Understanding the structure of school staff advice relations: an inferential social network perspective

Abstract

Understanding the structure of staff advice relationships and the factors that facilitate (and hinder) the flow of resources within schools is key to school improvement. Our study examines school staff advice networks for supporting vulnerable learners using Exponential Random Graph Models (ERGMs). We investigate the individual and structural mechanisms that shape these networks in six secondary schools and find evidence for the importance of mutuality, clustering and individual similarities. Educators tend to ask for advice from those in formal leadership or support positions, although informal hierarchies are also present. The study contributes with a novel application of an inferential social network approach to study patterns of advice relations among teachers, support staff and formal leaders in schools.