Dr Brainerd Prince
We’ve spent a lot of time looking at how a research project begins in the real world of the researcher and then transitions into the world of texts, within the boundaries of a discipline and discourse, addressing a contemporary debate that becomes the focus of the research. This debate translates into a research question, which the research seeks to answer. In the previous columns, we established this line of inquiry and then recently began to look at how to get answers to the research question, which we called the green bucket. We said this is where we find answers to the questions we have raised.
The green bucket, we argued, is the field that the researcher ploughs to gain insights and answers and it can vary—it might be a text, an anthropological field consisting of communities or people, a professional practice, or a site of experimentation. This field, which we refer to as the object of research, is the place from which we derive solutions and answers to our raised questions. We briefly touched on this but have yet to focus on how to collect data, how to analyze it, and how to ensure that the insights we derive from the data are valid, credible, and effectively answer the raised questions. We will return to this later in our upcoming columns.
Methodology is one of the three integral pieces of any research project. The first piece, symbolized by the red bucket, is the research question. The second piece, symbolized by the green bucket, is the object of research, or the space from which solutions arise. This third piece, symbolized by the yellow bucket (following the order of traffic colours), prepares us by providing the framework and lens for the research—this is the methodology bucket. But methodology, or at least parts of it, are directly related to the object of research and the type of data we hope to harvest for that particular project. Therefore, we had to briefly explore the green bucket before focusing on the yellow bucket.
The foremost insight we gained from our previous work on the green bucket is that the object of research must provide solutions for our research question. The object of research is called our primary source, and everything written about our primary source is part of our data and worthy of investigation. The primary source or the object we want to study to get solutions could be a person, text, phenomenon, group, experiment, company, organization, or even a practice or process. It could be anything in the world that we could systematically study to gain insights and solutions appropriate to the question we raised at the beginning. The bottom line is that by briefly exploring the green bucket, we've identified the primary source of our research.
With this background let’s return to the yellow bucket of methodology. When we think of the term methodology, a question often arises: what do we even mean by methodology? Methods – yes, we understand that. But what exactly is a methodology? The easy answer is that it’s a frame of reference, an explicit acknowledgement of the position from which the researcher looks at the data or the object of research.
One might wonder, if data is in front of us, why do we need a frame of reference? Why do we need any lens? There’s the data—just go collect it. This has been the practice, particularly in scientific and much of social science research, for a long time. However, what it does is that it makes invisible the eyes of the researcher, the subjectivity of the researcher, their intellectual background, and their intellectual commitments.
Usually, I give the example of the Mona Lisa painting hanging at the Louvre in Paris. If I were to look at it, I would be searching for those haunting eyes I’ve heard about, moving my head to see if they really do follow me. Interestingly, if you try this with any other photograph, you’ll find that every eye of any person in any photograph follows you hauntingly because it’s not their eyes following you—it’s your eyes fixed on them even when you move your head. This phenomenon made the Mona Lisa famous in recent history, and everyone praised it for its haunting eyes. Others praised it for the beauty and the enigmatic smile of the person in the picture. But the person was not an extraordinarily great historical figure. She is said to be Lisa Gherardini, the wife of Francesco del Giocondo, a Florentine silk merchant. Matter of fact the painting’s Italian title is La Gioconda.
Then came Dan Brown in the early 2000s with his historical fiction, writing about famous paintings like the Mona Lisa with cryptic messages. So, if someone read Dan Brown’s book and travelled to Paris, they might look at the Mona Lisa in the Louvre, searching for the clues the fictitious novel has fed them. But if you were an art student studying the history of art, particularly the High Renaissance art of Europe, and you had studied various styles and key features of High Renaissance paintings, then you would come closer to the painting to keenly observe it. You would look at the face, the boundary between the face and hair, noting the absence of lines—a technique known as ‘sfumato’ that Leonardo had mastered, and the excellent masterly rendition of that style in the painting is what makes the Mona Lisa truly valuable and priceless.
What I’m trying to illustrate is this: data is not as innocent as it seems. You will only see what you can recognize. Recognition is always a form of rethinking, using all your previous knowledge, experiences, and training to interpret and even see the data in the first place. This is phenomenal because the same dataset can be captured completely differently by people with different training.
In academic research, we symbolically put aside personal preferences and experiences to carve out a space for an intellectual position or academic leaning. The lens or framework we bring to data—whether it’s from gender studies, sociology, science, or philosophy—affects how we see and interpret it. This is where methodology becomes immensely important. It’s not just about methods but about the eyes, the perspective through which we view data.
When I think of methodology, I think of eyes and hands. The hands represent the doers—the methods required to collect data from the object of research, which vary depending on whether the data is qualitative, quantitative, experimental, or practice-based. Methods are determined by the type of data we propose to collect, which is why we needed to briefly explore the green bucket first.
But today, I want to focus on the eyes. The eyes determine how the data appears to us, what vocabulary and taxonomies we use to capture it, and how we view our own contribution to the research project. The eyes are powerful because they reflect the theoretical framework that we bring to data collection and analysis. This framework cannot be left at the disciplinary level; it must be made narrower, and tailored to the specific phenomenon or text we are studying.
Selecting the right theoretical framework can be quite challenging. It needs to match the nature of the data, be relevant to the core material, and support the final point we aim to make. For instance, if our conclusion is centred on societal power dynamics, applying a Foucauldian perspective could be effective. On the other hand, if we're exploring how knowledge is shaped by tradition, a MacIntyrean approach might be more fitting. Often, the most suitable framework becomes clear as we immerse ourselves in the research project. This process is crucial for shaping our unique voice and argument, much of which takes shape even before we start gathering or analyzing data.
Ultimately, the theoretical framework defines the limits of our research, helping us focus our work. It guides us in selecting the relevant data, ensuring we stay on track to address the research question we’ve set out to explore.I will end this column with a story told by Daniel Boyarin in the ‘Introduction’ of his book ‘Borderlines’:
‘Every day for thirty years a man drove a wheelbarrow full of sand over the Tijuana border crossing. The customs inspector dug through the sand each morning but could not discover any contraband. He remained, of course, convinced that he was dealing with a smuggler. On the day of his retirement from the service, he asked the smuggler to reveal what it was that he was smuggling and how he had been doing so. “Wheelbarrows; I've been smuggling wheelbarrows, of course.”’
The ‘wheelbarrows’ represent our conceptual lens. Often, those who say they don’t need theoretical frameworks are unfortunately smuggling in their eyes, their lenses, across the boundaries of one discourse to another, allowing them to be implicit and using them invisibly. But today’s research requires us to make explicit the wheelbarrows of our theoretical frameworks, to keep the expectations of the research clear, to give boundaries to our work, and to enable us to articulate an argument which is scaffolded by a particular way of looking at data.
Dr Brainerd Prince is an Associate Professor and Director of the Centre for Thinking, Language, and Communication (CTLC) at Plaksha University, Mohali.
