Using SAS PROC CALIS to fit Level-1 error covariance structures of latent growth models

Cherng G. Ding, Ten Der Jane

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)765-787
Number of pages23
JournalBehavior Research Methods
Volume44
Issue number3
DOIs
StatePublished - 1 Sep 2012

Keywords

  • Error covariance structure
  • Latent growth model
  • Structural equation modeling

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