Related Experiment Video
Updated: Jul 20, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
Published on: September 17, 2019
On the power of multivariate latent growth curve models to detect correlated change
Christopher Hertzog1, Ulman Lindenberger, Paolo Ghisletta
1School of Psychology, Georgia Institute of Technology, Atlanta, GA 30332-0170, USA. christopher.hertzog@psych.gatech.edu
Abstract:
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 slope covariances. Even with large samples (N = 500) and several longitudinal occasions (4 or 5), statistical power to detect covariance of slopes was moderate to low unless growth curve reliability at study onset was above .90. Studies using LGCMs may fail to detect slope correlations because of low power rather than a lack of relationship of change between variables. The present findings allow researchers to make more informed design decisions when planning a longitudinal study and aid in interpreting LGCM results regarding correlated interindividual differences in rates of development.
Related Concept Videos
Correlation and Regression
Longitudinal Studies
Exponential Equations for Modeling Growth
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Quadratic Models
Comparing the Survival Analysis of Two or More Groups
