Psychological Security, Psychological Loneliness, and Age as the Predictors of Cyber-Bullying Among University Students.

The present study aimed to explore the relationship between cyber-bullying and psychological security, psychological loneliness, and age. In other words, it sought to identify the predictive power of these three variables on cyber-bullying among university students. Participants were 426 male and female Saudi university students aged between 18 and 36 years (M = 21.00, SD = 2.42). Three scales were used to probe cyber-bullying and psychological security and loneliness. Data analysis revealed that 17.6% of the participants were cyber-bullies. Significant differences in cyber-bullying were found by gender (in favor of males). A significant positive correlation was found between cyber-bullying on one hand and psychological security and loneliness and age on the other. The findings also revealed that cyber-bullying among university students can be predicted by psychological loneliness and age. Psychological loneliness is the best predictor of cyber-bullying. It explained .284 of cyber-bullying, while age, psychological loneliness explained .339 of cyber-bullying. These findings shed more light on the psychological aspects included in cyber-bullying. It is a significant contribution in that it identified the motives beyond cyber-bullying and its adverse effects on individuals.

The dimensional validity and reliability of the Italian smartphone addiction inventory of Yemeni university students.

The increasing usage of smartphones globally necessitates the creation of reliable and valid scales to evaluate their psychological effects, particularly within academic settings such as universities. The current study aimed to identify the factorial structure of the Smartphone Addiction Inventory (SPAI) in the Republic of Yemen. The sample consisted of 1920 university students (1136 males and 784 females). The data was analyzed with the AMOS V25 statistical program. The results of the factor analysis supported the goodness of fit of the five-factor model to the data with excellent indices: RMSEA = 0.052, CFI = 0.910, GFI = 0.931, AGFI = 0.915, TLI = 0.907, NFI = 0.915, RFI = 0.916, and RMR = 0.032, all of which are within the ideal range to support the goodness of fit of the model to the factorial structure of the inventory, as the values of the explained variances ranged between 0.740 and 0.834., with indices of reliability in measurement. The results of the confirmatory factor analysis revealed that four items loaded on the Time Spent factor, four items on the Compulsivity factor, eight items on the Daily Life Interference factor, five items on the Craving factor, and three items on the Sleep Interference factor, with all loadings being statistically significant (>0.001). Based on these findings, research direction and recommendations were provided.