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<H1>Structural Equation Modeling</H1></CENTER>
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<H2>Overview</H2>
<P><I>Structural equation modeling</I> (SEM) grows out of and serves purposes similar to multiple <a href="http://www2.chass.ncsu.edu/garson/pa765/regress.htm" target="_blank" >regression</A>, but in a more powerful way which takes into account the modeling of interactions, nonlinearities, correlated independents, measurement error, correlated error terms, multiple latent independents each measured by multiple indicators, and one or more latent dependents also each with multiple indicators. SEM may be used as a more powerful alternative to multiple regression, path analysis, factor analysis, time series analysis, and analysis of covariance. That is, these procedures may be seen as special cases of SEM, or, to put it another way, SEM is an extension of the general linear model (GLM) of which multiple regression is a part. </P>
<P>Advantages of SEM compared to multiple regression include more flexible assumptions (particularly allowing interpretation even in the face of multicollinearity), use of confirmatory factor analysis to reduce measurement error by having multiple indicators per latent variable, the attraction of SEM's graphical modeling interface, the desirability of testing models overall rather than coefficients individually, the ability to test models with multiple dependents, the ability to model mediating variables, the ability to model error terms, the ability to test coefficients across multiple between-subjects groups, and ability to handle difficult data (time series with autocorrelated error, non-normal data, incomplete data).
<P>SEM is usually viewed as a confirmatory rather than exploratory procedure, using one of three approaches: </P>
<P>更多：<a href="http://www2.chass.ncsu.edu/garson/pa765/structur.htm" target="_blank" >http://www2.chass.ncsu.edu/garson/pa765/structur.htm</A></P>
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