SEM is a statistical technique that allows the simultaneous analysis of a series of structural equations. It is particularly useful when a dependent variable in one equation becomes an independent variable in another equation [Hair et al., 1998]. However, there has been some confusion and inconsistency as to what constitutes SEM. Hoyle [1995] defines SEM as "a comprehensive statistical approach to testing hypotheses about relations among observed and latent variables. Bollen and Long [1993] note that the use of the term structural equation modeling is often restricted to classical econometric modeling, but extended their definition of SEM to include general path analysis. It seems
that SEM may be regarded as a family of techniques (encompassing path analysis, partial least squares models, and latent variable SEM), or may be restricted to a single technique (latent variable SEM)
SEM is a statistical technique that allows the simultaneous analysis of a series of structural equations. It is particularly useful when a dependent variable in one equation becomes an independent variable in another equation [Hair et al., 1998]. However, there has been some confusion and inconsistency as to what constitutes SEM. Hoyle [1995] defines SEM as "a comprehensive statistical approach to testing hypotheses about relations among observed and latent variables. Bollen and Long [1993] note that the use of the term structural equation modeling is often restricted to classical econometric modeling, but extended their definition of SEM to include general path analysis. It seemsthat SEM may be regarded as a family of techniques (encompassing path analysis, partial least squares models, and latent variable SEM), or may be restricted to a single technique (latent variable SEM)
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