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

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

Studying Multivariate Change Using Multilevel Models and Latent Curve Models.

In longitudinal research investigators often measure multiple variables at multiple points in time and are interested in investigating individual differences in patterns of change on those variables. In the vast majority of applications, researchers focus on studying change in one variable at a time. In this article we consider methods for studying relations1.1ips between patterns of change on ...

Latent Growth Curve Models

Structured latent curve models for the study of change in multivariate repeated measures.

This article considers a structured latent curve model for multiple repeated measures. In a structured latent curve model, a smooth nonlinear function characterizes the mean response. A first-order Taylor polynomial taken with regard to the mean function defines elements of a restricted factor matrix that may include parameters that enter nonlinearly. Similar to factor scores, random coefficien...