Power of Latent Growth Curve Models to Detect Piecewise Linear Trajectories

Evaluating the Power of Latent Growth Curve Models to Detect Individual Differences in Change

On the power of multivariate latent growth curve models to detect correlated change.

We evaluated the statistical power of single-indicator latent growth curve models (LGCMs) to detect correlated change between two variables (covariance of slopes) as a function of sample size, number of longitudinal measurement occasions, and reliability (measurement error variance). Power approximations following the method of Satorra and Saris (1985) were used to evaluate the power to detect ...

Piecewise Linear–Linear Latent Growth Mixture Models With Unknown Knots

Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear–linear latent growth mixture model (LGMM) for describing segmented change of individual behavior over time where the data co...

Latent Growth Curve Models

Latent Growth Mixture Models to estimate PTSD trajectories

S tatistical models to estimate individual change over time and to investigate the existence of latent trajectories, where individuals belong to trajectories that are unobserved (latent), are becoming ever more popular. Such models are called Latent Growth Mixture Models (LGMM; Muthén & Muthén, 2000) and are often applied to estimate posttraumatic stress (PTSD) trajectories across several month...