1 - Introduction

Summary

Moving beyond the mystery and magic of data analysis

All research projects require a research design, phases of data collection or data production, and careful processes of analysis that lead to the dissemination of key findings. The activities of analysis have been defined as a process of attention, reflective systematic inquiry, and exploration.

To analyse is to examine or study something closely to understand it better or discover something about it. It means to break it down into its components to study its structure and find out what it is made up of by identifying its constituent parts and how they are put together. Data analysis involves all these. It involves the examination of data in detail in order to first understand it better and be able to draw conclusions from it. (Akinyoade, 2013, p 4)

Given its central importance, there could be an assumption that the nuts and bolts of analytical procedures are clear to researchers, and that they are well aware of the steps required to move beyond the mass of data accumulated in a study to the distillation and refinement of key points that attend to research aims and central questions. However, data analysis has been positioned as ‘the most complex and mysterious of all of the phases’ of research and ‘one that receives the least thoughtful discussion in the literature’ (Thorne, 2000, p 69). This impression of mystery and mystical journeys is sustained in the reporting of research studies that only offer a glimpse of the concrete steps of the analytical process.

Some authors use language that accentuates this sense of mystery and magic. For example, they may claim that their conceptual categories ‘emerged’ from the data – almost as if they left the raw data out overnight and awoke to find that the data analysis fairies had organised the data into a coherent new structure that explained everything. (Thorne, 2000, p 69)

This mysterious phenomenon, and the lack of insight into the processes by which conceptual leaps that generate abstract theoretical ideas from empirical data, have also been noted by other scholars who call for ‘greater openness and legitimacy for reflexive accounts’ of the ‘how’ of data analysis (Klag and Langley, 2013, p 149).