Short Version of the Social Networks Addiction Risk Questionnaire (CARS-R): Evidence From Network Psychometrics

Area of Science:

• Psychology
• Network Science
• Mental Health Research

Background:

• Social media addiction is linked to negative mental health outcomes.
• Network models provide a framework for understanding the complexity of mental health disorders.
• The psychometric properties of the Bergen Social Media Addiction Scale-Revised (CARS-R) require investigation within network analysis.

Purpose of the Study:

• To examine the psychometric properties of the CARS-R using network analysis.
• To validate the CARS-R scale's structure and reliability.
• To propose a new definition of social media addiction based on network findings.

Main Methods:

• Exploratory Graph Analysis (EGA) to identify network structure.
• Unrestricted Variables Analysis (UVA) to assess item relevance.
• Bootstrapped EGA (bootEGA) for invariance testing across demographic groups.
• Centrality index analysis to determine influential network nodes.

Main Results:

• EGA revealed a single community of nine nodes with high network loadings (>.35).
• All items were found to be relevant (UVA), and the scale demonstrated high reliability and stability.
• bootEGA confirmed CARS-R invariance across sex, age, and usage hours.
• Centrality analysis identified nodes C2, C9, and C7 as the most influential.

Conclusions:

• The study provides robust evidence for the psychometric functioning of the CARS-R within a network framework.
• Network analysis supports a unified structure for social media addiction symptoms.
• Results facilitate a refined, network-informed definition of social media addiction.