Network analysis of social anxiety and perfectionistic self-presentation

Document Type : Research Paper

Authors

1 Department of Psychology, Faculty of Medicine, Na.C., Islamic Azad University, Najafabad, Iran.

2 Professor, Department of Psychology, Faculty of Human Sciences, Shahed University, Tehran, Iran.

3 Department of Psychology, Faculty of Human Sciences, Shahed University, Tehran, Iran.

4 Department of Psychology, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran.

5 Department of Health Psychology and Behavioral Sciences Research Center, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Abstract

The present study examined the network structure of social anxiety and perfectionistic self-presentation. This was a correlational, network analysis-based study. 1,599 (74.5% female) undergraduate and general practitioner students from universities across the country completed the online version of the Ryerson Social Anxiety Scale (RSAS) and the Perfectionist Self-Presentation Scale (PSPS) using convenience sampling. Central and bridge symptoms were identified using the expected influence and the bridge expected influence indices. The case-dropping procedure was used to examine network stability. The accuracy of network edge weights was evaluated using the bootstrap method. The data were analyzed using the R version 4.3.1 and SPSS-22 software. Results showed that the non-display of imperfection has the greatest impact on other nodes in the network, followed by anxiety-provoking social situations and the interference of social anxiety with life. Also, the non-display of imperfection and anxiety-provoking social situations were identified as bridge symptoms. It can be concluded that the non-display of imperfection is the most important node in this network, and targeting it can improve social anxiety.

Keywords


Articles in Press, Accepted Manuscript
Available Online from 03 February 2026
  • Receive Date: 09 February 2025
  • Revise Date: 14 May 2025
  • Accept Date: 31 May 2025
  • First Publish Date: 03 February 2026
  • Publish Date: 03 February 2026