Time to criterion latent growth models.

Latent growth models, a special class of longitudinal models within the broader structural equation modeling (SEM) domain, provide researchers a framework for investigating questions about change over time; yet rarely is time itself modeled as a focal parameter of interest. In the current article, rather than treating time purely as an index of measurement occasions, the proposed Time to Criterion (T2C) model draws from Preacher and Hancock's (2012) latent growth model reparameterization guidelines to model individual variability (i.e., to treat as a random effect) in one's time to achieve a criterion level of a given outcome. As such, the T2C model also allows researchers to model predictors and distal outcomes of time, as well as benefiting more generally from the flexibility afforded by being embedded within the broader SEM framework to accommodate such real-world data issues as missingness, complex error structures, nonnormality, and nested data. In this study we derive T2C from the linear latent growth model and discuss model assumptions and interpretation. By illustrating the model using real data, we demonstrate both its utility for applied research and its implementation in conventional SEM software. We also discuss and illustrate an extension of the model for nonlinear growth. Overall, the T2C model presents a novel and interpretable growth parameterization for further understanding processes of change. (PsycINFO Database Record (c) 2019 APA, all rights reserved).