Most thesis or dissertation research involves collecting a large amount of primary data. As a novice researcher, it is easy to feel swamped by the sheer volume and variety of data you have gathered. Even quantitative or qualitative you have already collected and analysed can appear daunting to derive meaning from.
Students often tell us it is difficult to draw out the key ‘messages’ from their data set, which has been, for example, captured in chart form. They ask how this information can be developed into findings and conclusions. We advise that at this stage of the research process consider employing a technique, called spotlighting. This useful procedure helps identify the most significant data and explore the connections between them. Other similar techniques tend to emphasise logical categorisation (that is, putting data in boxes) whereas spotlighting pinpoints the connection between data and clusters of data.
Spotlighting is essentially a scanning process. It assists you to look through data quickly and screen them, so that you can swiftly develop a shortlist of ideas. These ideas, in turn, prompt further ideas, debate and discussion around a data hotspot. We recommend you follow five simple steps. First, drawing your data together in one place. Second, ‘scanning’, ‘over-viewing’ or ‘speed reading’ the data. Third, gathering initial thoughts and impressions (what is called a ‘trigger spot’). Fourth, clustering other pertinent data around that spot (involves generating a ‘hotspot’). Fifth, and final step, linking the data hotspot to your research questions or hypothesis.
The spotlighting technique works best if you can produce your data in physical form (or are able to write software applications to interrogate your data at scale). Take the example of an archaeologist trying to make sense of the artifacts they have uncovered from a dig. Using spotlighting, they might spread them out on a large display table to examine them. With business and humanities research, if you have collated and analysed quantitative data from questionnaires you could, similarly, print off the results on sheets of paper. You could then spread them out on a large, open floor area. If there is a very large amount of data (the answers, for example, from 1,000 questionnaires), you could summarise the data first using a spreadsheet or a statistical software package. Then print off the resulting charts, diagrams and tables, and pin them to a wall. Alternatively, you may have collected qualitative data from conducting interviews, and subsequently transcribed the conversations verbatim. Using spotlighting, you could enlarge the size of the text, print off the transcripts and spread them out somewhere convenient.
You are now in an ideal position to review the data you have laid out. Pull out or highlight, perhaps using a post-it note or fluorescent markers, any data that seem interesting. For example, data patterns and trends, called spots of data, which could be intriguing to explore. Look around to see if there are any related data that you can associate with the ‘spot’. You could, for instance, physically move all downward trending charts together into one cluster and draw a circle around them. Each of these clusters represents a potential hotspot. Sometimes you may need to join hotspots together, or at other times, keep them separate. It all depends on the situation. The focus is on identifying the meaning and/or implication of each hotspot. Consider, for example, how do the data relate to each other, what impression does it give you, what are its implications, what are its consequences, how does it connect to you personally as a researcher, and so on?
Next, ask yourself, which hotspots (or combinations of them) could provide answers (or partial answers) to your research questions or address your hypotheses? Write out your ‘free’ thoughts on each hotspot (or combinations of them). Then use these structured thoughts to develop a framework for your findings and conclusions. This helps you to identify groups of related data that are connected to each other, and in some definite way, to your research aims and objectives, and questions or hypotheses.
Whether you are interrogating quantitative or qualitative data, it is too easy to become absorbed in the data swamp. Spotlighting helps you resist attempts to ascribe meaning to data too early in the analytical process. The technique acts as a reminder to step back and take a broad perspective to identifying any patterns, or spots, in them. Like the archaeologist, attempt to get the ‘big picture’ first. This could mean, for instance, determining recurrences of the same number or pinpointing a series of numbers occurring frequently in tables of data. It could entail noticing the presence of the same/similar words appearing in pages of text or several charts that all show, collectively, consistent trends. A crucial aspect of spotlighting is taking this high-level, or ‘bird’s eye’, view. Be like a bird gliding on wind and then harnessing the energy from currents to hover over ground. In other words, reflect on the data you have spent time and energy collecting collected. Take time to look down on the data and wait for meaning to come to you. Use spotlighting to help you avoid getting too immersed with the detail of the data, so that you maintain a strategic overview.
Spotlighting reduces the extraneous data your mind needs to focus on. This allows you to highlight what is important and significant from your research. Our students tell us the technique is particularly useful after the analysis and interpretation of data is complete, and they are moving on to discussing, reporting and presenting their findings and contributions.
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Thank you for writing this post!
This actually answered my problem, thanks!