Aim of this study is to detect structures of verbal aggressiveness network and also reveal changes through time. Standardized questionnaires have been distributed to 168 students and 8 teachers at secondary schools in 2017. We performed complete social networks analysis and further processing by conventional statistics. According to the results, density could be a first indicator of verbal aggressiveness existence. The verbal aggressiveness seems to become denser through time. Most ties are asymmetric and only a small amount becomes mutual. Thus, inequality appears. Verbal aggressors seem to target more than one victim and use all forms of verbal aggression. Triad analysis can disclose elementary “sources of verbal aggressiveness”. More verbal aggression ties are added than deleted over time.
Verbal aggressiveness has defined as an attack in the perception of the individual to cause psychological pain to a person’s self-concept through communication process [
Purpose of this study is to detect structures of verbal aggressiveness network and to reveal changes through time. The academic added value of this research consists in static and longitude detection and in the exploration of structure of verbal aggressiveness networks. Thereby, a more insightful view is expected to be achieved in the understanding of verbal aggressiveness. To describe the static situation of verbal aggressiveness is descriptive rather than analytical. The diachronic analysis depicts the deeper dynamic of this phenomenon and enables a more sound understanding of constructive or deconstructive tendencies in school socialization. The practical added value consists in using of the results for consulting and pedagogic action in the classes due to the field-specific empirical findings.
Complete network analysis emerges a set of methods to study the relations of participants [
The fundamental question in social network analysis is the nature of a dyad. It is the minimal structural element (subgraph) in a social network and shows a probable relationship between two actors [
Several other indicators were used to describe the structure of network like degree, transitivity, reciprocity. Such indicators have been used and interpreted in several empirical researches [
The survey was conducted at two-time period. The first data set (Wave 1) collected in early fall (October) and the second one (Wave 2) at the late spring (May). The participants were belonging to four classes and the teacher was the one with most teaching hour at the class. Cluster sampling has been used [
The sample consisted of 176 individuals. The participants were 168 Greek students (47% boys, 53% girls) and 8 teachers (50% men, 50% women) from third grade of a public secondary school (1rst Gymnasium of Trikala). The mean age of students was 14.2 years (SD = 0.15) and the mean age of teachers was 46.5 years (SD = 1.6).
The participants answered a questionnaire consisted of two parts: a) non-network variables (e.g. gender, times they travel abroad, appearance, etc.), and b) network variables based on the Greek version of Verbal Aggressiveness Scale [
Visone (version 2.17) was used for the networks’ illustration and the computation of several indicators (e.g. node degree, average network degree). Additionally, through visone environment, igraph r-package was used for the computation of additional parameters (e.g. density, reciprocity, transitivity). Also, the visone software was used to depict the alteration of networks over time. Network and non-network data were entered into SPSS 21.0 for further statistical analysis.
In
Class | A | A | B | B |
---|---|---|---|---|
Wave | Wave 1 | Wave 2 | Wave 1 | Wave 2 |
Graph | ||||
Participants | 23 | 23 | ||
Female | 15 | 10 | ||
Male | 8 | 13 | ||
Density | 0.0513 | 0.025 | 0.144 | 0.262 |
Diameter | 3 | 3 | 5 | 6 |
Avg. degree | 2.261 | 1.130 | 6.348 | 11.565 |
Ratio in/out degree | 1.130 | 0.565 | 3.174 | 5.783 |
Reciprocity | 0.153 | 0.000 | 0.082 | 0.285 |
Transitivity | 0.222 | 0.000 | 0.394 | 0.551 |
Class | C | C | D | D |
---|---|---|---|---|
Wave | Wave 1 | Wave 2 | Wave 1 | Wave 2 |
Graph | ||||
Participants | 21 | 21 | ||
Female | 9 | 13 | ||
Male | 12 | 8 | ||
Density | 0.104 | 0.254 | 0.080 | 0.047 |
Diameter | 2 | 4 | 2 | 4 |
Avg. degree | 4.190 | 10.190 | 3.238 | 1.904 |
Ratio in/out degree | 2.095 | 5.095 | 1.619 | 0.952 |
Reciprocity | 0.136 | 0.280 | 0.058 | 0.300 |
Transitivity | 0.243 | 0.492 | 0.274 | 0.133 |
The density varies among networks and seems to be correlated with the average degree of the network, the ratio of in-degree to out-degree and with transitivity. So, the density could be a fist indicator of the existence of verbal aggression with multiple properties. The density of all networks is at low level (below 0.30) but networks A and D have the minimum density (below 0.08). Also, the range of reciprocity in networks has been ranged at low level (below 0.30).
