Firstly, I’m aware that I have broken the first ‘rule’ of blogging, which is to keep the posts short, and keep them coming regularly, but I had a bit of a hiatus due to other interests and demands over the summer. Hopefully, now to get back on track
Starting to record the new data which is being gathered as part of a research project, whether a long-term study like a PhD, or a quick toe-in-the-water project, is the most crucial, but perhaps the subtlest stage of the research. If you gather too little data, the project may flounder even before it gets started; too much data, and a metaphoric mountain of results can be generated by cross-correlation and individual analysis, which can paralyse a project almost as quickly as having no data at all. Then there is the question of what is the “right” data? How will I know it when I see it? In reality, it is as likely to be different for every individual project as the diversity of methods of data gathering. The correct procedure, of course, is to recognise that recording the correct data is integrally dependant on selecting the correct research methodology, and in carefully selecting how the data will be collected, coded, and stored in the future.
One of the most impressive records of research data that I can remember, is from a scientist who was studying birds of prey, and his handwriting in an old notebook recorded what seemed to me to be almost every conceivable factor which might influence nesting success, including several factors that I, personally, would never have begun to consider relevant. He was of course correct, for it is often the correlations with hidden, and often apparently spurious, information which leads to the really stunning breakthroughs in research projects. There are many different ways of the recording research data that you might collect, and there is no one-size-fits-all solution. If you are interviewing people, there is a choice between taking notes, audio recording, or video recording; all these methods have their advantages and disbenefits. Taking notes is less obtrusive, but also can be distracting for the researcher. Audio recording can be done easily with a digital recorder, or a suitable app on a smart-phone, but some people may be more guarded in their responses when they are being recorded, and there is also the problematic issue of what to do with all the data you have gathered. Gathering a huge mass of data can be attractive, but it needs to be proportionate to the scale of the project, because there is little point in generating a mountain of data if 80% is left unanalysed and unused. Great care needs to be taken to strike a balance between collecting a good data-set which provides rich possibilities for future analysis, against de-motivating your participants by presenting them with huge questionnaire or over-long interviews. Similar constraints apply when conducting laboratory experiments, fieldwork, or desk-top studies.
Finally, in addition to having to consider your recording requirements in terms of how you propose to codify and analyse the potential results (there is little point in collecting data so randomly that it cannot be interrogated effectively) there are the issues of long-term storage and access to the data. The research supervisor has a crucial role here, not simply in helping to shape what the research students proposes to gather, or how that might be analysed and interpreted, but in providing the continuity which may extend over several decades and overlap with numerous related research student projects. In an increasingly digital and open educational society, not simply the research results, but also the raw research data is also becoming more open and accessible. It is becoming more possible and more likely that scholars coming after you will read not just your conclusions, but also your original data recording notes, so think carefully about what you collect and how you record it!