Controlling for a variable

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In statistics, controlling for a variable is the attempt to reduce the effect of confounding variables on an observational study. It means that when looking at the effect of one variable, all other variable predictors are held constant.[1]


Experiments attempt to assess the effect of manipulating one or more independent variables on one or more dependent variables. To ensure the measured effect is not influenced by external factors, other variables must be held constant. These variables that are made to remain constant during an experiment are referred to as the control variables.

For example, if an outdoor experiment were to be conducted to compare how different wing designs of a paper airplane (your independent variable) affect how far it can fly (your dependent variable), you would want to ensure that you conduct the experiment at times when the weather is the same because you would not want weather to affect your experiment. In this case, your control variables may be wind speed and precipitation. If you started your experiment when it was sunny with no wind, but the weather changed, you'd want to postpone the completion of the experiment until your control variables (i.e. the wind and precipitation level) were the same as when the experiment began.

In controlled experiments of medical treatment options on humans, researchers randomly assign individuals to a treatment group or control group. This is done to reduce the confounding effect of irrelevant variables that are not being studied, such as the placebo effect. In contrast, in an observational study, researchers have no control over who receives the treatment. Instead, they must control for variables using statistics.

Justification for statistical control[edit]

Observational studies are used when controlled experiments may be unethical or impractical. For instance, if a researcher wished to study the effect of unemployment (the independent variable) on health (the dependent variable), it would be considered unethical by most institutional review boards to randomly assign some participants to have jobs and some not to. Instead, the researcher will have to create a sample where some people are employed and some are unemployed. However, there could be factors that affect both whether someone is employed and how healthy he or she is. Any observed association between the independent variable and the dependent variable could be due instead to these outside, spurious factors rather than indicating a true link between them. This can be problematic even in a true random sample. By holding extraneous variables constant, the researcher can come closer to understanding the true effect of the independent variable on the dependent variable.

See also[edit]


  1. ^ Frost, Jim. "A Tribute to Regression Analysis | Minitab". Retrieved 2015-08-04.