Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories

Authors

  • Luca Marvin University of California, Merced Author
  • Haiyan Liu University of California, Merced Author
  • Sarah Depaoli University of California, Merced Author

DOI:

https://doi.org/10.35566/jbds/v3n1/marvin

Keywords:

Piecewise Growth Curve Models, Bayesian SEM, Model Selection

Abstract

Bayesian growth curve modeling is a popular method for studying longitudinal data. In this study, we discuss a flexible extension, the Bayesian piecewise growth curve model (BPGCM), which allows the researcher to break up a trajectory into phases joined at change points called knots. By fitting BPGCMs, the researcher can specify three or more phases of growth without concern for model identification. Our goal is to provide substantive researchers with a guide for implementing this important class of models. We present a simple application of Bayesian linear BPGCMs to childrens' math achievement. Our tutorial includes Mplus code, strategies for specifying knots, and how to interpret model selection and fit indices. Extensions of the model are discussed.

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Published

2023-07-13

Issue

Vol. 3 No. 1 (2023): Volume 3 Number 1

Section

Theory and Methods

How to Cite

Marvin, L., Liu, H., & Depaoli, S. (2023). Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories. Journal of Behavioral Data Science, 3(1), 1-33. https://doi.org/10.35566/jbds/v3n1/marvin