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Control variables are variables included in multivariate analyses to identify spurious associations. In assessing whether X causes Y, it is important to examine whether the covariation between them persists after the effects of other variables on this association are removed. A variable is controlled when its influence on the other variables in the model is held constant.
In laboratory experiments, Z can be controlled by setting a fixed value for it and observing the relationships between X and Y for that fixed value. The experiment can then be repeated at other fixed values of Z and see whether the same results occur. However, in most social research, values of variables such as education, age, and income cannot be manipulated before obtaining the data. Therefore, the part of association between X and Y that is caused by variation in Z can be removed by comparing only cases with equal or similar values of Z at a time. