It is not uncommon to experience what this student described above. You have collected all your data (you may have also analysed some of it, at least initially) and now you are wondering what to do next. You may be wondering not only how to present it, but also what to present in the first place.
I think what is important to remember at this stage, is that the main aim of your work (whether it’s a dissertation, PhD thesis, or an academic article) should be to provide contextually grounded (that is, based on your collected data) explanations/claims, etc., that will ultimately make some contribution to the existing knowledge (remember when you were explaining your rationale and why your study is needed?). Also, although it may be tempting to discuss all of these intriguing insights that you found in your data, I would suggest that if your time is limited, you should just focus on answering your research questions. If you have found something that is really interesting but does not really help you answer these questions, you may always keep it for future publications, or even for your current work, if time permits (although, from my own experience I can say that by the time you are done describing and discussing your results, the last thing you will feel like doing is adding more stuff to it), but now I suggest you just stick to your research questions.
So, what have you found in your data that is relevant to these questions? What have you learnt from your data?
I know, this is probably the exact thing that you are struggling to figure out when you look at this mass of opinions, stories and ideas in front of you, so I suggests you do the following exercises to try to get your head around its relevance for your study (and therefore, to get a clear idea of what to discuss in your results/discussion chapters), as well as to decide what else to analyse.
Start free writing about what you found in your data (to learn more about free writing, watch this video). Spend at least 5 (but if you feel that you will need more, you can make it 10) minutes writing about what you have learnt from your data – what are the things that you know now, but didn’t know before you collected/analysed this data? Don’t worry about the structure, just try to get as much on paper as possible. This may also include any assumptions or reflections resulting from you being familiar with the data.
Once you have finished, create a table with each column being one of your research questions. Now, read through what you have written during your free writing activity and try to copy and paste extracts from it into a relevant column. In other words, try to decide which things that you have learned from your data answer a particular research question (it’s ok if some of these things fit into more than one column).
Now, that you have a table with some indication of which parts of your data are relevant to your research questions, you may have a good starting point for writing up your results. Of course, since these ideas were sketched during a free writing activity, you may want to come back to your data and have another, more detailed, look at it, but with some sort of structure that you have in front of you now, at least it is a bit easier to see what else you still need to do.
The table you have created may also be used for another activity that I often do when I try to interpret my data and develop some sort of a “theory” that would help me make sense of what I have in my data.
For this activity, you may either use the table you created for Activity 1 or you may just start from scratch, as I often do when I analyse my data. This is another free writing activity (did I tell you how much I love this technique? 😊). This time, write (5/10 minutes) about your data, but with your research questions in mind. Try to explain (you can imagine that you are presenting at a conference and you are talking to people who have no idea about your field and your study – for some reason it tends to help me) how what you have found in your data helps answer the particular research questions you have. This time, I want you to “think big” and not be afraid to make assumptions about the relationships found in your data, saying how some things you have found “possibly indicate” or “are likely to” mean something. Try to think of what (possibly) bounds your data and your research questions together, what may be the reason someone said something and some other person didn’t, why YOU THINK a particular group of people had a different opinion from another, etc. Try to really open your mind and be creative in how you view your data.
Now, what is the purpose of the above theorising if so much of it are merely your assumptions, you may wonder. Doing this has always helped me “dig deeper” into my data. Once I have written down all these “working hypotheses” about my data, I start looking at my data again and try to determine if these working explanations may be, in fact, valid. Doing this helps me determine what else I need to focus on, what is lacking from my results. I try to find not just the evidence that would support these claims but, most importantly, the data that would dismiss them. And sure enough, most of these hypotheses will be dismissed. But the good thing about it is that as you focus on investigating these hunches, you are developing your understanding of the data and very often you are developing new explanations. To confirm these, in turn, you are looking at another, and then another, element of your data, until you gradually arrive at the point when everything does seem to fit together. You are building your theory of what’s happening in the data (note that you don’t actually have to develop a new theory – what I mean by a “theory” here is your explanation of your data, not a universal theory of a given phenomenon), and the closer you are, the clearer it becomes to you what else to investigate. This is the technique I have been using in all my data analysis assignments.
As I noted above, these activities are helpful for the purpose of determining which findings to choose for our Results and Discussion chapters. They will help you focus on the findings that are relevant to your research questions, as well as show what else needs to be analysed in order to arrive at a coherent explanation of what you have found. As I am developing this “theory”, I also like to draw diagrams with all the elements (and relationships between them!) that constitute it. Again, this helps me understand whether this explanation makes sense and whether I have any data to support it. Drawing diagrams with the relationships between different elements also helps to think of major themes emerging in my data. As I am working out different elements of a diagram and the relationships between them (e.g. “teacher’s questions” seem to influence “students’ engagement”), I label these parts of the diagram (e.g. “the influence of teacher’s questions on students’ engagement”). These labels are “themes” that I will investigate further. Also, most of the times these themes will eventually become the main headings in my Discussion chapter.