Variables, Continuous

Continuous variables are variables that can take on any value within a range. Continuous variables are also considered metric or quantitative variables, where the variable can have an infinite number or value between two given points. A variable is continuous if it is theoretically possible for members of the group to fall anywhere on a spectrum with small amounts of a characteristic on one end and large amounts of a characteristic on the other end. Continuous variables are often measured in infinitely small units.

Many physical traits are considered continuous variables, along with psychological traits such as intelligence, extroversion, and creativity. The idea is that, no matter how similar two people are in extroversion, it is theoretically possible for a third person to have a level of extraversion between the other two people. To determine if a variable is continuous, there are two primary questions to ask: (a) If a small difference exists between two people in the group, could a third member of the group be positioned between the first two? (b) If so, could the third member be positioned between the first two no matter how small the difference between the first two?

Continuous variables are different from categorical, or discrete, variables. Continuous variables are quantitative in nature, but not all quantitative variables are continuous. For example, if a parent has two children, it is not logically possible for a third child to be “in between” the first two. However, height is considered a continuous variable. In a group of individuals where not all are equally tall, height is considered a variable. If two people in the group, Person A and Person B, are nearly the same height, consider whether a third person, Person C, could be taller than one but shorter than the other. Furthermore, could Person C’s height be between Persons A and B regardless of how small the difference between A and B? As long as Person A and Person B are not the same height, the variable is continuous. To illustrate, Person A could be 72.13 inches tall, and Person B could be 72.14 inches tall. Person C’s height can be an infinite number of values between 72.13 and 72.14, so theoretically Person C could fit between A and B.

Continuous variables are measured on interval or ratio scales (but not all interval or ratio scales are continuous). Interval scale data are measured on a linear scale, whereas ratio scale data are measured on a nonlinear scale. Some continuous data are treated simply as interval scale variables, whereas others are treated as a continuous ordinal scale. In some cases, continuous data are given discrete values; for example, age is continuous, but age at the most recent birthday is a discrete value. In such situations, it is appropriate to treat discrete values as continuous. Researchers are limited in their ability to measure such infinite numbers because there are infinite possibilities. As a result, numbers are often rounded off to make the data easier to work with, which means data are treated as discrete variables. An example of this rounding can be seen in grade point average (GPA). An individual can earn a 3.31 GPA, 3.32, and so on. These numbers are rounded and assigned to a rank order scale of A, B, C, D, and F. It is important to remember that continuous variables are often reported with numbers containing one or two decimal places, but reporting in this way does not change the fact that these variables are continuous. Deciding how to treat continuous data is important for choosing statistical techniques. When values are treated as continuous, statistical analysis is more powerful because data are lost when continuous data are recorded in a range or rounded. Simple ratios can be treated as continuous.

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