Understanding the structure of school staff advice relations: An inferential social network perspective. (2020)

• Record Type: Journal Article
• Authors: Ortega, Lorena; Boda, Zsófia; Thompson, Ian; Daniels, Harry
• Is Part Of: International journal of educational research. Volume 99(2020)
• Publication Date: 2020
• Subjects: Social network analysis -- School staff -- Advice networks -- ERGMs; Education -- Evaluation -- Periodicals; Curriculum evaluation -- Periodicals; Education -- Periodicals; Program Evaluation -- Periodicals; Éducation -- Évaluation -- Périodiques; Programmes d'études -- Evaluation -- Périodiques; Éducation -- Recherche -- Périodiques; Curriculum evaluation; Education -- Evaluation; Periodicals; 370.72
• DOI: 10.1016/j.ijer.2019.101517
• Languages: English
• ISSNs: 0883-0355

Abstract

Highlights: Advice-seeking patterns among educators are investigated using sociograms and ERGMs. Both individual and structural variables shape networks of advice in schools. Individual similarities, reciprocity and clustering effects predict advice relations. Advice networks match, to some extent, the formal structures of school organization. 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.