On fitting nonlinear latent curve models to multiple variables measured longitudinally

This article shows how nonlinear latent curve models may be fitted for simultaneous analysis of multiple variables measured longitudinally using Mx statistical software. Longitudinal studies often involve observation of several variables across time with interest in the associations between change characteristics of different variables measured within individuals. Other applications involve repeated measures for distinguishable individuals nested within small groups, such as families, with interest in the associations between change characteristics in variables for individuals within groups. This article shows how Mx can be used to carry out analysis of multiple variables measured over time where at least one variable is described by a function that includes one or more parameters that enter the model nonlinearly. An example is provided.