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A variable in an experiment which is held constant in order to assess the relationship between multiple variables [a], is a control variable. [2] [3] A control variable is an element that is not changed throughout an experiment because its unchanging state allows better understanding of the relationship between the other variables being tested. [4]
This is typically done so that the variable can no longer act as a confounder in, for example, an observational study or experiment. When estimating the effect of explanatory variables on an outcome by regression, controlled-for variables are included as inputs in order to separate their effects from the explanatory variables. [1]
A scientific control is an experiment or observation designed to minimize the effects of variables other than the independent variable (i.e. confounding variables). [1] This increases the reliability of the results, often through a comparison between control measurements and the other measurements. Scientific controls are a part of the ...
The same is true for intervening variables (a variable in between the supposed cause (X) and the effect (Y)), and anteceding variables (a variable prior to the supposed cause (X) that is the true cause). When a third variable is involved and has not been controlled for, the relation is said to be a zero order relationship. In most practical ...
In the examples listed above, a nuisance variable is a variable that is not the primary focus of the study but can affect the outcomes of the experiment. [3] They are considered potential sources of variability that, if not controlled or accounted for, may confound the interpretation between the independent and dependent variables .
A variable may be thought to alter the dependent or independent variables, but may not actually be the focus of the experiment. So that the variable will be kept constant or monitored to try to minimize its effect on the experiment. Such variables may be designated as either a "controlled variable", "control variable", or "fixed variable".
An optimal control is a set of differential equations describing the paths of the control variables that minimize the cost function. The optimal control can be derived using Pontryagin's maximum principle (a necessary condition also known as Pontryagin's minimum principle or simply Pontryagin's principle), [8] or by solving the Hamilton ...
A true experiment would, for example, randomly assign children to a scholarship, in order to control for all other variables. Quasi-experiments are commonly used in social sciences, public health, education, and policy analysis, especially when it is not practical or reasonable to randomize study participants to the treatment condition.