Social Media Addiction and Academic Performance: A Psychology Study of University Students in Patna
The present study explores the relationship between social media addiction and academic performance among university students in Patna, with a focus on associated psychological variables such as distress, coping strategies, stigma perception, and social support. A total of 126 students (aged 18–25 years) were randomly selected from multiple universities. Data were collected through in-person and online interviews using standardized tools including the Social Media Addiction Scale, Psychological Distress Scale, Coping Strategies Inventory, and Social Support measures. Academic performance was assessed through self-reported grades. Descriptive statistics (mean, standard deviation, frequency, percentage) were computed, and independent samples t-tests were conducted to examine group differences, particularly between male and female students, and between rural and urban backgrounds. Findings revealed that students reported moderate-to-high levels of social media addiction, which correlated with increased psychological distress and maladaptive coping, and reduced academic performance. Female students displayed higher distress levels (M = 27.2) compared to males (M = 25.1), though the difference was not statistically significant (t(124) = -1.63, p > 0.05). Rural students demonstrated higher stigma perception, while urban students reported stronger social support. The results underscore the dual role of coping and social support in buffering the adverse academic and psychological effects of excessive digital engagement. The study highlights implications for clinical psychology practice and educational policy, suggesting the need for awareness campaigns, counseling services, and stigma-reduction initiatives, which may also inform broader public health contexts such as tuberculosis (TB) management programs.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-04-01
- DOI
- https://doi.org/10.5281/zenodo.17241483
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00