Scientific article

On the Power of Multivariate Latent Growth Curve Models to Detect Correlated Change

Published inPsychological methods, vol. 11, no. 3, p. 244-252
Publication date2006

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 about.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.

  • Analyse
  • Etude longitudinale
  • Méthodologie
  • Statistique
  • Vieillissement
Citation (ISO format)
HERTZOG, Christopher et al. On the Power of Multivariate Latent Growth Curve Models to Detect Correlated Change. In: Psychological methods, 2006, vol. 11, n° 3, p. 244–252. doi: 10.1037/1082-989x.11.3.244
Main files (1)
Article (Published version)
ISSN of the journal1082-989X

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