 # Types of Variables in Statistics and Research

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## A List of Common and Uncommon Types of Variables

Watch the video for a brief overview of several common types of variables:

A “variable” in algebra really just means one thing—an unknown value. However, in statistics, you’ll come across dozens of types of variables. In most cases, the word still means that you’re dealing with something that’s unknown, but—unlike in algebra—that unknown isn’t always a number.

Some variable types are used more than others. For example, you’ll be much more likely to come across continuous variables than you would dummy variables. The following lists are sorted into common types of variables (like independent and dependent) and less common types (like covariate and noncomitant).

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## Common Types of Variables

• Categorical variable: variables than can be put into categories. For example, the category “Toothpaste Brands” might contain the variables Colgate and Aquafresh.
• Confounding variable: extra variables that have a hidden effect on your experimental results.
• Continuous variable: a variable with infinite number of values, like “time” or “weight”.
• Control variable: a factor in an experiment which must be held constant. For example, in an experiment to determine whether light makes plants grow faster, you would have to control for soil quality and water.
• Dependent variable: the outcome of an experiment. As you change the independent variable, you watch what happens to the dependent variable.
• Discrete variable: a variable that can only take on a certain number of values. For example, “number of cars in a parking lot” is discrete because a car park can only hold so many cars.
• Independent variable: a variable that is not affected by anything that you, the researcher, does. Usually plotted on the x-axis.
• Lurking variable: a “hidden” variable the affects the relationship between the independent and dependent variables.
• A measurement variable has a number associated with it. It’s an “amount” of something, or a”number” of something.
• Nominal variable: another name for categorical variable.
• Ordinal variable: similar to a categorical variable, but there is a clear order. For example, income levels of low, middle, and high could be considered ordinal.
• Qualitative variable: a broad category for any variable that can’t be counted (i.e. has no numerical value). Nominal and ordinal variables fall under this umbrella term.
• Quantitative variable: A broad category that includes any variable that can be counted, or has a numerical value associated with it. Examples of variables that fall into this category include discrete variables and ratio variables.
• Random variables are associated with random processes and give numbers to outcomes of random events.
• A ranked variable is an ordinal variable; a variable where every data point can be put in order (1st, 2nd, 3rd, etc.).
• Ratio variables: similar to interval variables, but has a meaningful zero.

## Types of Variables: References

Dodge, Y. (2008). The Concise Encyclopedia of Statistics. Springer.
Everitt, B. S.; Skrondal, A. (2010), The Cambridge Dictionary of Statistics, Cambridge University Press.
Gonick, L. (1993). The Cartoon Guide to Statistics. HarperPerennial.

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Stephanie Glen. "Types of Variables in Statistics and Research" From StatisticsHowTo.com: Elementary Statistics for the rest of us! https://www.statisticshowto.com/probability-and-statistics/types-of-variables/
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