Sub-goal I: The Bayesian approach to psychological network analysis
We have developed a new Bayesian approach to psychological network analysis that accounts for the uncertainty underlying the choice of structure and associated parameters. Using this methodology, Bayesian model averaging can be used to analyze the structure and parameters of the network. Our review of Bayesian methods for testing the conditional independence of pairs of variables in the network showed that the proposed approach allows robust inference about the structure of the network.
Sub-goal II: Bayesian network models tailored to psychological data
The new Bayesian methodology makes use of prior assumptions about the structure of the network and its parameters. We have shown that standard assumptions from the Bayesian literature provide a reasonable starting point. However, we also want to tailor these prior assumptions to the psychological network literature. To gain insight into published network results, we conducted a large-scale reanalysis, collecting and reanalyzing datasets used for psychological network analysis. The reanalysis data provide insight into what we can expect in terms of network structure and what are reasonable parameter values. Based on the literature and what we learned from the reanalysis, we developed a new prior distribution for network structure that can account for the clustering of variables.
Most psychological data involve ordinal variables, yet there was no proper network model for their analysis. This project developed a new graphical model for ordinal psychological data. The advantage of the proposed graphical model is that it can be used to directly assess the conditional dependence structure of the network.
Sub-goal III: Compel researchers to use the new methodology
We have two strategies to encourage applied researchers to use the new methodology. The first is to develop open source software, and the second is to disseminate the new methodology through tutorials, review papers, and workshops.
We have made three contributions to open source software: First, we developed the software bgms, which incorporates all of the innovations described above. It allows full Bayesian inference on networks of binary and ordinal variables, and the latest version can also be used to analyze how such networks compare across two independent samples. Second, we created easybgm software to simplify the Bayesian analysis of graphical models using existing software packages (including bgms). This software facilitates the computation of Bayesian parameter and network estimates, hypothesis testing of network structures, and provides a wide range of plotting options. Third, the Bayesian approach to network analysis is implemented in the user-friendly JASP software. The JASP program comes with a graphical user interface and makes it easy for researchers to load their data and point and click on their desired analysis.
To encourage researchers to adopt the new methodology, we wrote a tutorial on the Bayesian approach to network analysis and a review of Bayesian methods for testing the conditional dependence or independence of pairs of variables in the network. The advantages of the new Bayesian approach over contemporary non-Bayesian approaches are central to these publications. Together, these two publications and our open source software provide a solid foundation for researchers to venture into the new Bayesian approach. To further support the dissemination of the methodology, we have given tutorials on the methodology at several international workshops on network analysis and Bayesian modeling.