Analyzing a network sample of 67 students and professors at university physical education class, the authors aim to detect informal structures (hierarchies of targeting of verbal aggressiveness and interpersonal attractiveness), to point out determinants of these structural properties, and to formulate a typology of verbal aggression targets and attractive persons. Complete network analysis was applied on the sample. Four network analysis centrality indicators were used: in-degree, Katz status, pagerank and authority. Non-network and network determinants of being target of verbal aggressiveness or attractive were discussed. Basic results of the study were that, at least in particular university milieu the verbal aggressiveness does not seem to depend on the education level of the parents. Male nodes seem to be quite susceptible to become a target of verbal aggressiveness and also to be physically attracted (especially, of course, by female nodes). The professor that the students appreciate proves to be a determinant of targeting students for verbal aggressiveness. The appreciation proved to be related with social and physical attractiveness while the task attractiveness seems to be significantly correlated with the friends number. As for the attractiveness, the following types were proposed: a) the “perfect image”: the one who is socially and physically attractive tends also to be task attractive and b) the “handsome” is the socially attractive. Regarding verbal aggressiveness in combination with attractiveness, two types of target emerged: a) the “immune star”, namely a person who exerts social, physical and task attractiveness, without being a target, and b) the “targeted star”, who attracts being a target.
Verbal aggressiveness is considered as an aggressive form of communication that results in destructive effects on interpersonal relationships [
Studies conducted in the academic domain showed that verbal aggressiveness is negatively related to perceptions of immediacy and interpersonal attraction [
The perceived verbal aggressiveness of the instructor appears also to restrict understanding and credibility, affecting simultaneously the students’ motivation and willingness to communicate [
Moreover, it has been supported that verbal aggression induces antisocial fair play behaviors while prosocial fair play behaviors are restricted by the verbal aggressiveness of instructors [
The relationship between students and teachers in class goes bad, when teachers ridicule, mock, humiliate or threaten students [
Finally, according to [
Moreover, students with high levels of verbally aggressive behavior are more likely to perceive their instructor as ideologically biased [
The school environment is considered as one of the most significant factors of communication where the pupils can be educated not only from the teaching procedure―typical education―but also by the interpersonal communication with their instructors―untypical education [
The students’ motives for education are probably enhanced when the teacher uses the following behaviors: smile, gesture, has a relaxed posture, uses a variety of vocal expressions and a monotone voice during teaching [
Interpersonal attraction can influence the teachers’ ability to fairly judge according to moral values [
An increased interest in the investigation of interpersonal relationship through complete network analysis has been observed recently [
In recent research conducted by [
Another survey concerning complete social network analysis has revealed that gender can be an important factor of intimacy, as better intimacy is developed among male students for pleasant communication purposes. What is also implied is that students of younger ages tend to be more physically attractive. Finally, the results have shown than attraction can lead to verbal aggressiveness [
The aim of the survey was to depict structures of verbal aggressiveness and interpersonal attractiveness hierarchies and to examine the factors affecting the position in these hierarchies, using a sample of university students and professors at Physical Education Faculty as an illustration.
The following network analysis indicators have been used, which are calculated by Visone software in normalized form (%). Their structural meaning is the following (no formulas are presented, as they are easily accessible in the web).
1) In-degree (occasional hierarchy position). It is an elementary indicator of centrality. It is defined as percentage of diagonal interactions received by a certain node. It can be interpreted as an occasional property given by the first-contacted nodes.
2) Status suggested by Katz [
3) Pagerank (distributive hierarchy position). It is based on the transferred value (e.g. being insulted or attracted) from one node to others. It is quite similar to Katz status. However, it reveal more subtle layers of nodes and, thereby, it drastically restricts outliers. Furthermore, it prevents calculative deformations induced by Katz status.
4) Authority (qualified competitiveness). This indicates nodes attracting most links from many other nodes, who intensively seek to maintain links. Namely, high authority characterizes a student who has attracted links of many other students who intensively (not occasionally) are looking for something specific.
