Connecting groups and behaviours: A network analysis of identity-infused behaviours

Research in the social identity tradition acknowledges the multiplicity of our identities and the implications that identity compatibility has for our health and well-being. However, current measures of multiple group membership have not yet captured the richness and complexity of our social identity networks at the wider sample level, and data regarding the different behaviours typically associated with different group memberships are scarce. Adopting a network approach, we explore the co-occurrence of different group memberships within an individual (identity-by-identity network), the behaviours that are shared among identities (behaviour-by-identity network), and whether identities that are shared also share common behaviours (identity-by-behaviour network). An online survey asked participants (N = 286) to list the groups they are part of, as well as the behaviours viewed to be typical of group members. The networks identified several identities and behaviours to significantly co-occur at a rate both higher and lower than chance. Networks were found to be low in modularity; there was no evidence of clustering within the data. Permutation analyses demonstrated the overall structure of the networks to be significantly different than expected by chance. The co-occurrences identified serve as a meaningful resource for those conducting research into identities, group norms and their associated behaviours.

Automated summary

Research in the social identity tradition recognizes the importance of identity compatibility for our health and well-being, but current measures do not fully capture the complexity of our social identity networks and associated behaviors. By adopting a network approach, this study explores the co-occurrence of different group memberships within individuals and identifies shared behaviors among identities. The findings provide valuable insights for researchers studying identities, group norms, and associated behaviors.