The Impact of Measurement Noninvariance on Latent Change Score Modeling: A Monte Carlo Simulation Study
Document Type
Article
Publication Date
2020
Keywords
longitudinal data, latent change score model, dual change model, longitudinal measurement invariance
Digital Object Identifier (DOI)
https://doi.org/10.1080/10705511.2020.1711759
Abstract
Measurement invariance (MI) overtime is required for meaningful interpretation of changes in the latent change score model (LCSM). In this simulation study, we investigate the impact of measurement noninvariance on the estimation of LCSM when proportional change model, dual change model, and their bivariate versions are used with mean composites or MI-assumed measurement models. The results show that even noninvariance in one item could result in severe bias in the LCSM parameter estimates and false statistical inferences (e.g., Type I error). However, the impact depends on the simulation factors, fitted models, and estimated parameters. For example, the intercept-factor mean is biased in proportional change model but not in dual change model. In bivariate LCSM the parameter estimates of only the noninvariant variable are biased except coupling effects. Model fit indices such as RMSEA and SRMR are insensitive to the ignored noninvariance. Discussions and implications of findings are presented.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Structural Equation Modeling: A Multidisciplinary Journal, in press
Scholar Commons Citation
Kim, Eunsook; Wang, Yan; and Liu, Siyu, "The Impact of Measurement Noninvariance on Latent Change Score Modeling: A Monte Carlo Simulation Study" (2020). Educational and Psychological Studies Faculty Publications. 219.
https://digitalcommons.usf.edu/esf_facpub/219