In Appendix, Spearman correlation of degree is being presented. All items of verbal aggressiveness scale have been correlated at both waves of the research. If someone uses verbal aggressiveness it is likely to practically use all its forms.
Comparing the two phases could be observed that most items also correlate between two waves. Only “mocking behaviour” and partial “lessening behaviour” seem not to be associated. This could be explained by social learning. If someone experienced verbal aggressiveness is in part susceptible not to pay attention to future behaviours, thinking that behaviours are “normal” for the particular person. From an additional point of view, one could distinguish between cohesive core of verbal aggressiveness dimensions and not cohesive ones.
In
A small amount of the relations is mutual. As expected, only in the networks with high density the mutual dyads have shown high percentage. The majority of existing verbal aggressiveness is asymmetric. It could be explained by the “usual” process of verbal aggressiveness leading to inequality. First someone uses verbal aggressiveness (asymmetric relation) with purpose to hurt somebody. Only if the “victim” similarly reacts, then the relation becomes mutual.
The change over time can be depicted in four networks (
A Wave 1 | A Wave 2 | B Wave 1 | B Wave 2 | C Wave 1 | C Wave 2 | D Wave 1 | D Wave 2 | |
---|---|---|---|---|---|---|---|---|
Mutual | 0.79% (2) | 0.00% (0) | 1.19% (3) | 7.51% (19) | 1.43% (3) | 7.14% (15) | 0.48% (1) | 1.43% (3) |
Asymmetric | 8.70% (22) | 5.14% (13) | 26.48% (67) | 37.55% (95) | 18.10% (38) | 36.67% (77) | 15.24% (32) | 6.67% (14) |
Null | 90.51% (229) | 94.86% (240) | 72.33% (183) | 54.94% (139) | 80.48% (169) | 56.19% (118) | 84.29% (177) | 91.90% (193) |
Class A | Class B |
---|---|
22 deleted edges (blue) or 84.615% from existing edges at Wave 1 | 35 deleted edges (blue) or 47.945% from existing edges at Wave 1 |
4 unchanged edges (green) or 15.384% from existing edges at Wave 1 | 38 unchanged edges (green) or 52.054% from existing edges at Wave 1 |
9 added edges (red) or 1.778% from edges of network | 95 added edges (red) or 18.774% from edges of network |
Class C | Class D |
---|---|
10 deleted edges (blue) or 22.727% from existing edges at Wave 1 | 27 deleted edges (blue) or 79.411% from existing edges at Wave 1 |
34 unchanged edges (green) or 77.272% from existing edges at Wave 1 | 7 unchanged edges (green) or 20.588% from existing edges at Wave 1 |
73 added edges (red) or 17.381% from edges of network | 13 added edges (red) or 3.095% from edges of network |
In networks A and D, a large amount of the existing edges have been deleted. Also, little new edges have been added. If we consider the density of classes A and D, we could assume that low level density conserves verbal aggressiveness at low level.
In the other two networks (B and C) with higher density, the greatest part of existing verbal aggressiveness has been conserved. Also, a large amount of new relations added to the networks. Thus, it is reasonably assumed that the denser is the network the more aggressive becoming over time.
A triad in a directed graph is a subgraph which is composed of three nodes and the possible relation between them. The triad census is an especially useful summary of asocial network since it makes a large amount of network indicators calculable [
In
In
Verbal aggressiveness is presented in all classes, but the structural features are differentiated. Verbal aggressiveness’s network seems to have low density (below 0.5). The differences between network’s densities could be an indicator of the existence of verbal aggressiveness. A future question for research could be if the density of the verbal aggressiveness is correlated with the gender because the network with lower density has more women than men.
