The CALIS Procedure
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Overview
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Getting Started
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Syntax
Classes of Statements in PROC CALIS Single-Group Analysis Syntax Multiple-Group Multiple-Model Analysis Syntax PROC CALIS Statement BOUNDS Statement BY Statement COSAN Statement COV Statement DETERM Statement EFFPART Statement FACTOR Statement FITINDEX Statement FREQ Statement GROUP Statement LINCON Statement LINEQS Statement LISMOD Statement LMTESTS Statement MATRIX Statement MEAN Statement MODEL Statement MSTRUCT Statement NLINCON Statement NLOPTIONS Statement OUTFILES Statement PARAMETERS Statement PARTIAL Statement PATH Statement PCOV Statement PVAR Statement RAM Statement REFMODEL Statement RENAMEPARM Statement SAS Programming Statements SIMTESTS Statement STD Statement STRUCTEQ Statement TESTFUNC Statement VAR Statement VARIANCE Statement VARNAMES Statement WEIGHT Statement
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Details
Input Data Sets Output Data Sets The COSAN Model The FACTOR Model The LINEQS Model The LISMOD Model and Submodels The MSTRUCT Model The PATH Model The RAM Model Naming Variables and Parameters Setting Constraints on Parameters Automatic Variable Selection Estimation Criteria Relationships among Estimation Criteria Gradient, Hessian, Information Matrix, and Approximate Standard Errors Counting the Degrees of Freedom Assessment of Fit Total, Direct, and Indirect Effects Standardized Solutions Modification Indices Missing Values and the Analysis of Missing Patterns Measures of Multivariate Kurtosis Initial Estimates Use of Optimization Techniques Computational Problems Displayed Output ODS Table Names ODS Graphics
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Examples
Estimating Covariances and Correlations Estimating Covariances and Means Simultaneously Testing Uncorrelatedness of Variables Testing Covariance Patterns Testing Some Standard Covariance Pattern Hypotheses Linear Regression Model Multivariate Regression Models Measurement Error Models Testing Specific Measurement Error Models Measurement Error Models with Multiple Predictors Measurement Error Models Specified As Linear Equations Confirmatory Factor Models Confirmatory Factor Models: Some Variations The Full Information Maximum Likelihood Method Comparing the ML and FIML Estimation Path Analysis: Stability of Alienation Simultaneous Equations with Mean Structures and Reciprocal Paths Fitting Direct Covariance Structures Confirmatory Factor Analysis: Cognitive Abilities Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis Testing Equality of Covariance and Mean Matrices between Independent Groups Illustrating Various General Modeling Languages Testing Competing Path Models for the Career Aspiration Data Fitting a Latent Growth Curve Model Higher-Order and Hierarchical Factor Models Linear Relations among Factor Loadings Multiple-Group Model for Purchasing Behavior Fitting the RAM and EQS Models by the COSAN Modeling Language Second-Order Confirmatory Factor Analysis Linear Relations among Factor Loadings: COSAN Model Specification Ordinal Relations among Factor Loadings Longitudinal Factor Analysis
- References
| GROUP Statement |
- GROUP i </ options> ;
The GROUP statement signifies the beginning of a group specification block and designates a group number for the group. All subsidiary group specification statements after a GROUP statement belong in that group until another MODEL or GROUP statement is used. The subsidiary group specification statements refer to one of the following four statements:
For example, consider the following statements:
proc calis;
var X1-X4;
group 1 / label='Women' data=women_data;
freq Z;
group 2 / label='Men' data=men_data;
partial P;
model 1 / group = 1-2;
factor N=1; /* One factor exploratory factor analysis */
run;
In the GROUP statements, two groups are defined. Group 1, labeled as 'Women', refers to the data set women_data. Group 2, labeled as 'Men', refers to the data set men_data. Both groups are fitted by an exploratory factor model defined in Model 1, as indicated in the GROUP= option of the MODEL statement. While the frequency variable Z defined in the FREQ statement is applicable only to Group 1, the partial variable P defined in the PARTIAL statement is applicable only to Group 2. However, the VAR statement, which appears before the definitions of both groups, applies globally to both Group 1 and Group 2. Therefore, variables X1–X4 are the analysis variables in the two groups.
You can set group-specific options in each GROUP statement. All but one (that is, the LABEL= option) of these options are also available in the MODEL and PROC CALIS statements. If you set these group-specific options in the PROC CALIS statement, they will apply to all groups unless you respecify them in the GROUP statement. If you set these group-specific options in the MODEL statement, they will apply to all groups that are fitted by the associated model. In general, the group-specific options are transfered from the PROC CALIS statement to the MODEL statements (if present) and then to the fitted groups. In the transferring process, options are overwritten by the newer ones. If you want to apply some group-specific options to a particular group only, you should set those options in the GROUP statement corresponding to that group.
Option Available in the GROUP Statement Only
Options Available in the GROUP and PROC CALIS Statements
These options are available in the GROUP and PROC CALIS statements:
Option |
Description |
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Specifies the input data set |
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Specifies the data set that contains the weight matrix |
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Specifies the data set for storing the statistical results |
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Specifies the data set for storing the weight matrix |
See the section Listing of PROC CALIS Statement Options for more details about these options. If you specify these options in the PROC CALIS statement, they are transferred to all GROUP statements. They might be overwritten by the respecifications in the individual GROUP statements.
Options Available in GROUP, MODEL, and PROC CALIS Statements
These options are available in the GROUP, MODEL, and PROC CALIS statements:
Option |
Description |
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Computes the skewness and kurtosis without bias corrections |
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Defines nobs by the number of error df |
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Specifies that the INWGT= data set contains the inverse of the weight matrix |
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Computes and displays kurtosis |
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Specifies the maximum number of missing patterns to display |
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Defines the number of observations (nobs) |
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Suppresses the display of missing pattern analysis |
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Displays analyzed and estimated moment matrix |
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Specifies ODS Graphics selection |
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Displays the weight matrix |
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Defines nobs by the number of regression df |
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Computes the default residuals |
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Specifies the type of residuals |
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Specifies the ridge factor for covariance matrix |
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Prints univariate statistics |
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Specifies the data proportion threshold for displaying the missing patterns |
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Specifies variance divisor |
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Specifies the penalty weight to fit correlations |
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Specifies the ridge factor for the weight matrix |
If you specify these options in the PROC CALIS statement, they are transferred to all MODEL statements. These options are overwritten by the respecifications in the individual MODEL statements. After these options are resolved in a given MODEL statement, they are transferred further to the GROUP statements of which the associated groups are fitted by the model. Again, these options might be overwritten by the respecifications in the individual GROUP statements.