How to: Academic Writing for the Humanities Part 1

This presentation was originally called “Using Grounded Theory (sorta) and Qualitative Data Analysis (sorta) for Academic Writing in Religious Studies.” There is no reason why it needs to be limited to religious studies. I also didn't want to pigeonhole it too much with the GT and QDA part -- even though I found I use those two things quite a bit in my writing. Many will probably notice they use some sort of GT intuitively. While understanding GT and how I suggest using QDA and coding is NOT necessary to write well, for those struggling to find their writing groove, it could be a systematic way to improve the process.

What is the point of this presentation?

I've often heard grad students in the humanities describe difficulties with writing. Even when the end product was of high quality, the process of writing itself was often daunting. It doesn't need to be. It's a lot of work, but need not be difficult. Along with these complaints about intimidation, I've also heard colleagues make comments such as “how am I supposed to meet expectations for this term paper when in undergrad no one ever taught me how to write like this?” Writing at a graduate level is not easy. And when our high school and undergrad programs don't teach us the skills needed to analyze data at a graduate level, we're often left floating downstream and feeling like we aren't meeting expectations: either our own or our professors. This presentation is made with the goal of (1) making the process or writing easier and (2) showing some strategies for meeting expectations for quality of writing.

What is Grounded Theory?

The short answer is that Grounded Theory (GT) is a method employed mainly in the social sciences for developing research questions, constructing data collection techniques, collecting data, and constructing theories. Central to GT is the idea of “leaving baggage at the door.” While they play a part towards the end of the process, preconceptions and existing theories should be kept at bay for the time being (the “theory” should be “grounded” in the data). Also important to GT is the concept of a spiraling methodology. After a research question is decided upon, a preliminary literature review could easily lead to a revision of the original research question. This would then lead to further refinement of the literature review. The same is true of data collection. Data may show up that leads to a revision in collection techniques or even a step back all the way to deciding on a research question. This visual aid illustrates the spiral (or 2 steps forward 1 step back) nature of GT (from Qualitative Research Methods for the Social Sciences by Bruce L. Berg).

But I'm not doing a social science project with surveys, questionnaires, and interviews. Why should I use GT?

While GT was developed and is primarily used by social scientists doing these sorts of projects, it can just as easily (and effectively) be used for academic writing. The main difference is that the data collection and research design will be replaced with library, journal, and database searches. Instead of getting data from people through surveys and interviews (or videos, photos, etc. in the case of content analysis), we get our data through reading. Lots of reading. Yet the data gathered through reading can still be used within a GT method by using the spiraling technique listed above. While reading journal articles related to a research question, data may show up that leads to a revision or refinement of your research question. Or it might inspire a whole new research question altogether. One of the primary advantages to GT is that it helps keep confirmation bias in check.

 

OK, so what's the process?

A first step is to figure out a general topic for your paper. Remembering the spiral method, this step goes along with a literature review which will further refine the topic . . . which will lead to a refined literature review . . .which will lead to a re . . . you get the idea. Hopefully, you will end up with a research quesiton or series of questions. Write these down and organize them. Some questions will be “subquestions” of others and some will be different, yet related, categories entirely. From here, you do those handy Boolean searches for books and articles which will help answer your research question(s). These are what will become your qualitative data (QD).

Note: Almost everyone who writes in the humanities does this -- whether they consider they sources QD or not. While I go through how to use QDA software below and in Part 2, for papers with fewer sources (say . . . less than a dozen), it is probably not worth the time to use the software. But the same idea of coding (see part 2) applies -- just done mentally.

Luckily, most of this QD will be in the form of PDFs. Most PDFs allow you to copy and paste text. Of the dozen or so I used for this paper, only one prohibited this. So the next step is to read through all these articles. I had the PDF open in one window and my word processor open in another. Whenever I came across an excerpt I thought would help with my research question(s) (or that simply seemed interesting for further review), I copied and pasted it into my word processor. Two notes on this: (1) use .txt as your file type as this is what WeftQDA accepts, (2) before each excerpt put the authors initials and the page number on which the excerpt is found. This second step is important later on in the process.

 

Part two will look at how we deal with all this data.