On the power of multivariate latent growth curve models to detect correlated change.
- 1 January 2006
- journal article
- Published by American Psychological Association (APA) in Psychological Methods
- Vol. 11 (3), 244-252
- https://doi.org/10.1037/1082-989x.11.3.244
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.Keywords
Funding Information
- Deutsche Forschungsgemeinschaft
- Georgia Institute of Technology
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