Analyzing latent state-trait and multiple-indicator latent growth curve models as multilevel structural equation modelsFrontiers in Psychology: Quantitative Psychology and Measurement
AbstractLatent state-trait (LST) and latent growth curve (LGC) models are frequently used in the analysis of longitudinal data. Although it is well-known that standard single-indicator LGC models can be analyzed within either the structural equation modeling (SEM) or multilevel (ML; hierarchical linear modeling) frameworks, few researchers realize that LST and multivariate LGC models, which use multiple indicators at each time point, can also be specified as ML models. In the present paper, we demonstrate that using the ML-SEM rather than the SL-SEM framework to estimate the parameters of these models can be practical when the study involves (1) a large number of time points, (2) individually-varying times of observation, (3) unequally spaced time intervals, and/or (4) incomplete data. Despite the practical advantages of the ML-SEM approach under these circumstances, there are also some limitations that researchers should consider. We present an application to an ecological momentary assessment study (N = 158 youths with an average of 23.49 observations of positive mood per person) using the software Mplus (Muthén and Muthén, 1998–2012) and discuss advantages and disadvantages of using the ML-SEM approach to estimate the parameters of LST and multiple-indicator LGC models.
Citation InformationChristian Geiser, Jacob Bishop, Ginger Lockhart, Saul Shiffman, et al.. "Analyzing latent state-trait and multiple-indicator latent growth curve models as multilevel structural equation models" Frontiers in Psychology: Quantitative Psychology and Measurement Vol. 4 (2013)
Available at: http://works.bepress.com/christian-geiser/17/