Proc Calis
In the present article, we demonstrates the use of SAS PROC CALIS to fit
various types of Level-1 error covariance structures of latent growth
models (LGM). Advantages of the SEM approach, on which PROC CALIS is
based, include the capabilities of modeling the change over time for
latent constructs, measured by multiple indicators; embedding LGM into a
larger latent variable model; incorporating measurement models for
latent predictors; and better assessing model fit and the flexibility in
specifying error covariance structures. The strength of PROC CALIS is
always accompanied with technical coding work, which needs to be
specifically addressed. We provide a tutorial on the SAS syntax for
modeling the growth of a manifest variable and the growth of a latent
construct, focusing the documentation on the specification of Level-1
error covariance structures. Illustrations are conducted with the data
generated from two given latent growth models. The coding provided is
helpful when the growth model has been well determined and the Level-1
error covariance structure is to be identified.
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