Overview of PROC CALIS

The section Overview: CALIS Procedure gives you an overall picture of the CALIS procedure but without the details.

Changes and Enhancements

Read the section Changes and Enhancements if you have previous experience with PROC CALIS or PROC TCALIS (experimental in SAS/STAT 9.2). The SAS/STAT 9.22 version of PROC CALIS enhances the previous version of PROC CALIS by incorporating the features and functionalities of the TCALIS procedure. Some of features in PROC TCALIS are further enhanced or modified in this version of PROC CALIS. If you have not used PROC CALIS previously, you can skip this section.

Basic Model Specification

The structural equation example in the section Getting Started: CALIS Procedure provides the starting point to learn the basic model specification. You learn how to represent your theory by using a path diagram and then translate the diagram into the PATH model for PROC CALIS to analyze. Because the PATH modeling language is new, this example is useful whether or not you have previous experience with PROC CALIS. The PATH model is specified in the section PATH Model. The corresponding results are shown and discussed in Example 25.15.

After you learn about the PATH modeling language and an example of its application, you can do either of the following:

• You can continue to learn more modeling languages in the section Getting Started: CALIS Procedure.

• You can skip to the section Syntax Overview for an overview of the PROC CALIS syntax and learn other modeling languages at a later time.

You do not need to learn all of the modeling languages in PROC CALIS. Any one of the modeling languages (LINEQS, LISMOD, PATH, or RAM) is sufficient for specifying a very wide class of structural equation models. PROC CALIS provides different kinds of modeling languages because different researchers might have previously learned different modeling languages or approaches. To get a general idea about different kinds of modeling languages, the following subsections in the Getting Started: CALIS Procedure section are useful:

• LINEQS: Section LINEQS Model

• RAM: Section RAM Model

• LISMOD: Section LISMOD Model

• FACTOR: Section A Factor Model Example

• MSTRUCT: Section Direct Covariance Structures Analysis

After studying the examples in the Getting Started: CALIS Procedure section, you can strengthen your understanding of the various modeling languages by studying more examples such as those in section Examples: CALIS Procedure. Unlike the examples in the Getting Started: CALIS Procedure section, the examples in the Examples: CALIS Procedure section include the analysis results in addition to the explanations of the model specifications.

You can start with the following two sets of basic examples:

• MSTRUCT model examples
  The basic MSTRUCT model examples demonstrate the testing of covariance structures directly on the covariance matrices. Although the MSTRUCT model is not the most common structural equation models in applications, these MSTRUCT examples can help you understand the basic form of covariance structures and the corresponding specifications in PROC CALIS.

• PATH model examples
  The basic PATH model examples demonstrate how you can represent your model by path diagrams and by the PATH modeling language. These examples show the most common applications of structural equation modeling.

The following is a summary of the basic MSTRUCT model examples:

• Example 25.1, Estimating Covariances and Correlations shows how you can estimate the covariances and correlations with standard error estimates for the variables in your model. The model you fit is a saturated covariance structure model.

• Example 25.2, Estimating Covariances and Means Simultaneously extends Example 25.1 to include the mean structures in the model. The model you fit is a saturated mean and covariance structure model.

• Example 25.3, Testing Uncorrelatedness of Variables shows a very basic covariance structure model, in which the covariance structures can be specified directly. The variables in this model are uncorrelated. You learn how to specify the covariance pattern directly.

• Example 25.4, Testing Covariance Patterns extends Example 25.3 to include other covariance structures that you can specify directly.

The following is a summary of the basic PATH model examples:

• Example 25.5, Linear Regression Model shows how you can fit a linear regression model with the PATH modeling language of PROC CALIS. This example also introduces the path diagram representation of "causal" models. You compare results obtained from PROC CALIS and from the REG procedure, which is designed specifically for regression analysis.

• Example 25.6, Multivariate Regression Models extends Example 25.5 in several different ways. You fit covariance structure models with more than one predictor, with direct and indirect effects. This example also discusses how you can choose the "best" model for your data.

• Example 25.7, Measurement Error Models explores the case where the predictor in simple linear regression is measured with error. The concept of latent true score variable is introduced. You use PROC CALIS to fit a simple measurement error model.

• Example 25.8, Testing Specific Measurement Error Models extends Example 25.7 to test special measurement error models with constraints. By using PROC CALIS, you can constrain your measurement error models in many different ways. For example, you can constrain the error variances or the intercepts to test specific hypotheses.

• Example 25.9, Measurement Error Models with Multiple Predictors extends Example 25.7 to include more predictors in the measurement error models. The measurement errors in the predictors can be correlated in the model.

More elaborate examples about the MSTRUCT and PATH models are listed as follows:

• Example 25.15, Path Analysis: Stability of Alienation shows you how to specify a simple PATH model and interpret the basic estimation results. The results are shown in considerable detail. The output and analyses include: a model summary, an initial model specification, an initial estimation method, an optimization history and results, residual analyses, residual graphics, estimation results, squared multiple correlations, and standardized results.

• Example 25.17, Direct Covariance Structures Model shows you how to fit your covariance structures directly on the covariance matrix by using the MSTRUCT modeling language. You also learn how to use the FITINDEX statement to create a customized model fit summary and how to save the fit summary statistics into an external file.

