Illustration of a Software-Aided Content Analysis Methodology Applied to Educational Research

Abstract

This paper presents a software-aided methodology for content analysis by implementing the Leximancer software package, which can convert plain texts into conceptual networks that show how the prevalent concepts are linked with each other. The generated concept maps are associative networks of meaning related to the topics elaborated in the analyzed documents and reflect the creators’ core mental representations. The applicability of Leximancer is demonstrated in an education research context, probing university students’ epistemological beliefs, where a qualitative semantic analysis could be applied by inspecting and interpreting the portrayed relationships among concepts. In addition, concept-map-generating matrices, ensuing from the previous step, are introduced to another specialized software, Gephi, and further network analysis is performed using quantitative measures of centrality, such as degree, betweenness and closeness. Besides illustrating the method of this semantic analysis of textual data and deliberating the advances of digital innovations, the paper discusses theoretical issues underpinning the network analysis, which are related to the complexity theory framework, while building bridges between qualitative and quantitative traditional approaches in educational research.

Keywords

• textual data
• content analysis
• concept maps
• Leximancer software
• network analysis
• complexity