|The TCALIS Procedure|
The VAR statement lists the numeric variables to be analyzed. It is one of the subsidiary group specification statements. You can use the VAR statement no more than once within the scope of each GROUP or the PROC TCALIS statement.
Variables in the VAR statement of the associated group are included in the corresponding model if the model is one of the following types: LINEQS, PATH, or RAM. Any VAR variables that are not mentioned in the model specification by using the main and subsidiary model specification statements are treated as exogenous variables in the model. This also means that for these models the use of the VAR statement in the associated groups is not necessary when all observed variables intended for analysis are already mentioned in the main and subsidiary model specification statements. Given the model specification, PROC TCALIS finds the set of observed variables for analysis automatically. You need the VAR statement only when you want to force the inclusion of those observed variables not mentioned in the model specification.
For the FACTOR model, the VAR variables of the associated groups are included in the model only for an exploratory factor analysis. The VAR statement is ignored for a confirmatory factor analysis, in which the observed variables can be defined only through the factor-variable-relations in the FACTOR statement.
The VAR statement of the associated group is ignored when the corresponding model is of either the LISMOD or MSTRUCT type. The observed variables for these two types of models must be defined in the variable lists of the LISMOD or MSTRUCT statement. For the LISMOD model, each observed variable for analysis is defined in either the YVAR= or XVAR= option of the LISMOD statement. For the MSTRUCT model, all observed variables for analysis are defined in the VAR= option of the MSTRUCT statement.
The VAR statement should not be confused with the PARAMETERS statement. In the PARAMETERS statement, you specify additional parameters in the model. Parameters are population quantities that characterize the functional relationships, variations, or covariation among variables. Unfortunately, parameters are sometimes referred to as variables in the optimization context. You have to make sure that all variables specified in the VAR statement correspond to the variables in the input data set, while the parameters specified in the PARAMETERS statement are population quantities that characterize distributions of the variables and their relationships.
Note: This procedure is experimental.