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Levels of Measurement

When we talk about levels of measurement, we are talking about how we measure a variable. There are two broad types of variables that can be further broken into the 4 main levels of measurement:

**Categorical**(qualitative) – variables where data areinto categories**grouped**- Nominal - levels of the variable are identifiers only. There is no inherent order to the categories.
- Examples: breed of dog, name of university, favorite food

- Ordinal - levels of the variable belong in a specific order
- Examples: grade in school, position in race, rating scales

- Nominal - levels of the variable are identifiers only. There is no inherent order to the categories.
**Continuous**(quantitative) – variables where data fall along a spectrum with**standard intervals**- Interval - values on the scale fall at set distances, but the scale does not have a true 0 point
- Examples: composite scores, temperature

- Ratio - values on the scale fall at set distances and there is a true 0 point
- Examples: height, weight, speed, time

- Interval - values on the scale fall at set distances, but the scale does not have a true 0 point

Note: SPSS lumps both interval and ratio into a single classification: "Scale"

The graphic below should help you visualize the four different levels of measurement. See the definitions and examples below for each.

Definitions and Examples

**Nominal** variables are *categorical* variables where the categories are different only because they are named differently. We cannot rank or order the categories. Some examples include the following: race/ethnicity, gender, eye color, or neighborhood.

**Ordinal** variables are *categorical* variables where the categories can be ordered or ranked. Some examples include the following: class level (freshman, sophomore, junior, senior) and education level (less than HS, HS diploma, some college, college degree).

**Interval **variables are *continuous/scale* variables with no meaningful/absolute zero. A meaningful/absolute zero means that there is an absence of something. In an interval variable, 0 is just another data point along the scale, it does NOT mean the absence of something. For example, 0 degrees Fahrenheit is not the absence of heat or temperature, it is just another number along the temperature spectrum (it does mean it’s pretty cold, though).

**Ratio** variables are *continuous/scale* variables with a meaningful/absolute zero. In a ratio variable, 0 means that there is nothing there. For example, if I have 0 dollars, I have no money. If I have 0 hairs on my head, I am bald.

- This handout is of the Levels of Measurement graphic seen above. Download to have a copy of your own.
- This handout is an extension of the Levels of Measurement definitions and examples seen above with additional tips. Download to have a copy of your own.

- Last Updated: Mar 12, 2023 6:02 PM
- URL: https://resources.nu.edu/statsresources
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