This lesson will explain the difference between qualitative data and quantitative data.
In this tutorial, you're going to learn about the difference between qualitative data and quantitative data by examining:
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.
Gender: 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:
Nominal Measurements and Ordinal Measurements.
Favorite 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 reported 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.
Rating 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 is expressed numerically. And it makes sense to do numerical operations with it, like finding averages or adding them together. So for instance, age is a quantitative value as is weight, commute time to work, and outdoor temperature.
So all of these are measured in numbers. And 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.
Data used in statistics falls under one of two broad classifications: categorical, which is called “qualitative”, or numerical, which is called “quantitative”.
Qualitative data branches out even further to nominal, which means that the names are important, and ordinal, which means the order is important.
Numerical values must make sense to do numerical operations with them. They are treated differently when organizing graphical displays and applying statistics to them.
Good luck!
Source: This work is adapted from sophia author jonathan osters.
Categorical data with qualities that cannot be ordered or ranked.
Categorical data with qualities that can be ordered or ranked.
Data that describes. It can't be measured or used for arithmetic.
Data that is numerical. It can be measured and it can be used for arithmetic. .