• Example 25.19, Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis uses the MSTRUCT modeling language to illustrate a simple multiple-group analysis. You also learn how to use the ODS SELECT statement to customize your printed output.

• Example 25.21, Testing Competing Path Models for the Career Aspiration Data illustrates how you can fit competing models by using the OUTMODEL= and INMODEL= data sets for transferring and modifying model information from one analysis to another. This example also demonstrates how you can choose the best model among several competing models for the same data.

After studying the PATH and MSTRUCT modeling languages, you are able to specify most commonly used structural equation models by using PROC CALIS. To broaden your scope of structural equation modeling, you can study some basic examples that use the FACTOR and LINEQS modeling languages. These basic examples are listed as follows:

• Example 25.10, Measurement Error Models Specified As Linear Equations explores another way to specify measurement error models in PROC CALIS. The LINEQS modeling language is introduced. You learn how to specify linear equations of the measurement error model by using the LINEQS statement. Unlike the PATH modeling language, in the LINEQS modeling language, you need to specify the error terms explicitly in the model specification.

• Example 25.11, Confirmatory Factor Models introduces a basic confirmatory factor model for test items. You use the FACTOR modeling language to specify the factor-variable relationships.

• Example 25.12, Confirmatory Factor Models: Some Variations extends Example 25.11 to include some variants of the confirmatory factor model. With the flexibility of the FACTOR modeling language, this example shows how you fit models with parallel items, tau-equivalent items, or partially parallel items.

More advanced examples that use the LINEQS and FACTOR modeling languages are listed as follows:

• Example 25.13, The Full Information Maximum Likelihood Method shows how you can use the full information maximum likelihood (FIML) method to estimate your model when your data contain missing values. This example shows that the full information maximum likelihood method makes the maximum use of the available information from the data, as compared with the default ML (maximum likelihood) methods.

• Example 25.14, Comparing the ML and FIML Estimation discusses the similarities and differences between the ML and FIML estimation methods as implemented in PROC CALIS. It uses an empirical example to show how ML and FIML obtain the same estimation results when the data do not contain missing values.

• Example 25.16, Simultaneous Equations with Mean Structures and Reciprocal Paths is an econometric example that shows you how to specify models using the LINEQS modeling language. This example also illustrates the specification of reciprocal effects, the simultaneous analysis of the mean and covariance structures, the setting of bounds for parameters, and the definitions of metaparameters by using the PARAMETERS statement and SAS programming statements. You also learn how to shorten your output results by using some global display options such as the PSHORT and NOSTAND options in the PROC CALIS statement.

• Example 25.18, Confirmatory Factor Analysis: Cognitive Abilities uses the FACTOR modeling language to illustrate confirmatory factor analysis. In addition, you use the MODIFICATION option in the PROC CALIS statement to compute LM test indices for model modifications.

• Example 25.22, Fitting a Latent Growth Curve Model is an advanced example that illustrates the use of structural equation modeling techniques for fitting latent growth curve models. You learn how to specify random intercepts and random slopes by using the LINEQS modeling language. In addition to the modeling of the covariance structures, you also learn how to specify the mean structure parameters.

If you are familiar with the traditional Keesling-Wiley-Jöreskog measurement and structural models (Keesling; 1972; Wiley; 1973; Jöreskog; 1973) or the RAM model (McArdle; 1980), you can use the LISMOD or RAM modeling languages to specify structural equation models. The following example shows how to specify these types of models:

• Example 25.20, Illustrating Various General Modeling Languages extends Example 25.15, which uses the PATH modeling language, and shows how to use the other general modeling languages: RAM, LINEQS, and LISMOD. These modeling languages enable you to specify the same path model as in Example 25.15 and get equivalent results. This example shows the connections between the general modeling languages supported in PROC CALIS. A good understanding of Example 25.15 is a prerequisite for this example.

Once you are familiar with various modeling languages, you might wonder which modeling language should be used in a given situation. The section Which Modeling Language? provides some guidelines and suggestions.

Syntax Overview

The section Syntax: CALIS Procedure shows the syntactic structure of PROC CALIS. However, reading the Syntax: CALIS Procedure section sequentially might not be a good strategy. The statements used in PROC CALIS are classified in the section Classes of Statements in PROC CALIS. Understanding this section is a prerequisite for understanding single-group and multiple-group analyses in PROC CALIS. Syntax for single-group analyses is described in the section Single-Group Analysis Syntax, and syntax for multiple-group analyses is described in the section Multiple-Group Multiple-Model Analysis Syntax.

You might also want to get an overview of the options in the PROC CALIS statement. However, you can skip the detailed listing of the available options in the PROC CALIS statement. Most of these details serve as references, so you can consult them only when you need to. You can just read the summary tables for the available options in the PROC CALIS statement in the following subsections:

• Data Set Options

• Model and Estimation Options

• Options for Fit Statistics

• Options for Statistical Analysis

• Global Display Options

• Optimization Options

Details about Various Types of Models

Several subsections in the section Details: CALIS Procedure can help you gain a deeper understanding of the various types of modeling languages, as shown in the following table:

The specification techniques you learn from the examples cover only parts of the modeling language. A more complete treatment of the modeling languages is covered in these subsections. In addition, you can also learn the mathematical models, model restrictions, and default parameterization of all supported modeling languages in these subsections.