SEM is an extension of the General[[general Linearlinear Modelmodel]] (GLM) that simultaneously estimates relationships between multiple independent and dependent variables, in the case of a structural model and/or multiple observed and latent variables, in the case of confirmatory factor analysis. SEM is best applied to theory testing, as opposed to the more exploratory areas of theory development.▼
* What is Structural Equation Modeling (SEM)?
▲SEM is an extension of the General Linear Model (GLM) that simultaneously estimates relationships between multiple independent and dependent variables, in the case of a structural model and/or multiple observed and latent variables, in the case of confirmatory factor analysis. SEM is best applied to theory testing, as opposed to the more exploratory areas of theory development.
* Why SEM?
SEM has several important advantages over ordinary least squares (OLS) regression. These include:
-SEM allows for multiple dependent variable, whereas OLS regressions allows only a single dependent variable.
-SEM has more flexible assumptions that does OLS regression, although the assumption of multivariate normality must be met for SEM.
-SEM allows simultaneous tests of multiple groups.
* Types of SEM techniques
* How is SEM same and different from other statistical techniques?
- SEM is similar to path analysis. In fact, path analysis can be thought of as a special case of SEM in which each latent construct has only a single indicator. This analysis is said to be conducted at the "scale level", whereas SEM is conducted at the "item level".
