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Abstract

Latent curve models have become a popular approach to the analysis of longitudinal data. At the individual level, the model expresses an individual’s response as a linear combination of what are called “basis functions” that are common to all members of a population and weights that may vary among individuals. This article uses differential calculus to define the basis functions of a latent curve model. This provides a meaningful interpretation of the unique and dynamic impact of each basis function on the individual-level response. Examples are provided to illustrate this sensitivity, as well as the sensitivity of the basis functions, to changes in the measure of time.

Keywords

latent curve model nonlinear growth model longitudinal data basis functions structured latent curve model

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Understanding Individual-level Change Through the Basis Functions of a Latent Curve Model

Abstract

Keywords

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