An Illustration of Second-Order Latent Growth Models

By G. R. Hancock, Wen-Ling Kuo, F. Lawrence. 2001

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A Comparison of Latent Growth Models for Constructs Measured by Multiple Items (2007, methodology): “Hancock et al. (2001) pointed out that the curve-of-factors model has the advantage of creating a theoretically error-free construct for growth modeling, instead of using composites of observed variables, which are contaminated by measurement error.”
Structural equation modeling: strengths, limitations, and misconceptions. (2005, methodology): “Higher-order growth models such as factor-of-curve and curve-of-factor models (e.g., Hancock et al. 2001) extend these capabilities and address the critical issue of the optimal level of the diagnostic or dimensional hierarchy for addressing questions pertaining to etiology, treatment, or…”
Examining the Relations Between Executive Function, Math, and Literacy During the Transition to Kindergarten: A Multi-Analytic Approach (2017, methodology): “Latent intercepts for EF were also fixed to zero to identify the growth component of the model (see also Figure 1 in Hancock et al., 2001).”
Investigating population heterogeneity with factor mixture models. (2005, unknown): “To compare growth trajectories of latent classes with respect to factors that do not contain measurement error, it is necessary to analyze multivariate outcomes at each time point ( Hancock, Kuo, & Lawrence, 2001 ).”