Research Tips
Structural Equation Modeling
Structural equation modeling is a form of multivariate statistical analysis. It is grounded in the correlations among variables and builds on two other correlation-based multivariate techniques: regression analysis and factor analysis. As with regression analysis, structural equation models explore relationships among variables. As with factor analysis, structural equation models are also used to create and test measurements of latent variables (abstract concepts such as customer orientation that cannot be measured directly and so are typically examined using proxy measures formed by combining answers to multiple questions). Traditionally, researchers might use factor analysis to form variables and then regression analysis to test relationships between them, but with structural equation modeling both of these steps happen simultaneously. Researchers often estimate multiple structural models and then use statistical tests to determine which model best fits the data.
One reason structural equation modeling has become more common in recent years is that software is now available which makes it much easier to implement. Nonetheless, results from structural equation modeling may be inaccurate or misleading if there are problems with things such as sampling, measurement, or model specification, so knowledge of both the phenomena being studied and the statistical basis for the technique are important for ensuring that results are generated and interpreted appropriately.