The Identity Sorting Dials are a really useful tool to start (again, I’ll say start) thinking about the interplay between human considerations like fit (“can I see myself in this category”) and ease (“how difficult is it for me to interact with this data process”) and technical priorities like certainty (what kind of confidence interval do our categories lead to) and specificness (what is the resolution of the data we need).
Note: Ok, so ‘specificness’ isn’t totally a word, but we wanted to make sure that it didn’t get confused with technical statistical concepts like ‘specificity’ so we’re going with it!
The specificness dial refers to how specific each group is. The big difference between the Specificness Dial and the Fit Dial is that Fit is about how well a category represents an individual. If your sexual orientation categories are “Gay, Lesbian, Straight”, there is no good Fit for someone who identifies as bisexual.
Specificness deals with varying levels of resolution but should Fit equally well at all levels.
For example, if I’m from Toronto, a “North American” category is appropriate to me; it is a good fit because I definitely identify as “North American”. I agree that I can be effectively described as North American, but it’s not as precise or specific as Canadian. And Canadian is not as exact or detailed as Ontarian or Torontonian. All these levels fit me, but each level of Specificness is appropriate for answering different questions.
Let’s look at the identity component of religion to get a better understanding of Specificness. If I’m trying to use data to answer a question about how someone’s Christianity is affecting something, I could use varying levels of Specificness:
What is your religion?
B) Not Christian
What is your religion?
C) Eastern Orthodox
E) Other Christian denomination
F) Not Christian
What is your religion?
- a) Roman Catholic
- a) Lutheran
- b) Methodist
- c) Presbyterian
- d) Baptist
- e) Evangelical
- f) Mormon
- g) Mennonite
- h) Amish
- i) Quaker
- Eastern Orthodox
- a) Greek Orthodox
- b) Russian Orthodox
- c) Bulgarian Orthodox
- a) Ethiopian Orthodox Church
- b) Coptic Orthodox Church
- Other Christian Denomination
- Not Christian
The point of the four Identity Sorting Dials is that when you adjust one, you affect the others. If I am trying to answer a particular question about the impact of someone’s Greek Orthodoxy, I might want to ask them very specifically about it. But if I’m interested in something more general about Christianity, then I might be able to pump up my Certainty dial with the much larger sample size of “Eastern Orthodox”, or even just “Christian”.
Of course, we could delve into nearly endless levels of specificity. This is also not a comprehensive list of religious denominations by any means. Furthermore, if someone identifies in the “Other” or “Not Christian” options, like someone who practices Hinduism say, they certainly have a Fit issue; they don’t see a category that describes them well, and I, as the analyst, won’t be able to generate very precise information about their religious identity if they are in a catchall category like ‘other’. Whether that’s a problem depends on your situation, the questions you are trying to answer, and the people you are trying to center in your work.
But if I’m Greek Orthodox, and I’m presented with any of these options, I can easily see myself in a category each time. In the first option, I would select option A) Christian; in the second, option C) Eastern Orthodox; and in the third, option 3a: Greek Orthodox. All these categories fit me, but they have different levels of Specificness. Describing someone who is Greek Orthodox as a Christian may be more general but just as accurate, much like calling a Torontonian a Canadian.
However, that’s not always true. Different people have different relationships to categories. Many Mormons might not consider themselves Protestants. I have a neighbor who does consider themself a Torontonian like me, but at a nation level, he identifies as Cree, not Canadian. These are examples of Fit issues, and they are critical to flag and adjust for.
When we construct categories and dive deeper into them, creating subcategories, whether that’s national, provincial, or municipal, or denominational, we must pay attention to the effects of Fit and of Specificness for all the people we care about in our projects. If we don’t, we may inadvertently create ill-fitting categories that misrepresent people (bad for feelings and really bad for accurate science). We may get so specific that we can’t use quantitative data to create reliable meaning for such small groups or go so broad that our results are useless.
When creating, evaluating, analyzing, or reporting on categories of people, we must make choices across Fit, Specificness, Ease, and Certainty intentionally, transparently, and in alignment with our equity priorities. When it comes to the Dials, there’s no “right answer,” just the right sweet spot this time, for this project.