| Literature DB >> 29483887 |
Eva M Romera1, Mauricio Herrera-López2, José A Casas1, Rosario Ortega Ruiz1,3, Rosario Del Rey4.
Abstract
Cybergossip is the act of two or more people making evaluative comments via digital devices about somebody who is not present. This cyberbehavior affects the social group in which it occurs and can either promote or hinder peer relationships. Scientific studies that assess the nature of this emerging and interactive behavior in the virtual world are limited. Some research on traditional gossip has identified it as an inherent and defining element of indirect relational aggression. This paper adopts and argues for a wider definition of gossip that includes positive comments and motivations. This work also suggests that cybergossip has to be measured independently from traditional gossip due to key differences when it occurs through ICT. This paper presents the Colombian and Spanish validation of the Cybergossip Questionnaire for Adolescents (CGQ-A), involving 3,747 high school students (M = 13.98 years old, SD = 1.69; 48.5% male), of which 1,931 were Colombian and 1,816 were Spanish. Test models derived from item response theory, confirmatory factor analysis, content validation, and multi-group analysis were run on the full sample and subsamples for each country and both genders. The obtained optimal fit and psychometric properties confirm the robustness and suitability of a one-dimensional structure for the cybergossip instrument. The multi-group analysis shows that the cybergossip construct is understood similarly in both countries and between girls and boys. The composite reliability ratifies convergent and divergent validity of the scale. Descriptive results show that Colombian adolescents gossip less than their Spanish counterparts and that boys and girls use cybergossip to the same extent. As a conclusion, this study confirmes the relationship between cybergossip and cyberbullying, but it also supports a focus on positive cybergossip in psychoeducational interventions to build positive virtual relationships and prevent risky cyberbehaviors.Entities:
Keywords: adolescence; cross-cultural; cybergossip; gender; gossip; psychometric properties; self-report
Year: 2018 PMID: 29483887 PMCID: PMC5816232 DOI: 10.3389/fpsyg.2018.00126
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
Mean, standard deviation, skewness, kurtosis, and 3PL analysis (IRT).
| CG 1 | I have made comments about other friends or classmates to get into a group on social networks or WhatsApp. | 1.19 | 0.55 | 3.81 | 17.87 | 2.03 | 1.61 | 0.02 |
| CG 2 | I talk about others on social networks or WhatsApp because it makes me feel closer to my group of friends | 1.41 | 0.83 | 2.43 | 6.20 | 1.89 | 1.00 | 0.04 |
| CG 3 | I have told things about a classmate or friend on social networks or WhatsApp to make the group change their opinion about him/her | 1.45 | 0.83 | 2.10 | 4.50 | 1.92 | 1.00 | 0.08 |
| CG 4 | When I'm angry with a classmate or friend, I talk about it on social networks or WhatsApp | 1.39 | 0.78 | 2.36 | 5.80 | 1.73 | 0.97 | 0.00 |
| CG 5 | I have said negative things about another person on social networks or WhatsApp without realizing it | 1.40 | 0.74 | 2.24 | 5.72 | 1.99 | 0.76 | 0.00 |
| CG 6 | I have shared a classmate's secret with others on social networks or WhatsApp | 1.29 | 0.68 | 2.90 | 9.58 | 1.76 | 1.19 | 0.00 |
| CG 7 | I use social networks or WhatsApp to share stories I hear about others with my friends | 1.56 | 0.94 | 1.86 | 3.13 | 2.19 | 0.52 | 0.00 |
| CG 8 | When somebody in my group does something bad, I tell the rest of my classmates via social networks or WhatsApp so they know about it | 1.33 | 0.71 | 2.61 | 7.67 | 2.23 | 0.99 | 0.02 |
| CG 9 | I talk with my friends on social networks or WhatsApp about what's going on with other classmates for fun | 1.32 | 0.71 | 2.77 | 8.65 | 1.95 | 1.03 | 0.00 |
a, discrimination; b, difficulty; c, probability of failure (random).
Matrix of CGQ-A polychoric correlations.
| 1 | 1 | ||||||||
| 2 | 0.61 | 1 | |||||||
| 3 | 0.54 | 0.52 | 1 | ||||||
| 4 | 0.43 | 0.43 | 0.43 | 1 | |||||
| 5 | 0.46 | 0.47 | 0.45 | 0.63 | 1 | ||||
| 6 | 0.48 | 0.41 | 0.43 | 0.53 | 0.59 | 1 | |||
| 7 | 0.46 | 0.43 | 0.53 | 0.58 | 0.59 | 0.61 | 1 | ||
| 8 | 0.48 | 0.49 | 0.50 | 0.52 | 0.56 | 0.54 | 0.59 | 1 | |
| 9 | 0.49 | 0.49 | 0.43 | 0.49 | 0.56 | 0.53 | 0.58 | 0.59 | 1 |
All correlations were significative, p < 0.01.
Spearman's Rho correlations between ECIPQ and CGQ-A.
| 1. Cyberaggression | 1.12 | 0.26 | – | 5.13 | 41.75 | ||
| 2. Cybervictimization | 1.22 | 0.35 | 0.49 | – | 3.85 | 23.06 | |
| 3. Cybergossip | 1.37 | 0.50 | 0.44 | 0.30 | – | 2.36 | 7.94 |
p < 0.01.
Figure 1CFA of the CGQ-A scale (*p < 0.05).
Multi-group analysis of the CGQ-A scale per country and gender.
| Country | Mod 1 | 287.072 | 54 | 0.000 | 0.966 | 0.975 | 0.053 | 0.061 | |||||||
| Mod 2 | 276.523 | 62 | 0.000 | 0.973 | 0.977 | 0.047 | 0.061 | 10.549 | 0.534 (n.s.) | 8 | 0.007 | 0.002 | 0.006 | 0.000 | |
| Gender | Mod 1 | 306.722 | 54 | 0.000 | 0.980 | 0.985 | 0.055 | 0.049 | |||||||
| Mod 2 | 317.516 | 62 | 0.000 | 0.982 | 0.985 | 0.051 | 0.050 | 10.794 | 0.793 (n.s.) | 8 | 0.002 | 0.000 | 0.004 | 0.001 |
Mod 1, model without restrictions; Mod 2, model restricted on factor weights; n.s., non-significant.