“Statistics is the grammar of science.” – Karl Pearson
Variables are characteristics or properties of data that can take on different values or amounts for different individuals in the population. They are data attributes such as ID number, age, gender, and body temperature. Variables can be classified according to their function or the way they’re used in a study.
A variable can be independent or dependent. An independent variable can take different values. An independent variable affects or determines a dependent variable. A dependent variable can take different values in response to an independent variable. In some contexts, a researcher selects or controls the value of an independent variable, in order to determine its relationship to the dependent variable.
For example, a researcher investigating the effect of fertilizer on plant growth could change the amount of fertilizer – the independent variable – in order to observe the effect on the plants – the dependent variable. In other contexts, however, the independent variable’s values are simply taken as given.
For example, suppose a researcher is trying to determine the effect of a variable such as incarceration rate on crime rate. The researcher can’t manipulate the variable incarceration rate. It is simply observed. In this case, you might hear this variable referred to as a predictor variable. You might also hear this type of variable referred to as an explanatory or control variable. A dependent variable is also known as a response variable or an outcome variable.