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Previous issue (2021. Vol. 11, no. 4)

Modelling and Data Analysis

Publisher: Moscow State University of Psychology and Education

ISSN (printed version): 2219-3758

ISSN (online): 2311-9454

DOI: https://doi.org/10.17759/mda

License: CC BY-NC 4.0

Published since 2011

Published 4 times a year

Free of fees
Open Access Journal

 

Ordered Partial Correlation Networks in Psychological Research 80

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Artemenkov S.L.
PhD in Engineering, Professor of the Department of Applied Informatics and Multimedia Technologies, Head of the Center of Information Technologies for Psychological Research of the Faculty of Information Technologies, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-1619-2209
e-mail: slart@inbox.ru

Abstract
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.

Keywords: correlation analysis, partial correlation networks, regularization, network modeling in psychology, language R

Column: Data Analysis

DOI: https://doi.org/10.17759/mda.2021110202

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