Evaluating the Power of Latent Growth Curve Models to Detect Individual Differences in Change
By C. Hertzog, Timo von Oertzen, Paolo Ghisletta, Ulman E. R. Lindenberger. 2008
138 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Sociology and Political Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/0cfd2c15041f33bc658c1f0de724aa07db96a98e
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In Management Science and Operations Research
In Sociology and Political Science
In Statistics and Probability
Citations in the corpus
- Comparison of Inverse Wishart and Separation-Strategy Priors for Bayesian Estimation of Covariance Parameter Matrix in Growth Curve Analysis (2016, methodology): “The Level 1 residual variance has been found to influence both power and Type I error of parameter estimates from growth models (e.g., Hertzog, Lindenberger, Ghisletta, & von Oertzen, 2006; Hertzog et al., 2008; Ke & Wang, 2015).”
- Planned missing designs to optimize the efficiency of latent growth parameter estimates (2014, methodology): “Hertzog et al. (2008) compared the power of several different methods for testing the significance of latent growth curve parameters and found that the most powerful method is the generalized variance test where a nested model likelihood-ratio test is used to compare the log-likelihood of the LGM…”