Ordered Partial Correlation Networks in Psychological Research

Network modeling, which has emerged in recent years, can be successfully applied to the consideration of relationships between measurable psychological variables. In this context, psychological variables are understood as directly affecting each other, and not as a consequence of a latent construct. The article describes regularization methods that can be used to effectively assess the sparse and interpretable network structure based on partial correlations of psychological indicators. An overview of the glasso regularization procedure using EBIC model selection for evaluating an ordered sparse network of partial correlations is presented. The issues of performing this analysis in R in the presence of normal and non-normal data distribution are considered, taking into account the influence of the hyperparameter, which is manually set by the researcher. The considered approach is also interesting as a way to visualize possible causal connections between variables. This review bridges the gap related to the lack of an accessible description in Russian of this approach, which is still uncommon in Russia and at the same time promising.

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