The dissertation topic selection of doctoral students using dynamic network analysis

Abstract

The significance of a doctoral student's completed dissertation is of immense importance both to the field and student. The dissertation not only signifies the candidate's ability to perform independent research, it also confirms the candidate's ability to provide original contributions to knowledge. This study examines the dissertation topic selection process of doctoral educational leadership students in order to understand what influences the student's dissertation topic selection. The emerging approach of Dynamic Network Analysis (DNA) was used to examine the interactions between task, belief, resource and knowledge in determining students' choice. Data was analyzed using the Organizational Risk Analyzers' (ORA) software measures of Newman Grouping, centrality betweenness, cognitive demand, knowledge exclusivity, resource exclusivity, eigenvector centrality, and total degree centrality. The results suggest that topic selection is influenced by faculty member's research agenda, departmental core courses, and network factors like professional experience, life experience, and practical experience. This result will help doctoral students understand the influences of mentor and adviser's research interest, departmental core courses, professional experiences and life experiences as they navigate through dissertation topic selection and research.