This tutorial will discuss the way you determine the categories and classes of qualitative data. you will learn about:
This tutorial is going to regard practical concerns regarding categories and classes of information when you're dealing with qualitative data.
The ways we choose to separate the data by differentiating characteristics. Too many or too few categories can be problematic.
Something like hair color is a pretty straightforward example of how to categorize data. Assuming people are using their natural color, we can typically break it up into probably about these six categories.
So qualitative data is split into categories.
Sometimes it's not so obvious what category people belong in. So what about someone who looks like this?
Does this child belong in the blonde category because she has some blonde hair? She also pretty clearly has some brown hair, so how do you categorize her? Do you put her in the brown category because she has some brown hair, the blonde category because she has some blonde hair, or do we add a new category for like dirty blonde or brown blonde?
If you start making categories in this way, you can end up with something like this, where you have the original six categories and also additional categories for black-brown and brown-blonde and brown-gray and all of these other things.
Even with all these categories, it's still necessarily going to be hard to categorize people. What's the difference between brown and black brown? It's going to be very difficult to figure out how any one person fits into these categories.
So the category in which people will be placed is subjective.
And the number of categories itself is a concern because categories can start to proliferate out of control if you don't put a cap on them. There are two problems: having too many categories, and having too few categories.
1. If you have too many categories, your pie charts and bar graphs are going to be overwhelming; your pie slices are going to get really, really thin and your bars are going to get really, really small.
If you have too many categories, you're going to have lots of options but not a lot of data points within each bin.
2. Conversely, you can have the opposite problem, where you have too few categories that aren't very informative anymore.
In these graphs, there is a good amount in one category and a little bit more than half in the other category. Because these two categories are so large, this might not be as informative as it would have been with more categories.
If you can, use not too many nor too few categories.
Categories and classes are how we separate data into different ideas based on the characteristics of the data points. Sometimes you don't have an objective basis for assigning categories with your qualitative data, and your data can get confusing when you end up with subjective categories. So you have to try and put the brakes on and stop the over proliferation of categories and just say that you’re going to put people into a category even if they don't necessarily fit neatly into it because you don't want to proliferate the number of categories too much.
Source: THIS WORK IS ADAPTED FROM SOPHIA AUTHOR JONATHAN OSTERS