NETWORK ANALYSIS AS A METHOD FOR MODELING THE INTERNAL ARCHITECTURE OF SITUATIONAL ANXIETY UNDER MARTIAL LAW
DOI:
https://doi.org/10.32782/3041-2021/2026-3-32Keywords:
situational anxiety, network psychometrics, graph theory, Gaussian Graphical Model, Node Strength, bootstrappingAbstract
This study uses network psychometrics to model the internal architecture of situational anxiety among psychology professionals and students (N = 90) under martial law. This cohort faces elevated psycho-emotional burdens and vicarious traumatization risks. We propose shifting from traditional sum-score assessments – which obscure the clinical picture – to a graph-based approach. This method conceptualizes cognitive, somatic, and emotional manifestations as a dynamic, self-sustaining system of lateral interconnections rather than outcomes of a latent variable. Data obtained via the STAI-S (C. D. Spielberger) were processed in R using the qgraph and bootnet packages. To prevent model oversaturation, 8 key descriptors were selected. A sparse Gaussian Graphical Model (GGM) was constructed based on regularized partial correlations and optimized using the EBICglasso algorithm. Topological analysis revealed high edge heterogeneity and an integrated cognitive-somatic core. According to the Node Strength centrality index, the cognitive node “Worry” is the leading systemic driver. It operates in a strong tandem with the somatic node “Tension” to rapidly activate other symptoms. The model’s metric reliability and architecture stability were verified via non-parametric and case-dropping bootstrapping (N = 1000); the correlation stability coefficient (CS-coefficient) confidently exceeded established psychometric thresholds. These findings open fundamentally new perspectives for designing precise psychotherapeutic interventions. They demonstrate that deactivating the central target node can dismantle the entire pathological network of situational anxiety during prolonged distress.
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