Defining Three Basic Types of Variables in Statistics
Three basic types of variables in statistics
Almost every statistical analysis involves, at minimum, an independent and a dependent variable. Beside these, there's a third category of variable that concerns researchers: confounding variables. These three categories make up the most basic and foundational types of variables in statistics. As such, it is essential to have a firm grasp of what they represent. The next few paragraphs will discuss in detail the definitions, as well as tricks to remember them.
An independent variable is the input for an analysis — it is the variable that influences the outcome. This is what the researchers are examining, with respect to its impact on the outcome. In classes covering fundamental statistical, independent variables often include values like ethnicity or gender. It is either assigned by the researcher (for instance, assigning a subject either a placebo or drug) or a naturally occurring value such as gender. In one example, a researcher might be testing whether gender influences headache frequency/severity. Another way to think about the independent variable is that it is independent of everything else in the analysis, and is not affected by other variables. For example, being an independent person, others do not change you; rather, you form your own opinion.
Within the independent variable, there can be multiple levels. These are not to be confused with the variable itself; rather, the independent variable can be broken down into smaller groups. For instance, if phone manufacturers are an independent variable, the actual companies (Apple, Samsung, Motorola) are the levels.
The dependent variable is the outcome. It is the variable whose value is influenced by the independent variable. For example, we want to see if the price of a shoe is related to how long it will last. In this case, the price of the shoe is the independent variable, the price categories are the levels, and the dependent variable is how long the shoes last (in months). Another way to think of the dependent variable, is that it is what we are measuring.
The confounding variable is very much what it sounds like — it is something that is confusing the results. Simply speaking, it is something that hides the real influence of the independent variable. Like independent variables, confounding variables have a relationship with the dependent variable, and can influence the outcome. These are not always identified in by the researchers, in which case they can reduce the effectiveness of the analysis.
In review, we discussed the three basic types of variables: independent, dependent, and confounding. From these three, we build the foundation of statistical analyses. Independent variables are what influence the outcomes, which are the dependent variables, and confounding variables are influences on the dependent variables other than the independent variables.