Providing software solutions since 1976
Capabilities
- Analysis of Variance
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- Market Research
- Missing Value Imputation
- Mixed Models
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- Regression
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- Survey Sampling and Analysis
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- Transformations
- SAS/STAT Procedures A-Z
The CALIS Procedure
- fits structural equation models
- estimate parameters and test hypotheses for constrained and unconstrained problems in the following:
- multiple and multivariate linear regression
- linear measurement-error models
- path analysis and causal modeling
- simultaneous equation models with reciprocal causation
- exploratory and confirmatory factor analysis of any order
- canonical correlation
- a wide variety of other (non)linear latent variable models
- parameters are estimated using the following criteria:
- unweighted least squares (ULS)
- generalized least squares (GLS, with optional weight matrix input)
- maximum likelihood (ML, for multivariate normal data)
- full information maximum likelihood (FIML, for treating data with random missing values)
- weighted least squares (WLS, ADF, with optional weight matrix input)
- diagonally weighted least squares (DWLS, with optional weight matrix input)
- specify models using the following modeling languages:
- FACTOR—supports the input of factor-variable relations
- LINEQS—like the EQS program (Bentler 1995), uses equations to describe variable relationships
- LISMOD—utilizes LISREL (Jöreskog and Sörbom 1985) model matrices for defining models
- MSTRUCT—supports direct parameterization in the mean and covariance matrices
- PATH—provides an intuitive causal path specification interface
- RAM—utilizes the formulation of the reticular action model (McArdle and McDonald 1984)
- REFMODEL—provides a quick way for model referencing and respecification
- perform multiple-group analysis. Groups can also be fitted by multiple models simultaneously.
- specify linear and nonlinear equality and inequality constraints on the parameters with several different statements, depending on the type of input
- Lagrange multiplier test indices are computed for simple constant and equality parameter constraints and for active boundary constraints
- general equality and inequality constraints can be formulated using programming statements
- specify free unnamed parameters in all models
- offers a variety of methods for the automatic generation of initial values for the optimization process:
- two-stage least squares estimation
- instrumental variable factor analysis
- approximate factor analysis
- ordinary least squares estimation
- McDonald’s (McDonald and Hartmann 1992) method
- offers the following optimization algorithms:
- Levenberg-Marquardt algorithm (Moré, 1978)
- trust-region algorithm (Gay 1983)
- Newton-Raphson algorithm with line search
- ridge-stabilized Newton-Raphson algorithm
- various quasi-Newton and dual quasi-Newton algorithms: Broyden-Fletcher-Goldfarb-Shanno and Davidon-Fletcher-Powell, including a sequential quadratic programming algorithm for processing nonlinear equality and inequality constraints
- various conjugate gradient algorithms: automatic restart algorithm of Powell (1977), Fletcher-Reeves, Polak-Ribiere, and conjugate descent algorithm of Fletcher (1980)
- offers an analysis of linear dependencies in the information matrix (approximate Hessian matrix) that might be helpful in detecting unidentified models
- produces a SAS data set containing information about the optimal parameter estimates (parameter estimates, gradient, Hessian, projected Hessian and Hessian of Lagrange function for constrained optimization, the information matrix, and standard errors)
- produces a SAS data set containing residuals and, for exploratory factor analysis, the rotated and unrotated factor loadings
- analysis of multiple samples with equal sample size can be performed by the analysis of a moment supermatrix containing the individual moment matrices as block diagonal submatrices
- uses ODS Graphics to create graphs as part of its output
- obtain separate analyses on observations in groups
- compute weighted covariances or correlations
- uses ODS to create a SAS data set corresponding to any table
For further details see the SAS/STAT User's Guide:
The CALIS Procedure
(
PDF | HTML )
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 Two 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
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