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A List of Common and Uncommon Types of Variables

Scout the video for a cursory overview of several common types of variables:

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A "variable" in algebra really simply means one affair—an unknown value. However, in statistics, you'll come beyond dozens of types of variables. In well-nigh cases, the word still ways that you're dealing with something that's unknown, but—different in algebra—that unknown isn't ever a number.

Some variable types are used more than others. For example, you lot'll be much more probable to come beyond continuous variables than you lot 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).

Click on whatsoever bold variable name to larn more about that item type.

Common Types of Variables

• Chiselled 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 abiding. For instance, in an experiment to determine whether light makes plants grow faster, yous would have to control for soil quality and h2o.
• Dependent variable: the effect of an experiment. As you change the contained variable, you lot 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 machine park can just concord so many cars.
• Independent variable: a variable that is not affected by annihilation that you, the researcher, does. Ordinarily plotted on the x-centrality.
• Lurking variable: a "subconscious" variable the affects the relationship between the independent and dependent variables.
• A measurement variable has a number associated with it. It'due south an "amount" of something, or a"number" of something.
• Nominal variable: another name for categorical variable.
• Ordinal variable : similar to a chiselled variable, but there is a clear order. For case, income levels of low, middle, and high could be considered ordinal.
• Qualitative variable: a broad category for any variable that can't exist counted (i.due east. has no numerical value). Nominal and ordinal variables fall nether 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 signal tin be put in lodge (1st, 2nd, third, etc.).
• Ratio variables: similar to interval variables, but has a meaningful zero.

Less Common Types of Variables

• Active Variable: a variable that is manipulated by the researcher.
• Ancestor Variable: a variable that comes earlier the independent variable.
• Aspect variable: another name for a categorical variable (in statistical software) or a variable that isn't manipulated (in design of experiments).
• Binary variable: a variable that tin can merely have on ii values, ordinarily 0/1. Could besides exist yes/no, tall/brusk or some other two-variable combination.
• Collider Variable: a variable represented by a node on a causal graph that has paths pointing in likewise equally out.
• Covariate variable: like to an independent variable, it has an effect on the dependent variable but is usually not the variable of interest. See also: Noncomitant variable.
• Criterion variable: another name for a dependent variable, when the variable is used in non-experimental situations.
• Dichotomous variable: Another proper noun for a binary variable.
• Dummy Variables: used in regression analysis when y'all want to assign relationships to unconnected categorical variables. For instance, if you had the categories "has dogs" and "owns a machine" you might assign a 1 to mean "has dogs" and 0 to mean "owns a auto."
• Endogenous variable: like to dependent variables, they are affected by other variables in the system. Used almost exclusively in econometrics.
• Exogenous variable: variables that affect others in the organisation.
• Explanatory Variable: a blazon of independent variable. When a variable is independent, it is not affected at all by whatever other variables. When a variable isn't contained for certain, information technology's an explanatory variable.
• Extraneous variables are any variables that you are not intentionally studying in your experiment or test.
• A grouping variable (also called a coding variable, group variable or by variable) sorts data within data files into categories or groups.
• Identifier Variables: variables used to uniquely place situations.
• Indicator variable: some other proper name for a dummy variable.
• Interval variable: a meaningful measurement between two variables. Also sometimes used as another name for a continuous variable.
• Intervening variable: a variable that is used to explain the relationship between variables.
• Latent Variable: a hidden variable that can't be measured or observed directly.
• Manifest variable: a variable that can exist directly observed or measured.
• Manipulated variable: another proper noun for independent variable.
• Mediating variable or intervening variable: variables that explain how the relationship between variables happens. For case, it could explain the difference betwixt the predictor and criterion.
• Moderating variable: changes the force of an effect between independent and dependent variables. For example, psychotherapy may reduce stress levels for women more men, so sex activity moderates the effect between psychotherapy and stress levels.
• Nuisance Variable: an inapplicable variable that increases variability overall.
• Observed Variable: a measured variable (normally used in SEM).
• Outcome variable: similar in meaning to a dependent variable, but used in a non-experimental report.
• Polychotomous variables: variables that tin have more two values.
• Predictor variable: similar in significant to the independent variable, just used in regression and in non-experimental studies.
• Responding variable: an informal term for dependent variable, commonly used in scientific discipline fairs.
• Scale Variable: basically, another name for a measurement variable.
• Study Variable (Enquiry Variable): tin mean whatsoever variable used in a study, merely does have a more formal definition when used in a clinical trial.
• Examination Variable: another name for the Dependent Variable.
• Treatment variable: another proper name for independent variable.

Types of Variables: References

Dodge, Y. (2008). The Concise Encyclopedia of Statistics. Springer.
Everitt, B. South.; Skrondal, A. (2010), The Cambridge Dictionary of Statistics, Cambridge University Press.
Gonick, 50. (1993). The Cartoon Guide to Statistics. HarperPerennial.

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