A Comparison of Latent Growth Models for Constructs Measured by Multiple Items

Univariate latent growth modeling (LGM) of composites of multiple items (e.g., item means or sums) has been frequently used to analyze the growth of latent constructs. This study evaluated whether LGM of composites yields unbiased parameter estimates, standard errors, chi-square statistics, and adequate fit indexes. Furthermore, LGM was compared with 2 alternatives: LGM with fixed error variances and the curve-of-factors model (McArdle, 1988 McArdle, J. J. 1988. “Dynamic but structural equation modeling of repeated measures data”. In Handbook of multivariate experimental psychology, , 2nd ed., Edited by: Nesselroade, J. R. and Cattell, R. B. 561–614. New York: Plenum. [Crossref] , [Google Scholar]), which is a multivariate latent growth model. It was found that the 2 univariate models only yield adequate results when the items are essentially tau-equivalent and there is strict factorial invariance. The curve-of-factors model was found to produce adequate results in all conditions, but it usually requires large sample sizes.