Providing software solutions since 1976
Capabilities
- Analysis of Variance
- Bayesian Analysis
- Categorical Data Analysis
- Cluster Analysis
- Descriptive Statistics
- Distribution Analysis
- Discriminant Analysis
- Exact Methods
- Group Sequential Design and Analysis
- Market Research
- Missing Value Imputation
- Mixed Models
- Multivariate Analysis
- Nonparametric Statistics
- Power and Sample Size
- Psychometric Analysis
- Regression
- Spatial Analysis
- Structural Equations
- Survey Sampling and Analysis
- Survival Analysis
- Transformations
- SAS/STAT Procedures A-Z
The TCALIS Procedure
- fits structural equation models
- estimate parameters and test hypotheses for constrained and
unconstrained problems in various situations, including but not limited to the following:
- exploratory and confirmatory factor analysis of any order
- linear measurement-error models or regression with errors in variables
- multiple and multivariate linear regression
- multiple-group structural equation modeling with mean and covariance structures
- path analysis and causal modeling
- simultaneous equation models with reciprocal causation
- structured covariance and mean matrices
- specify models using a variety of 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
- supports equality, boundary, linear, and nonlinear parameter constraints
- provides the following statistical summaries of the data:
- covariance and mean matrices and their properties
- descriptive statistics like means, standard deviations, univariate skewness, and kurtosis measures
- multivariate measures of kurtosis
- weight matrix and its descriptive properties
- provides the following supplementary statistical analysis after a model is
fitted and accepted by the researcher:
- computing square multiple correlations and determination coefficients
- direct and indirect effects partitioning with standard error estimates
- model modification tests such as Lagrange multiplier and Wald tests
- computing fit summary indices
- computing predicted moments of the model
- residual analysis
- factor rotations
- standardized solutions with standard errors
- testing parametric functions, individually or simultaneously
- supports the following estimation methods:
- diagonally weighted least squares (DWLS, with optional weight matrix input)
- generalized least squares (GLS, with optional weight matrix input)
- maximum likelihood (ML, for multivariate normal data); this is the default method
- unweighted least squares (ULS)
- weighted least squares or asymptotically distribution-free method (WLS or ADF, with optional weight matrix input)
- can determine parameter starting values using one or any combination of the following methods:
- approximate factor analysis
- default initial values
- instrumental variable method
- matching observed moments of exogenous variables
- McDonald’s (McDonald and Hartmann 1992) method
- ordinary least squares estimation
- random number generation
- two-stage least squares estimation
- supports the following optimization algorithms:
- Levenberg-Marquardt algorithm
- trust-region algorithm
- 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)
- supports the following types of output data sets:
- parameter estimates and their covariance estimates
- fit indices and some pertinent modeling information
- model specifications and final estimates
- descriptive statistics, residuals, predicted moments, and latent variable scores regression coefficients
- weight matrices used in the modeling
- obtain separate analyses on observations in groups
- uses ODS to create a SAS data set corresponding to any table
- uses ODS Graphics to create graphs as part of its output
For further details see the SAS/STAT User's Guide:
The TCALIS Procedure
( PDF | HTML )
Examples
- Path Analysis: Stability of Alienation
- Simultaneous Equations with Mean Structures and Reciprocal Paths
- A Direct Covariance Structures Model
- Confirmatory Factor Analysis: Cognitive Abilities
- Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis
- Illustrating Various General Modeling Languages
- Fitting a Latent Growth Curve Model
- Higher-Order and Hierarchical Factor Models
- Linear Relations among Factor Loadings
- A Multiple-Group Model for Purchasing Behavior
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