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Statistics Resources

This guide contains all of the ASC's statistics resources. If you do not see a topic, suggest it through the suggestion box on the Statistics home page.

Levels of Measurement and Variable Types

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:

  1. Categorical (qualitative) – variables where data are grouped into categories
    1. 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
    2. Ordinal - levels of the variable belong in a specific order
      • Examples: grade in school, position in race, rating scales
  2. Continuous (quantitative) – variables where data fall along a spectrum with standard intervals
    1. Interval - values on the scale fall at set distances, but the scale does not have a true 0 point
      • Examples: composite scores, temperature 
    2. Ratio - values on the scale fall at set distances and there is a true 0 point
      • Examples: height, weight, speed, time

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.

Levels of Measurement figure

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.

 

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