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Understanding the Creation of Peer Network Structure: New Evidence from Simulations Studies

Sat, April 14, 3:45 to 5:15pm, Hilton, Floor: Third Floor, Board Room 3

Abstract/Description

INTRODUCTION: Simulation studies are one way that scholars can begin to understand the development of peer network structure. Simulations are useful because they can be used and modified in order to determine the most likely path toward the development of a hypothesized structure over time. One way to begin investigations of developing peer network structures is to first consider real world developmental data. Therefore, the current simulation study was based on previous research examining the development of a peer network after school entry (e.g., Schaefer et al., 2010). We build upon this school entry study because the mechanisms observed during peer network formation at school entry will also apply to adolescent school transitions (e.g., the transition to middle school and high school).
GOALS: The primary goal of the current study was to simulate the formation of peer network structure—using a Monte Carlo simulation method—and to begin to understand how peer networks form and particular network structures emerge, for better or worse, within classrooms and schools. Understanding the emergence of peer network structure has important implications for directing adaptive (prosocial) and redirecting maladaptive (bullying) peer network dynamics via intervention and prevention strategies.
METHODS: For each iteration of the simulation algorithm, a randomly selected relationship (or absence of relationship) is established or dissolved. This is done to simulate the dynamics of a real world peer relationship network. Thus, many networks are generated with different strengths of the mechanisms of interest (e.g., popularity defined through the in-degree and transitivity defined through the number of common friends) and with different relationship patterns in the simulated peer group. Statistical estimates are then obtained to assess how closely the obtained peer network structure matches the hypothesized peer network structure.
RESULTS: The results demonstrate that indeed popularity and transitivity effects ultimately determine the structure of an emerging peer network. Simulations also confirmed that the critical mechanisms behind the development of a peer network are: (a) The presence of changing levels of popularity and (b) transitivity effects whereby friends of friends tend to become friends in the peer network. To be more precise, stable network structures emerge as popular peers become more selective of social relationships over time and as the presence of transitive connections increases over time.
CONCLUSION: The current findings point to the significance of popularity and transitivity in the development of peer network structures. Thus, if prevention science aims to understand, or modify, peer network dynamics it will be important to understand and address the social dynamics surrounding popularity and transitivity effects as a starting point for modifications in the peer network.

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