This course is designed for doctoral students who need a significant familiarity with those statistical techniques known collectively as "structural equation modeling (SEM)," "causal modeling," or "analysis of covariance structures," along with related techniques. SEM is a generalized statistical procedure that can be used for many purposes, such as confirmatory factor analysis, path analysis, and causal models. It is excellent for comparing groups, examining longitudinal data, and dealing with multilevel analysis. SEMs basically refer to a bunch of similar models developed by different scholars (e.g., LISREL, AMOS, PLS, MPlus, EQS, CALIS, etc.) Specifically, this course introduces the LISREL (Linear Structural Relationships) modeling techniques, which are very useful in dealing with substantive problems in behavioral sciences and marketing.

Applications of LISREL can be found almost everywhere. LISREL is powerful in areas like measurement and hypothesis testing. With respect to measurement, it is capable of examining reliability, dimensions of constructs, and validity. As for hypothesis testing, it produces parameter estimates and goodness-of-fit indices which can be used for testing of assumptions, theory verification, or even comparison of competing models. People who use survey or experimental design will find the LISREL methodology applicable to a wide range of models such as exploratory and confirmatory factor analysis, path analysis, and econometric models for time series data.