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
| Changes and Enhancements |
The following sections describe the new features of this version of PROC CALIS.
Built-In Covariance and Mean Structures
PROC CALIS now supports the fitting of some standard covariance and mean patterns by using the COVPATTERN= and the MEANPATTERN= options. These standard covariance and mean patterns are built into PROC CALIS. You can call these built-in patterns by appropriate keywords without using explicit model specifications such as the MSTRUCT and MATRIX statements. For example, you can now test the compound symmetry pattern of a covariance matrix by simply specifying the COVPATTERN=COMPSYM option. PROC CALIS then generates the compound symmetry pattern internally for model fitting. To specify the same covariance pattern in the previous version of PROC CALIS, you would need to use the MSTRUCT statement and specify the parameters of the covariance pattern in the MATRIX statement. Another example is using the COVPATTERN=EQCOVMAT option to test the equality of covariance matrices among independent groups. See the COVPATTERN= and MEANPATTERN= options for details about the supported covariance and mean patterns.
Covariance and Mean Structure Analysis with the COSAN Model
PROC CALIS now supports covariance and mean structure analysis in the COSAN model. You can specify the central mean vector in each term of the mean structure formula. See the COSAN statement and the section The COSAN Model for details.
Extended PATH Modeling Language
You can specify variances, covariances, means, and intercepts as paths in the PATH statement. The syntax enables you to map all the parameters in the path diagram to the PATH statement specification. See the PATH statement for details. Even if you specify variances, covariances, means, or intercepts in the PVAR, PCOV, and MEAN statements (but not in the path statement), you can still display these parameter estimates as paths in the output table for the regular path effect (coefficient) estimates by using the EXTENDPATH option.
Full Information Maximum Likelihood Method
PROC CALIS implements the full information maximum likelihood method (FIML) for treating data with random missing values. The FIML method uses all the available information from the data set, including observations with missing values, so that it is statistically more efficient than the ML (maximum likelihood) method (as implemented in PROC CALIS). You can use METHOD=FIML to invoke the FIML method. In addition to the estimation, the FIML method also provides detailed analysis of the missing patterns such as the coverage statistics of the sample moments, frequencies and proportions of the missing patterns, and the descriptive statistics of the missing patterns. You can use new options MAXMISSPAT=, NOMISSPAT, and TMISSPAT= to control the output of missing patterns analysis.
Improved RAM Model Specification
You can now specify the variable list explicitly in the VAR= option of the RAM statement. This variable list is useful to make immediate references of the variables (manifest or latent) in the model. The mean structure specification of the RAM model is also supported. See the RAM statement and the section The RAM Model for details.
Unnamed Free Parameter Specification
You can specify free parameters in all models without using explicit parameter names (that is, unnamed free parameters). This makes your model specification more efficient. For example, in the PATH statement, you can specify only the paths without using the parameter names for the path effects (coefficients). PROC CALIS generates the parameter names automatically. However, you can also input the parameter names whenever it is necessary (for example, for setting parameter constraints). Unnamed free parameters specification is supported in all modeling languages. For details, see the syntax of the following statements: COV, FACTOR, LINEQS, MATRIX, MEAN, PATH, PCOV, PVAR, RAM, and VARIANCE.
Structural Equation Modeling Application
The Structural Equation Modeling Application is a graphical user interface to structural equation modeling techniques. You can specify models in graphical form to represent the hypothesized relationships among the variables. It is accessed from JMP software and uses the CALIS procedure for its computations.
The application enables you to define model variables in a path diagram by dragging data set variables to the diagram and to define the relationship between the model variables by using an arrow tool. You can move the variables to arrange the path diagram exactly the way you want. You can easily make a copy of a model, modify it, and analyze the new model, and you can compare several models with appropriate fit statistics. Finally, you can save the model specifications and the results for later use.
The Structural Equation Modeling Application provides access to a subset of the capabilities in the CALIS procedure. It supports mainly the PATH model specification through the path diagram interface. It does not support many other advanced features in PROC CALIS. For example, multiple-group analysis and full information maximum likelihood estimation are not available in the Structural Equation Modeling Application. For details, see The Structural Equation Modeling Application.