Title |
Fitting growth curve models in the Bayesian framework
|
---|---|
Published in |
Psychonomic Bulletin & Review, May 2017
|
DOI | 10.3758/s13423-017-1281-0 |
Pubmed ID | |
Authors |
Zita Oravecz, Chelsea Muth |
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. |
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Country | Count | As % |
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Netherlands | 1 | 17% |
United Kingdom | 1 | 17% |
Denmark | 1 | 17% |
Norway | 1 | 17% |
Unknown | 2 | 33% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 50% |
Scientists | 2 | 33% |
Science communicators (journalists, bloggers, editors) | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 166 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 44 | 27% |
Researcher | 33 | 20% |
Student > Master | 16 | 10% |
Student > Bachelor | 13 | 8% |
Student > Doctoral Student | 11 | 7% |
Other | 31 | 19% |
Unknown | 18 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 70 | 42% |
Social Sciences | 12 | 7% |
Agricultural and Biological Sciences | 10 | 6% |
Neuroscience | 7 | 4% |
Medicine and Dentistry | 6 | 4% |
Other | 27 | 16% |
Unknown | 34 | 20% |