Structural equation modeling: Difference between revisions

Revision as of 21:23, 10 April 2006

Revision as of 23:42, 19 April 2006

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* Basic steps in performing SEM analysis

# '''Model specification'''—Since SEM is a confirmatory technique, it is imperative that the model is specified correctly based on the type of analysis that the modeller is attempting to confirm. There are usually two main parts to SEM: the ''structural model'' showing dependencies between latent and exogeneous variables, and the ''measurement model'' showing the relations between the latent variables and their indicators. Confirmatory [[factor analysis]] models, for example, contain only contain the measurement part; while linear regresionregression can be viewed as an SEM that only has the structural part. Specifying the model delineates relationships between variables that are thought to be related (and therefore want to be 'free' to vary) and those relationships between variables that already have an estimated relationship, which can be gathered from previous studies (these relationships are 'fixed' in the model).

# '''Estimation of free parameters'''—parameter estimation is made comparing the actual variance/covariance matrices representing the relationships between variables and the estimated variance/covariance matrices of the best fitting model. This is obtained through numerical maximization of a ''fit criterion'' as provided by maximum likelihood, weighted least squares or asymptotically distribution free methods. This is best accomplished by using a specialized SEM analysis program, such as AMOS, EQS, LISREL, Mplus, SAS PROC CALIS.

# '''Assessment of fit'''— Using an SEM analysis program, one can compare the estimated matrices representing the relationships between variables in the model to the actual matrices. Individual factors within the model are also examined within the estimated model in order to see how well the proposed model fits the driving theory.