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Who victimizes whom and who defends whom? A multivariate social network analysis of victimization, aggression, and defending in early childhood.

Gijs HuitsingClaire P Monks
Published in: Aggressive behavior (2018)
The aim of this research was to investigate the interplay between victim-aggressor relationships and defending relationships in early childhood to test the proposition that young aggressors are less selective than older children in their choice of vulnerable targets. Cross-sectional multivariate statistical social network analyses (Exponential Random Graph Models) for a sample of 177 preschoolers from seven classes, 5- to 7-years-old, revealed that boys were more aggressive than girls, toward both boys and girls, whereas defending relationships were most often same-sex. There was significant reciprocity in aggression, indicating that it was more often bidirectional rather than unidirectional. In addition, aggressors clearly defended each other when they shared their targets of aggression, whereas a marginally significant trend appeared for defending between victims who were victimized by the same aggressors. Furthermore, teacher-rated dominance was positively associated with children's involvement in both aggression and victimization, and teacher-rated insecurity was associated with less aggression, but not with victimization. These findings suggest that those who are reported as being victimized may retaliate, or be aggressive themselves, and do not display some of the vulnerabilities reported among older groups of victims. The findings are in line with the proposition that young aggressors are less strategic than older children in targeting vulnerable victims. The network approach to peer victimization and defending contributes to understanding the social processes facilitating the development of aggression in early childhood.
Keyphrases
  • intimate partner violence
  • middle aged
  • young adults
  • healthcare
  • cross sectional
  • mental health
  • physical activity
  • high school
  • community dwelling
  • convolutional neural network
  • deep learning