Speaker

Dr. Yi Feng
Assistant Professor of Quantitative Psychology
UCLA

In 2023, Dr. Feng received her PhD in Quantitative methodology from the University of Maryland and joined the Department of Psychology at UCLA as an Assistant Professor. Her work focuses on advanced quantitative methods and their application in psychology and education research, with specific interests in structural equation modeling (SEM), latent growth models, random variability modeling, power analysis/sample size determination, and causal graphical models. Her work has been published in outlets including Psychological Methods, Psychometrika, and Multivariate Behavioral Research. Her 2019 paper (with G Hancock) on latent growth models with floors, ceilings, and random knots received the Tanaka award for best paper in MBR.

Title

Ask What You Mean and Mean What You Ask: Strategic Reparameterization of Latent Growth Curve Models

Abstract

Latent growth modeling (LGM) has been widely used in longitudinal studies. Falling within the structural equation modeling (SEM) framework, LGM allows the researchers to examine individuals’ longitudinal growth in measured or latent outcome variables. Although linear growth models are most commonly seen in practice, LGM is actually a far more flexible analytical framework than that. LGM is not only able to accommodate nonlinear growth trajectories, but also the change of different functional forms over time. More importantly, it is also possible to strategically reparameterize the model, such that the growth aspects of focal research interest can be directly estimated as fixed model parameters or random coefficients. During this presentation, the SEM-based structured latent curve modeling (SLCM) approach for modeling nonlinear trajectories (Blozis, 2004, 2007) will first be introduced, followed by the general approach outlined by Preacher and Hancock (2012, 2015) that allows us to convert almost any aspect of change into a fixed or random coefficient in LGM.

Examples of model reparameterization using the above approaches will be provided, giving particular attention to strategically reparameterizing a piecewise latent growth model, which can be used to model growth trajectories that are bounded by a floor and a ceiling at the two ends of the observation period (Feng et al., 2019). With the proposed reparameterized model, researchers will be able to directly examine the transition points, floor levels, and/or ceiling levels as fixed or random coefficients. Real data examples will be presented to demonstrate the implementation of the reparameterized models.