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The CALIS Procedure

Overview
Compatibility with the CALIS Procedure in SAS/STAT 9.2 or Earlier Compatibility with the TCALIS Procedure in SAS/STAT 9.2 A Guide to the PROC CALIS Documentation
Getting Started
A Structural Equation Example A Factor Model Example Direct Covariance Structures Analysis Which Modeling Language?
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 PATHDIAGRAM 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
Details
Input Data Sets Output Data Sets Default Analysis Type and Default Parameterization 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 Path Diagrams: Layout Algorithms, Default Settings, and Customization Estimation Criteria Relationships among Estimation Criteria Gradient, Hessian, Information Matrix, and Approximate Standard Errors Counting the Degrees of Freedom Assessment of Fit Case-Level Residuals, Outliers, Leverage Observations, and Residual Diagnostics Latent Variable Scores 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
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 Residual Diagnostics and Robust Estimation 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

Details: CALIS Procedure

Subsections:

Input Data Sets
Output Data Sets
Default Analysis Type and Default Parameterization
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
Path Diagrams: Layout Algorithms, Default Settings, and Customization
Estimation Criteria
Relationships among Estimation Criteria
Gradient, Hessian, Information Matrix, and Approximate Standard Errors
Counting the Degrees of Freedom
Assessment of Fit
Case-Level Residuals, Outliers, Leverage Observations, and Residual Diagnostics
Latent Variable Scores
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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