ABSTRACT
The purpose of this chapter is to offer a general, necessarily brief and nontechnical, overview of structural equation modeling (SEM)—a methodology representing the second generation of multivariate analysis. Unlike the statistical tools of the first generation, exemplified by such techniques as cluster analysis, multivariate regression, principal component analysis (PCA), and others, SEM allows for answering multiple interrelated research questions within a single analysis. Use of SEM allows researchers to posit the presence of the relationships between the multiple unobserved, or latent, variables, where every latent variable is associated with multiple observed variables, often called indicators or measures. The corresponding basic structure of SEM is illustrated by Figure 8.1.
