Structural equation modeling (SEM) refers to the use of a general framework for linear multivariate statistical analysis that includes as special cases less general models, such as linear regression, factor analysis, and path analysis. Researchers can use SEM in a hypothetico-deductive context to test complex hypotheses or in an inductive context to estimate parameter values (effect sizes). For example, one might test a model of job performance or assume such a model to estimate effect sizes of different explanatory variables. Somewhat more controversially, researchers can also use SEM as an exploratory method for hypothesis generation. SEM applies equally well to experimental, quasi-experimental, and passive observation research designs. Like other statistical models, SEM can facilitate causal inference, although nothing inherent to SEM requires a causal interpretation. ...

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