Most relations are asymmetric. There are few mutual aggressive ties. This could show that many students don’t choose to respond with the same way, even if they have been attacked verbally. Thus, inequality appears. This behavioural pattern could explain why highly argumentative persons perceive arguing as a mean for lessening conflict.
Triad analysis can disclose elementary “sources of verbal aggressiveness”. This
Class | A | A | B | B | C | C | D | D | |
---|---|---|---|---|---|---|---|---|---|
Triad | Wave | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
1 - 003 | Real (%) | 75.776 | 85.827 | 49.407 | 3.999 | 63.534 | 38.485 | 67.068 | 78.947 |
2 - 012 | Real (%) | 18.859 | 12.931 | 22.417 | 15.302 | 16.316 | 4.773 | 19.925 | 15.414 |
3 - 102 | Real (%) | 1.468 | 0.000 | 0.678 | 2.428 | 0.000 | 0.379 | 0.226 | 2.556 |
4 - 021D | Real (%) | 1.242 | 0.452 | 19.932 | 28.515 | 14.812 | 37.955 | 9.549 | 0.902 |
5 - 021U | Real (%) | 0.903 | 0.565 | 0.508 | 0.565 | 0.376 | 0.076 | 0.827 | 0.301 |
6 - 021C | Real (%) | 0.734 | 0.226 | 0.960 | 1.129 | 0.075 | 0.000 | 0.226 | 0.301 |
7 - 111D | Real (%) | 0.169 | 0.000 | 0.226 | 0.169 | 0.000 | 0.000 | 0.000 | 0.376 |
8 - 111U | Real (%) | 0.508 | 0.000 | 0.960 | 5.872 | 2.857 | 4.167 | 0.752 | 0.977 |
9 - 030T | Real (%) | 0.169 | 0.000 | 3.275 | 4.065 | 0.827 | 0.909 | 0.977 | 0.000 |
10 - 030C | Real (%) | 0.000 | 0.000 | 0.000 | 0.056 | 0.000 | 0.000 | 0.000 | 0.000 |
11 - 201 | Real (%) | 0.000 | 0.000 | 0.000 | 0.113 | 0.075 | 0.379 | 0.000 | 0.075 |
12 - 120D | Real (%) | 0.056 | 0.000 | 0.113 | 0.452 | 0.000 | 0.303 | 0.000 | 0.000 |
13 - 120U | Real (%) | 0.056 | 0.000 | 1.468 | 8.075 | 0.977 | 1.758 | 0.451 | 0.075 |
14 - 120C | Real (%) | 0.000 | 0.000 | 0.000 | 0.113 | 0.000 | 0.000 | 0.000 | 0.000 |
15 - 210 | Real (%) | 0.056 | 0.000 | 0.056 | 1.242 | 0.150 | 0.985 | 0.000 | 0.075 |
16 - 300 | Real (%) | 0.000 | 0.000 | 0.000 | 0.903 | 0.000 | 0.833 | 0.000 | 0.000 |
can be interpreted that if someone is verbal aggressive, he/she probably will direct it to multiple targets. Also, correlation revealed that verbally aggressive students probably tend to use as many verbal aggressiveness forms as possible to harm the self-concepts of other students. However, one can distinguish between cohesive and not cohesive core of verbal aggressiveness dimensions.
Over time analysis showed the way of proliferation and reduction of verbal aggression. If verbal aggressiveness becomes denser, one can observe a greater amount of new verbal attacks added to the networks and only a small amount of the existing relation deleted. “Violence begets violence”. On the contrary, the diminution of the existing verbal aggressiveness without adding new entries seems to decrease verbal aggressiveness.
Theocharis, D. and Bekiari, A. (2018) Dynamic Analysis of Verbal Aggressiveness Networks in School. Open Journal of Social Sciences, 6, 14-28. https://doi.org/10.4236/jss.2018.61002
Spearman correlation of verbal aggressiveness (degree).