix refer to the list of variables. The row variables receive effects from the column variables. You specify two parameters in the _BETA_ matrix:
element [2,1] is the effect of R_Amb
on F_Amb
, and element [2,1] is the effect of F_Amb
on R_Amb
. Other effects are fixed zeros in this matrix.
The _GAMMA_ matrix contains information about the relationships between the variables (ETA= variables) and the variables (XI= variables). The row variables are the variables, which receive effects from the column variables. You specify eight free parameters in this matrix. These eight parameters represent the eight path coefficients
from (the intelligence and environment factors) to the variables (the ambition factors), as shown in the path diagram in Figure 17.47. A shortcut in the MATRIX statement syntax for the _GAMMA_ matrix has been used. That is, [1, 1 to 4]
means the [1,1], [1,2], [1,3], and [1,4] elements, and [2, 3 to 6]
means the [2,3], [2,4], [2,5], and [2,6] elements. All these elements are free parameters in the model and free parameter
names are generated for these elements.
Figure 17.53 shows the initial _PSI_ and _PHI_ matrices.
Figure 17.53: Career Aspiration Analysis 3: Initial Variances and Covariances
Initial _PHI_ Matrix  

f_rpa  f_riq  f_rses  f_fses  f_fiq  f_fpa  
f_rpa 







f_riq 







f_rses 







f_fses 







f_fiq 







f_fpa 







NOTE: Parameters with prefix '_Add' are added by PROC CALIS. 
The _PSI_ matrix contains information about the covariances of error terms for the variables, which are endogenous in the structural model. There are two variables in the model—the two ambition factors R_Amb
and F_Amb
. You specify the [2,1] element as a free parameter in the MATRIX statement for _PSI_. This means that the error covariance
between R_Amb
and F_Amb
is a free parameter to estimate in the model. In Figure 17.53, both [2,1] and [1,2] elements are named as _Parm15
because _PSI_ is a symmetric matrix. Again, the diagonal elements of this covariance matrix, which are for the error variances
of the ambition factors, are default free parameters in PROC CALIS. These parameters are named with the prefix _Add
.
Finally, the _PHI_ matrix contains information about the covariances among the exogenous latent factors in the structural model. For the _PHI_ matrix, you fix all the diagonal elements to 1 in the MATRIX statement for _PHI_. This makes the latent variable scales identified. These fixed values are echoed in the output of the initial _PHI_ matrix shown in Figure 17.53. In addition, all covariances among latent exogenous variables are set to be free parameters by default.