This is typically done so that the variable can no longer act as a confounder in, for example, an observational study or experiment.
[1] A limitation of controlling for variables is that a causal model is needed to identify important confounders (backdoor criterion is used for the identification).
To ensure the measured effect is not influenced by external factors, other variables must be held constant.
In an observational study, researchers have no control over the values of the independent variables, such as who receives the treatment.
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 institutional review boards to randomly assign some participants to have jobs and some not to.
[3] The simplest examples of control variables in regression analysis comes from Ordinary Least Squares (OLS) estimators.
(Some researchers perceive a "u-shape": life satisfaction appears to decline first and then rise after middle age.