At a conference, when you ask somebody to tell you about their current project, what do they typically say? I often get a puzzling response: instead of beginning by telling me about an idea, the person starts by describing their data. They tell me they are using survey data they have collected, or data from an archive, or data they’ve scraped from the web. As they go on at length about the nature of the data, I have to interrupt them and ask for what purpose the data will be used. Then, I’m likely to get a description of an analytic method or computer software. It’s almost as if they have devoted most of their working hours to thinking about what they can do with the data they have collected – – or will collect – – and very little time to the question of where their project fits into some larger scheme. Sure, ensuring data preparation and analysis is done correctly to allow for correct statistics and so forth is vital, but it’s not the end all be all.
I’ve realized that this response partially explains why many graduate students have such a difficult time in writing a thesis proposal. Two kinds of problems result from a “data first” strategy.
First and most obviously, beginning with data considerations may lead to the unintended outcome of writing a theoretical framework and conceptual model, complete with hypotheses, that are totally framed around what the data permits. In the worst-case scenario, this can resemble the kinds of narratives corporate historians write when they begin with what they know about their firms in the present and then build a story to suit. Researchers may anticipate journal reviewers’ biases toward “significant” results and may simply wait to begin writing their story until they’ve conducted preliminary analyses.
In the writing workshops that I offer at conferences, I often have students tell me that they wait to write the introduction to their paper or thesis until after they’ve done the “analysis and results” section. This is certainly a safe strategy to follow if one wants to economize on doing multiple drafts of a paper, but it goes against the spirit of disciplined inquiry that we try to engender in our theory and methods classes.
Second and far more damaging from my point of view, following a data first strategy severely constrains creativity and imagination. Writing a theoretical introduction and conceptual model that is implicitly tailored to a specific research design or data set preemptively grounds any flights of fancy that might have tempted an unconstrained author. By contrast, beginning with a completely open mind in the free writing phase of preparing a proposal or paper allows an author to pursue promising ideas, regardless of whether they are “testable” with what is currently known about available data.
When I say “write as if you don’t have the data,” I’m referring to the literature review and planning phase of a project, preferably before it has been locked into a specific research design. Writing about ideas without worrying about whether they can be operationalized – – whether in field work, surveys, or simulations – – frees authors of the burden they will eventually face in writing their “methods” section. Eventually, a researcher will have to explain what compromises have been made, given the gap between the ideas they set out to explore and the reality of data limitations, but that bridge will be crossed later. Rushing over that bridge during the idea generation stage almost guarantees that the journey will be a lifeless one.
Even if someone is locked into a mentor’s or principal investigator’s research design and data set, I would recommend they still begin their literature review and conceptual modeling as if they had the luxury of a blank slate. In their initial musings and doodles, as they write interpretive summaries of what they read, they might picture a stone wall that temporarily buffers them from the data obligations that come with their positions as data supplicants. Writing without data constraints will, I believe, free their imaginations to range widely over the realm of possibilities, before they are brought to earth by practical necessities.
So, the next time someone asks you about what you are working on, don’t begin by talking about the data. Instead, tell them about the ideas that emerged as you wrote about the theories and models that you would like to explore, rather than about the compromises you will eventually be forced to make. The conversation will be a lot more interesting for both of you!
After writing my blog post, I came across a book by Sandra Yancy McGuire and Stephanie McGuire, which argues that the worst mistake students can make in doing homework problems is to first look at the solution. By looking at the solution at the back of the text book, they deny themselves the opportunity to try to work through the problem themselves, to see if they can come up with the solution on their own. The McGuire’s point out that if you begin with a solution, your frame of reference narrows immediately to that offered by the people who wrote the textbook. However, there might be other ways to solve the problem. There may be, in particular, a quicker or more creative way to solve the problem.
Trying to solve the problem without looking at the solution is more work, but it also leads to deeper learning. Hard-won solutions are a heck of a lot more satisfying than prepackaged easily won solutions. And remembered much longer!
Here is the reference:
Chapter 5, “Metacognitive Learning Strategies at Work,” in the book, Teach Students How to Learn: Strategies You Can Incorporate into Any Course to Improve Student Metacognition, Study Skills, and Motivation, by Sandra Yancy McGuire with Stephanie McGuire. Published by Stylus Publishing, LLC, 22883 Quicksilver Drive, Sterling, Virginia 20166-2102. https://sty.presswarehouse.com/books/features.aspx