Structural Equation Modeling

Peter Schmidt & Johannes Herrmann

Structural equation modeling (SEM) is a very general statistical approach for modeling and estimating data. It is seen as a combination of factor analysis and regression or path analysis. These procedures are regarded as special cases of SEM. It contains, in addition, classical multivariate techniques such as analysis of variance, analysis of covariance, dummy regression, and canonical correlation as special cases. A structural equation model contains latent variables that should correspond to theoretical constructs from substantive theory and their reflective indicators or items that form the measurement model. The relationships between the latent variables (constructs and factors) and their indicators (observed variables) are quantified by the corresponding factor loadings. The regression coefficients between the latent variables (structural relations) take random and nonrandom measurement error into account and are, therefore, not biased. This part of the model is called “structural model” and represents the underlying theory. This entry presents some of ...

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