“Thus your data shimmers.” (Lisa Robertson)
I’m really enjoying preparing our weekly Wednesday talks. I’ve now had a chance to cover the theory and methods sections in some detail. This week I’ll be talking about writing the analysis. Because I’m trying to keep these talks applicable to the different levels that students are working at, as well as the full range of CBS degree programs, I’ve found myself occasionally waxing philosophical. I think this week’s talk will be a little more practical, but still general enough, I hope, to be of use to everyone. The overarching theme will be that of using your data to support factual claims about the object you have studied. That is, in our analysis we’re always moving from our direct observation of reality to our interpretation of that reality. It will be useful to think of each paragraph as including both an interpretation, which will be expressed in the key sentence, and some observations, which will support the factual claim it makes. That is, each paragraph in your analysis will assert a fact on the basis of your data.
Let’s begin with the data, which we have talked about before. It consists of what you have directly observed. In ethnography, it’s your record of what what people have done and said. In survey research, it consists of how they filled out your questionnaire. In financial market analysis, it consists of the stock prices you have exported from a relevant financial database. In discourse analysis, it’s the archive of documents you have collected. However you have gathered it, you deploy it in your analysis section by quoting (words or figures) as they appear in your data set or summarizing aggregates. Your statements about your data are true or false in a highly objective and unambiguous way. People either said what you quote them for or they didn’t. A certain number answered “yes” to a question and another just as certain number answered “no”. You just have to count them. The documents either invoke the codes you’re looking for or they don’t.
But an analysis is not just a summary of your data. You have collected the data in order to represent the facts as they are, independent of your data and your analysis, and making your data represent facts always requires an interpretation. The amount of days employees are off on sick leave in a particular company is a data point. Whether the company has a stressful working environment is a fact to be determined by your analysis. You gathered the data in order to determine the fact but, interestingly, if your readers want to observe the same fact, they don’t have to use the same data. Facts are not made of data, we might say, they just “give off” data. Like an astronomer gathers the light from a star, you design your instruments to be sensitive to data about the people you study. To borrow Lisa Robertson’s image, data is a “shimmer” on the surface of your facts. The data are ultimately ephemeral (which is why you have to keep a good record of them); the facts are made of sterner stuff.
Again, your analysis doesn’t just describe your data; it doesn’t just make claims about your sample. It makes claims about the world in which we live out of interpretations of your data. It tells us what your data has shown you, what it has taught you about your object. As I have said before on this blog, this lets you think of each paragraph in your analysis as repeating a simple pattern: the key sentence tells us what you mean and the rest of the paragraph tells us how you know. The key sentence may tell us what the people you have studied believe or desire, but the rest of the paragraph will tell us what they said or what they did to make you think so. Present your interpretation in the key sentence and build the rest of your paragraph around your observations. Obviously, you should make sure your observations support your interpretations.
It is tempting to see the analysis as a “write up” of your data. If we’re working with qualitative data, we’ll often start with memorable quotes from our interviews or striking observations from the fieldwork. Quantitative researchers might start with the “significant” results in their contingency tables. Either way, the writer thinks of their prose as a way tying these data point together, connecting the dots. But it is much better to organize your analysis around a set of claims about the world — statements of actual, ordinary fact. You will ultimately be composing a finite series of paragraphs, each of which says one thing, and supports that claim with your data. So plan out your analysis section as a series of claims that you are able to support, not just a number of themes inspired by your data. After all, your readers don’t just want to know about your data; they want to know what your data shows us about the world in which we live. They want your observations and your interpretations of them.