A network is by definition a non-random sample. However, this is not considered to be a weakness, as purpose of this research was not the descriptive statistic (generalization of any descriptive quantitative property) but the analytical statistics (correlations).
Network (“snowball”) sampling has been conducted in a class of 62 students (4th semester) and 5 professors from a Physical Education Faculty in October 2015. The sample consisted of 39 male and 28 female, aged from 20 to 65 (M = 23, SD = 2.35). The participants belonged to different socio-economic status.
All students and professors were familiar with each other and have answered a standardized questionnaire about several forms of relation developed among them. The questionnaires should be named, because otherwise a complete network analysis would technically infeasible. It was emphasized to them that their names would be known only to the researcher. In this way, sincere information was expected to be received.
The questionnaire consisted of two parts: a) non-network variables (e.g. gender, birth year etc.), and b) network variables (verbal aggressiveness and attractiveness). The part b of the questionnaire was based on the Verbal Aggressiveness Scale [
The part b of the questionnaire was based on the Interpersonal Attraction Scale [
Finally, in part b, additional questions were added to the questionnaires of [
Visone 1.1 was used in order to process the network data for extracting the values of in-degree, Katz status, pagerank and authority for every node. Both non-network and network variables (in-degree etc.) were entered in SPSS 21. After normality control with Kolmogorov-Smorinov and Shapiro-Wilk, bivariate correlation Spearman was applied at significance level of p ≤ 0.05 (**) and p ≤ 0.01 (*). Moreover, Spearman test was preferred to a multivariate analysis, not only because it is non-parametric and more appropriate for this data setting, but also because it is bivariate and, thus, offers a clearer overview of all possible correlations among variables (multivariate analysis would be more appropriate for examining fewer and more specific variables). Principal component analysis was also used for formulating typology. The interpretation of the results has been based on in-depth interviews. These were conducted with students in form of individual discussions as well as in form of focus groups for rapid introduction into the situation.
Additionally, it should be clarified that, on the one hand, it is well known that permutation techniques have been developed in order to deal with dependence limitations of network data (QAP, ERGM etc.). On the other hand, such permutation techniques concern probabilities of ties appearance and correlations between networks which are considered as “dependent” and “independent” variables as a whole. This is not the case in this research, where various centrality values of nodes (not ties) were correlated between each other as well as with non-network variables. The above-mentioned permutation techniques cannot not enable such a correlation. Apart from that, aim of this analysis is not to predict whether a network will come about from another but rather to estimate whether e.g. an occasional (high indegree) verbal aggressor tends to become an accumulative (high Katz status) physical aggressor. This can (and should) be calculated only with techniques of conventional analytic statistics such as Spearman. Moreover, this conventional approach has already been tested and used in multidisciplinary academic literature and seems also to be in accordance with empirical data of the reality [
In
Differences can be observed in density between these networks. The networks of attraction are denser than these of verbal aggressiveness. This is expectable, as the main aim of the students at the university is to cooperate in science and to be socialized and not to develop conflicts.
