Types of Variables

Surveys can contain many kinds of questions; these questions are often called variables. There are some basic types of variables. It is important to understand the different types of variables because they can lead to different kinds of data and guide analysis.

Categorical Variables

As the name implies, a categorical variable is made up of categories. Typically, there are a set number of categories a participant can select from, and each category is distinct from the other. Familiar types of categorical variables are variables like ethnicity or marital status. A unique characteristic of many categorical variables (especially binary and nominal) is that the categories are not necessarily ordered in a meaningful way. A variable for ethnicity may be coded in the following way: African American as 1, Asian as 2, and Caucasian as 3. Which ethnicity gets assigned which number is arbitrary, so the numerical ordering of the variable doesn’t provide information about ethnicity. The three types of categorical variables—binary, nominal, and ordinal—are explained further below.

Categorical Variables

  • Binary Variables

    A simple version of a categorical variable is called a binary variable. This type of variable lists two distinct, mutually exclusive choices. True-or-false and yes-or-no questions are examples of binary variables.

    • Ex. Do you like bananas?
      • Yes
      • No
  • Nominal Variables

    A categorical variable that has more than two categories to select from is called a nominal variable.

    • Ex. How do you describe yourself? (Select all that apply)
      • African American, not of Hispanic origin
      • American Indian or Alaskan Native
      • Asian/Pacific Islander
      • Hispanic/Latino
      • White, not of Hispanic origin
      • Other, please specify ______________
  • Ordinal Variables

    Another version of a categorical variable is an ordinal variable, which has categories that can be put in a logical order. However, ordinal data do not tell us about the differences between the categories. The differences between the categories could be unknown or inconsistent. For example, a participant might be asked to describe their income as low, medium, or high. While these categories can be ordered in a logical way, there is no determined increment between each. We know that high is more than medium, but we don’t know by how much.

    • Ex. What is your highest level of education completed?
      • Less than high school
      • High school diploma/GED
      • Some college
      • Associate’s degree
      • Bachelor’s degree
      • Graduate degree

Continuous Variables

A continuous variable can take on any score or value within a measurement scale. In addition, the difference between each of the values has a real meaning. Familiar types of continuous variables are income, temperature, height, weight, and distance. There are two main types of continuous variables: interval and ratio.

  • Interval Variables

    The first type of continuous variable is the interval variable, a variable that can be ordered with a distance or level between each category that is equal and static.

    • Ex. What is the average daytime temperature in Fahrenheit during the summer in Tucson?
      • 99 degrees
      • 100 degrees
      • 101 degrees
      • 102 degrees
      • 103 degrees
      • 104 degrees
      • 105 degrees
  • Ratio Variables

    Another type of continuous variable is a ratio variable, which has one difference from an interval variable: the ratio between the scores provides information about the relation between responses. For example, if respondents were surveyed about their stress levels on a scale of 0-10, a respondent with a stress level of 10 should have twice the stress experienced as a respondent who selected a stress level of 5. A ratio variable needs to have a clear 0 point. Age, height, and weight are also good examples of ratio variables. Someone who is 6’0” tall is twice as tall as someone who is 3’0” tall.

    • Ex. Please select your child’s weight.
      • 65 lbs.
      • 70 lbs.
      • 75 lbs.
      • 80 lbs.
      • 85 lbs.
      • 90 lbs.
      • 95 lbs.