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Let the Data Speak: Using Rigour to Extract Vitality from
 Qualitative Data

Qualitative data can be gathered from an array of rich
 sources of research information. One of the popular ways to collect
 this data is by interviewing a range of experts on the topic,
 followed by transcription, resulting in a database of written
 documents, often supplemented by other documented data that informs
 the topic. Thematic or Content Analysis can then be used to explore
 the data and identify themes of meaning that enlighten the research
 topic, with the themes being gathered into nodes. The researcher now
 has an array of nodes, which needs to be organised into a coherent
 model, and more importantly, one that represents the views of the
 research informants. To do this with some degree of rigour, the
 researcher needs some way of ranking the nodes in terms of their
 relative importance. The node ranking can be based on experience, or
 on the literature, but neither of these approaches looks to the data
 itself. If the database contains new or unexpected knowledge,
 neither experience nor the literature will guide us to it, and vital
 new insights may easily be missed. The framework outlined in this
 paper aims to provide a sound first‑cut analysis of the data, based
 on the evidence in the research interviews themselves. Clearly the
 literature and research experience have an important role to play in
 shaping the results of any research. However this paper argues that
 one should proceed only after the data itself has been offered "the
 first chance to speak".The node classification matrix detailed here,
 identifies distinct node categories, each ranging in significance
 and with particular characteristics that reveal key aspects of the
 informants' views. In this way the researcher can use the nodes to
 reveal the voice of the experts, and build a scientifically rigorous
 set of results from a qualitative database.