Qualitative data is also often called “categorical data”. It is not numerical in the sense that we can do numerical operations with it, like adding numbers together or finding an average, but rather, it fits in the category.
EXAMPLEGender: male and female. That's a qualitative variable with two categories.
Letter grades AND zip codes feature numbers, but you wouldn’t necessarily do mathematical equations with them. You wouldn’t find an average zip code, for instance. The purpose of zip codes is to divide areas into categories. Hair color is another example of qualitative data because you can group those with black hair and put those with blonde hair in another group.
It's important to know that qualitative data can be divided further into two categories:
EXAMPLEFavorite color. The order of the listed categories makes no difference. It doesn't matter if you put the colors below in the order of the color spectrum or not.
With nominal data, it only makes sense to reference which category has the largest frequency. In this case, let’s say most people say that green is their favorite color. That is what you would report and it doesn’t matter that green is the 4th box from the left.
EXAMPLERating scale. The order of the listed categories is very important because the order is associated with a type of value. It’s very important that you don’t mix up the order here because the circle on the furthest left indicates you are feeling no pain.
With ordinal data, it’s important to keep the order straight, or rather, in order, to express a spectrum ranging from lowest to highest, or worst to best. Ratings like that.
On the other hand, you have quantitative data. Quantitative data are expressed numerically. It makes sense to do numerical operations with it, like finding averages or adding them together.
Examples of quantitative data include:
All of these are measured in numbers. It makes sense to find, for instance, averages of these. So you can do numerical operations with them.
It's important to note that data is displayed differently for qualitative data than with quantitative data. Statistical operations depending on the type of data that we have.
Determine if each situation is qualitative or quantitative data.
[MUSIC PLAYING] Here we have some examples to help you understand the differences between qualitative and quantitative data. So first, we have blood type. That's going to be an example of qualitative data. It's a description. It's telling you something about yourself, but it's not something that can be added, or subtracted, or used for arithmetic even.
On the other hand, number of kids is quantitative data. Or how about a phone number? So even though it's a number, it's still qualitative data because, really, who would ever add or subtract those values? So what's an example of quantitative data? How about something like income? Income is quantitative data because, again, it's a value that's giving us a quantity. It's telling us how much money you make, and that's a value you could add, subtract, and do the mean and other measures of arithmetic.
Source: This work is adapted from Sophia author Jonathan Osters.