DH Final – Process Statement

Theoretical Rationale

Just as with religion, American’s seem to have a set understanding of what it means to do ‘data’. I have addressed the issue of collecting and observing data in previous posts but want to reiterate a few key ideas about defining data here.

First: Data does not speak for itself — Scholars and the general public, as knowledge consumers and producers, need to start asking critical questions about the data presented to us.

This starts with understanding that observation is much more complex than it is often given credit for. I have used the following example several times in the past since I first heard it from my advisor, but it demonstrates this complexity beautifully. From philosopher Karl Popper’s Conjectures and Refutations: The Growth of Scientific Knowledge:

“. . . though beetles may profitably be collected, observations may not.
Twenty-five years ago I tried to bring home the same point to a group of physics students in Vienna by beginning a lecture with the following instructions : ‘Take pencil and paper; carefully observe, and write down what you have observed!’ They asked, of course, what I wanted them to observe. Clearly the instruction, ‘Observe!’ is absurd. (It is not even idiomatic, unless the object of the transitive verb can be taken as understood.) Observation is always selective. It needs a chosen object, a definite task, an interest, a point of view, a problem. And its description presupposes a descriptive language, with property words; it presupposes similarity and classification, which in their turn presuppose interests, points of view, and problems.”

Popper, 1963

Objects of study — like religious families — do not explain themselves just by existing. They require selection on the part of the observer. Before an object becomes important, it is isolated from the other objects around it. Naming a thing prescribes importance because it was chosen to be observed in the first place. All of this is to reiterate that to be the person to name an object is to be in a position of social influence.

Many modern religious studies scholars have debated the role of the scholar in studying religion. As the interpretative essay of this project has hopefully demonstrated, defining and categorizing religion can be more convoluting than clarifying. The question is; if naming what religion is in the first place is so difficult how should scholars go about observing, documenting, and studying the-thing-that-must-not-be-named?

This debate is not unique to religious studies or the humanities, generally. Every field of study has debates on methodology but religion is a more obvious example of this issue as it can be understood as a unique, individualistic, firmly-held belief (and as such, is extremely difficult to measure).

As with Popper’s analysis, I often find myself returning to Craig Martin’s NAASR article, “The Thing Itself Always Steals Away” (mentioned in previous writings and blog posts of mine). The issue — specifically Martin is discussing the Realist v. Antirealist debate — is not whether or not religion actually exists or not but what is accomplished when people name something as such. Whether it be tax exemptions for an aquarium, social prestige, or data collection, naming a thing has more influence than is often admitted by scholars and knowledge consumers.

Technical Rationale

Each dataset was first organized into a pie chart, and although these sorts of charts are not the easiest for human eyes to visualize, they do an efficient job of demonstrating the varying number of categories used by each survey. Because the data was collected from a varying number of survey participants, each pie slice represents that religious family category as a percentage of the total number of interviewees. The overwhelming kaleidoscope look of the pie charts is supposed to be a bit unsettling as it reinforces the variance between each definition of religious families. It is not that scholars need to find the right definition and stick with it but that too much variation can render data useless. It is not about founding the right definition its about understanding what classification

The charts not only help demonstrate the multitude of ways that lines of similar categories can be drawn but also to show how the numbers (that so often are taken as fact without question) can be contradictory based on the way categories are fabricated.