## Qualitative Variable: What is it?

A qualitative variable, also called a categorical variable, is a variable that **isn’t numerical**. It describes **data that fits into categories.** For example:

- Eye colors (variables include: blue, green, brown, hazel).
- States (variables include: Florida, New Jersey, Washington).
- Dog breeds (variables include: Alaskan Malamute, German Shepherd, Siberian Husky, Shih tzu).

These are all qualitative variables as they have no natural order. On the other hand, quantitative variables have a value and they can be added, subtracted, divided or multiplied.

Watch the video for an overview:

Quantitative Variable |
Qualitative Variables |

Fractions | Cat breeds |

Decimals | Cities |

Odd Numbers | Fast Food Chains |

Whole Numbers | College Major |

Irrational Numbers | Fraternities |

Ordered pairs (x,y) | Hair Color |

Negative Numbers | Computer Brands |

Map coordinates | Beer breweries |

Positive Numbers | Pop music genre |

Exponents | Tribe |

As a general rule, if you can apply some kind of math (like addition), it’s a quantitative variable. Otherwise, it’s qualitative. For example, you can’t add blue + green (unless you’re in an art class — even then you “mix” them, you don’t add them!).

**Numbers are sometimes assigned to qualitative variables** for **data analysis**, but they are still classified as qualitative variables despite the numerical classification. For example, a study may assign the number “1” to males and “2” to females.

## Qualitative Variables and the Nominal Scale

Qualitative variables aren’t ordered on a numerical scale so they are placed on a **nominal scale**. The word “nominal” means “name”, which is exactly what qualitative variables are. A nominal scale is a scale where no ordering is possible or implied (except for alphabetical ordering like New York, Washington, West Virginia or Chelsea, Edinburgh, London). In other words, the nominal scale is where data is assigned to a category.

More on quantitative variables.

## References

Dodge, Y.; Cox, D.; Commenges, D.; Davidson, A; Solomon, P.; and Wilson, S. (Eds.). The Oxford Dictionary of Statistical Terms, 6th Edition. New York: Oxford University Press, 2006.