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An overidentified model is a model for which there is more than enough information in the data to estimate the model parameters. By contrast, an underidentified model has insufficient information from the data to estimate the free parameters, and a just-identified model has just enough information to solve for the free parameters. Identification is an important part of many statistical models but is most frequently and extensively discussed in the context of structural equation modeling. In structural equation modeling, the information available from the data is contained in the observed variance–covariance matrix and the parameters consist of the freely estimated parameters of the model. A model must be just-identified or overidentified in order to estimate parameters. Overidentified models are particularly important in structural equation modeling ...

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