scholarly journals Applying hierarchical bayesian modeling to experimental psychopathology data: An introduction and tutorial.

2021 ◽  
Vol 130 (8) ◽  
pp. 923-936
Author(s):  
Ivy F. Tso ◽  
Stephan F. Taylor ◽  
Timothy D. Johnson
Keyword(s):  
Bayesian Modeling ◽  
Hierarchical Bayesian ◽  
Experimental Psychopathology ◽  
Hierarchical Bayesian Modeling

scholarly journals Applying Hierarchical Bayesian Modeling to Experimental Psychopathology Data: An Introduction and Tutorial

2021 ◽  
Author(s):  
Ivy Fei Tso ◽  
Stephan F. Taylor ◽  
Timothy D. Johnson
Keyword(s):  
Bayesian Inference ◽  
Bayesian Methods ◽  
Bayesian Modeling ◽  
Attractive Alternative ◽  
Hierarchical Bayesian ◽  
Gaze Perception ◽  
Mathematical Methods ◽  
Experimental Psychopathology ◽  
Hierarchical Bayesian Modeling ◽  
Bayesian Inference Framework

Over the past two decades Bayesian methods have been gaining popularity in many scientific disciplines. However, to this date, they are rarely part of formal graduate statistical training in clinical science. Although Bayesian methods can be an attractive alternative to classical methods for answering certain research questions, they involve a heavy “overhead” (e.g., advanced mathematical methods, complex computations), which pose significant barriers to researchers interested in adding Bayesian methods to their statistical toolbox. To increase the accessibility of Bayesian methods for psychopathology researchers, this paper presents a gentle introduction of the Bayesian inference framework and a tutorial on implementation. We first provide a primer on the key concepts of Bayesian inference and major implementation considerations related to Bayesian estimation. We then demonstrate how to apply hierarchical Bayesian modeling (HBM) to experimental psychopathology data. Using a real dataset collected from two clinical groups (schizophrenia and bipolar disorder) and a healthy comparison sample on a psychophysical gaze perception task, we illustrate how to model individual responses and group differences with probability functions respectful of the presumed underlying data-generating process and the hierarchical nature of the data. We provide the code with explanations and the data used to generate and visualize the results to facilitate learning. Finally, we discuss interpretation of the results in terms of posterior probabilities and compare the results with those obtained using a traditional method.

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