Business Knowledge Series course
Presented by Catherine Truxillo, Ph.D., Director, Analytical Education, Education Division, SAS
This course introduces the experienced statistical analyst to structural equation modeling (SEM) in the CALIS procedure in SAS/STAT software. The course also introduces the PATHDIAGRAM statement in the CALIS procedure, which draws path diagrams based on fitted models.
Structural equation modeling is a statistical technique that combines elements of traditional multivariate models, such as regression analysis, factor analysis, and simultaneous equation modeling. These models are often represented as matrices, equations, and/or path diagrams and can explicitly account for uncertainty in observed variables and for estimation bias due to measurement error. Competing models can be compared to one another, providing information about the complex drivers of the outcome variables of interest. Many applications of SEM can be found in the social, economic, and behavioral sciences, where measurement error and uncertain causal conditions are commonly encountered. This course does not address models containing categorical endogenous variables or multilevel SEM, as these methods are not supported in the CALIS procedure.
Learn how to
 explain a regression model in terms of a structural equation model
 compare results from the REG and CALIS procedures
 produce a path diagram of your model results
 customize a path diagram
 specify models and evaluate model fit in the CALIS procedure using the PATH input style
 specify mediation models and test for complete and partial mediation
 perform complex path analysis
 perform confirmatory factor analysis
 specify general latent variable models
 perform robust estimation for data with outliers
 perform fullinformation maximum likelihood estimation for incomplete data
 perform honest assessment to validate models.
Who should attend
Most appropriately, social, behavioral, economic, and health researchers interested in fitting complex path models and latent variable models
Formats available  Duration   
Classroom: 
2.0 days   
eLearning: 
14 hours/180 day license 

Before attending this course, you should
 have a strong background in regression modeling
 be familiar with factor analysis
 be familiar with the concepts taught in Statistics 2: ANOVA and Regression or have equivalent knowledge.