Moderating the consequences of longitudinal change for distal outcomes

There has been a growing interest in using earlier change to predict downstream distal outcomes in development, however, prior work has mostly focused on estimating the unique effect of the different growth parameters (e.g., intercept and slope) rather than focusing on the trajectory as a whole. Here I lay a distal outcome latent curve model with latent interactions which attempts to model the *joint* effect of growth parameters on these later outcomes. I show again that these models require us to contend with unintuitive time coding effects which can impact the direction and significance of effects and that plotting and probing are necessary for disambiguating these joint effects. These graphical approaches emphasize practical approaches for applied researchers in understanding these effects. I then outline how future research can help clarify optimal approaches for using the trajectory as a whole rather than the unique effects of its individual sub-components.