Abstract:
This paper describes the processes and outcomes of making sense of 10 megabytes of unstructured text data. Within the context of collaboration between three schooling sector partners in the Students Completing Schooling Project (SCSP), the five representative team members faced a considerable challenge in thematising the 163 transcripts resulting from interviews with 209 students. A challenge for the research team was to adequately represent the complexity of early school leaving. Sensitive methodology was required in compressing data into key words so that the complexity was maintained without representing the problem as too vast to identify action. Outcomes of the thematising were to identify the 'dominant' story for each transcript; to identify the best examples of 'key' stories; to develop a computerised index to enable ready access to sections of transcripts; and, most importantly, to assist in the development of a sophisticated account of early school leaving while honouring the voices of our informants. The decision to use the software NUD*IST (Non-Numeric Unstructured Data - Indexing Searching Theorising) was based on the project's commitment to the need to stay close to the original text, and to develop a comprehensive index of the data to enable themes to be tracked across transcripts.