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Voelkle, Manuel C.; Oud, Johan H. L.; von Oertzen, Timo; Lindenberger, Ulman – Structural Equation Modeling: A Multidisciplinary Journal, 2012
This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary "T" and "N" by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time…
Descriptors: Structural Equation Models, Maximum Likelihood Statistics, Computation, Factor Analysis
Hertzog, Christopher; von Oertzen, Timo; Ghisletta, Paolo; Lindenberger, Ulman – Structural Equation Modeling: A Multidisciplinary Journal, 2008
We evaluated the statistical power of single-indicator latent growth curve models to detect individual differences in change (variances of latent slopes) as a function of sample size, number of longitudinal measurement occasions, and growth curve reliability. We recommend the 2 degree-of-freedom generalized test assessing loss of fit when both…
Descriptors: Sample Size, Error of Measurement, Individual Differences, Statistical Analysis