Psychonomic Bulletin & Review

, Volume 25, Issue 1, pp 235–255 | Cite as

Fitting growth curve models in the Bayesian framework

Brief Report

Abstract

Growth curve modeling is a popular methodological tool due to its flexibility in simultaneously analyzing both within-person effects (e.g., assessing change over time for one person) and between-person effects (e.g., comparing differences in the change trajectories across people). This paper is a practical exposure to fitting growth curve models in the hierarchical Bayesian framework. First the mathematical formulation of growth curve models is provided. Then we give step-by-step guidelines on how to fit these models in the hierarchical Bayesian framework with corresponding computer scripts (JAGS and R). To illustrate the Bayesian GCM approach, we analyze a data set from a longitudinal study of marital relationship quality. We provide our computer code and example data set so that the reader can have hands-on experience fitting the growth curve model.

Keywords

Bayesian modeling Growth curve modeling 

Notes

Acknowledgments

We would like to thank Mike Rovine for generously providing us the dataset on marital happiness.

The research reported in this paper was sponsored by grant #48192 from The John Templeton Foundation.

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Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  1. 1.The Pennsylvania State UniversityState CollegeUSA
  2. 2.The Pennsylvania State UniversityState CollegeUSA

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