Growth modeling is among the main techniques used in the study of development and within-person change. Moving beyond the typically used models of linear change, we have pushed for consideration of and use of non-linear models that capture individual differences in timing and tempo of development. However, fitting these models to empirical data is often difficult. In this talk we review a selection of non-linear growth models, and illustrate how easy-to-use Bayesian fitting approaches (implemented in R) are allowing us to model and examine timing of change in not just one variable, but in multiple variables simultaneously.

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