Regarding hierarchical forms, professors are not at the top of verbal aggressiveness but rather on the top of some attraction hierarchies. The top nodes are in part the same and in part different ones in different hierarchies. For this reason, it is useful to apply several indicators (Katz, pagerank etc.) and not only one. Different indicators reveal different properties and meanings [
It is firstly emphasized that parents’ educational level are not included in
In
Verbal aggressiveness | Task attraction | Social attraction | Physical attraction | |
---|---|---|---|---|
Birth year | 0.131 | −0.336 (**) | −0.210 | −0.313 (*) |
0.321 | 0.009 | 0.111 | 0.016 | |
Gender (male = 0, female = 1) | −0.335 (*) | −0.226 | −0.264 | −0.346 (*) |
0.018 | 0.107 | 0.058 | 0.014 | |
Distance from city center | −0.006 | 0.598 (*) | −0.178 | −0.205 |
0.984 | 0.019 | 0.525 | 0.464 | |
Accommodation in flat | 0.035 | 0.460 (**) | 0.258 | 0.340 |
0.848 | 0.008 | 0.154 | 0.057 | |
Desired occupation with sport | 0.223 | 0.454 (*) | 0.366 | 0.207 |
0.319 | 0.034 | 0.094 | 0.356 | |
Perceived acceptance of other students | 0.076 | 0.269 | 0.343 (*) | 0.318 (*) |
0.594 | 0.056 | 0.014 | 0.023 | |
Interested in being accepted by other students | 0.056 | −0.003 | −0.124 | −0.078 |
0.656 | 0.983 | 0.322 | 0.532 | |
Company group | 0.039 | 0.282 (*) | 0.099 | 0.230 |
0.753 | 0.021 | 0.425 | 0.062 | |
Number of estimated students | 0.295 (*) | 0.063 | −0.026 | 0.019 |
0.020 | 0.624 | 0.840 | 0.886 |
*Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2-tailed).
In
These typology results seem to be complementary to the previous findings of [
Network analysis is not included in general in many papers until now. However, this method is appropriate for detecting indiscernible structures and not for producing descriptive statistics. This strength and limitation should always be considered in such analyses.
Social attraction | Physical attraction | |
---|---|---|
Task attraction | 0.656 (**) | 0.529 (**) |
0.000 | 0.000 | |
Social attraction | 0.622 (**) | |
0.000 |
**Correlation is significant at the 0.01 level (2-tailed).
Type | ||
---|---|---|
1 | 2 | |
Friently behavior | 0.736 | 0.670 |
Desire for friendly chat | 0.736 | 0.673 |
Attractive appearance | 0.739 | 0.663 |
Generally attractive appearance | 0.739 | 0.661 |
Substantial assistance in scientific work | 0.737 | 0.672 |
Valuable contribution to scientific work | 0.736 | 0.674 |
Offensive behavior | −0.730 | 0.679 |
Negative comments | −0.740 | 0.666 |
Ironic comments | −0.735 | 0.664 |
Rude behavior | −0.719 | 0.685 |
Underestimated attitude | −0.711 | 0.694 |
Causing bad feelings | −0.731 | 0.669 |
Mocking behavior | −0.734 | 0.673 |
Underestimation of intelligence | −0.730 | 0.676 |
Verbal aggressiveness | −0.733 | 0.680 |
Task attraction | 0.737 | 0.673 |
Social attraction | 0.736 | 0.672 |
Physical attraction | 0.739 | 0.662 |
Extraction method: principal component analysis; a 2 components extracted.
Results of present study, regarding structures in verbal aggressiveness and interpersonal attraction, indicated that it is useful to apply several indicators (Katz, pagerank etc.) and not only one. Different indicators reveal different properties and meanings.
Basic results of the study were that, at least in particular university milieu the verbal aggressiveness does not seem to depend on the education level of the parents. Gender seems also to be of importance, as male nodes seem to be quite susceptible to become a target of verbal aggressiveness and also to be physically attracted (especially, of course, by female nodes). The professor that the students appreciate proves to be a determinant of targeting students for verbal aggressiveness. The appreciation proved to be related with social and physical attractiveness while the task attractiveness seems to be significantly correlated with the friends number.
As for the attractiveness, the following types were proposed: a) the “perfect image”: the one who is socially and physically attractive tends also to be task attractive and b) the “beautiful” is the socially attractive. Regarding verbal aggressiveness, two types of target emerged: a) the “immune star”, namely a person who exerts social, physical and task attractiveness, without being a target, and b) the “targeted star”, who attracts being a target.
Alexandra Bekiari,Spyreta Spyropoulou, (2016) Exploration of Verbal Aggressiveness and Interpersonal Attraction through Social Network Analysis: Using University Physical Education Class as an Illustration. Open Journal of Social Sciences,04,145-155. doi: 10.4236/jss.2016.46016