________ variables are variables that moderate the relationship between two or more variables.

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Common Types of Variables
Less Common Types of Variables
Types of Variables: References

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

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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).

Click on any bold variable name to learn more about that particular type.

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.

Less Common Types of Variables

Active Variable: a variable that is manipulated by the researcher.
Antecedent Variable: a variable that comes before the independent variable.
Attribute 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 can only take on two values, usually 0/1. Could also be yes/no, tall/short or some other two-variable combination.
Collider Variable: a variable represented by a node on a causal graph that has paths pointing in as well as out.
Covariate variable: similar 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 name for a binary variable.
Dummy Variables: used in regression analysis when you want to assign relationships to unconnected categorical variables. For example, if you had the categories “has dogs” and “owns a car” you might assign a 1 to mean “has dogs” and 0 to mean “owns a car.”
Endogenous variable: similar 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 system.
Explanatory Variable: a type of independent variable. When a variable is independent, it is not affected at all by any other variables. When a variable isn’t independent for certain, it’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 identify situations.
Indicator variable: another 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 be directly observed or measured.
Manipulated variable: another name for independent variable.
Mediating variable or intervening variable: variables that explain how the relationship between variables happens. For example, it could explain the difference between the predictor and criterion.
Moderating variable: changes the strength of an effect between independent and dependent variables. For example, psychotherapy may reduce stress levels for women more than men, so sex moderates the effect between psychotherapy and stress levels.
Nuisance Variable: an extraneous variable that increases variability overall.
Observed Variable: a measured variable (usually used in SEM).
Outcome variable: similar in meaning to a dependent variable, but used in a non-experimental study.
Polychotomous variables: variables that can have more than two values.
Predictor variable: similar in meaning to the independent variable, but used in regression and in non-experimental studies.
Responding variable: an informal term for dependent variable, usually used in science fairs.
Scale Variable: basically, another name for a measurement variable.
Study Variable (Research Variable): can mean any variable used in a study, but does have a more formal definition when used in a clinical trial.
Test Variable: another name for the Dependent Variable.
Treatment variable: another name for independent variable.

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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In analytical health research there are generally two types of variables. Independent variables are what we expect will influence dependent variables. A Dependent variable is what happens as a result of the independent variable. For example, if we want to explore whether high concentrations of vehicle exhaust impact incidence of asthma in children, vehicle exhaust is the independent variable while asthma is the dependent variable.

A confounding variable, or confounder, affects the relationship between the independent and dependent variables. A confounding variable in the example of car exhaust and asthma would be differential exposure to other factors that increase respiratory issues, like cigarette smoke or particulates from factories. Because it would be unethical to expose a randomized group of people to high levels of vehicle exhaust,[1] a study comparing two populations with differential exposure to vehicle exhaust would rely on a natural experiment, or a situation in which this already occurs due to factors unrelated to the researchers. In this natural experiment, a community living near higher concentrations of car exhaust may also live near factories that pollute or have higher rates of smoking.

When running a study or analyzing statistics, researchers try to remove or account for as many of the confounding variables as possible in their study design or analysis. Confounding variables lead to bias, or a factor that may cause an estimate to differ from the true population value. Bias is a systematic error in study design, subject recruitment, data collection, or analysis that results in a mistaken estimate of the true population parameter.[2]

Although there are many types of bias, two common types are selection bias and information bias. Selection bias occurs when the procedures used to select subjects and others factors that influence participation in the study produce a result that is different from what would have been obtained if all members of the target population were included in the study.[2] For example, an online website that rates the quality of primary care physicians based on patients’ input may produce ratings that suffer from selection bias. This is because individuals that had a particularly bad (or good) experience with the physician may be more likely to go to the website and provide a rating.

Information bias refers to a “systematic error due to inaccurate measurement or classification of disease, exposure, or other variables.”[3] Recall bias, a type of information bias, occurs when study participants do not remember the information they report accurately or completely. The subject of confounding and bias relates to a larger discussion of the relationship between correlation and causation. Although two variables may be correlated, this does not imply that there is a causal relationship between them.

One way to determine whether a relationship between variables is causal is based on three criteria for research design: temporal precedence meaning that the hypothesized cause happens before the measured effect; covariation of the cause and effect meaning that there is an established relationship between the two variables regardless of causation; and a lack of plausible alternative explanations. Plausible alternative explanations are other factors that may cause the dependent variable under observation.[4]. These alternative explanations are closely related to the concept of internal validity.

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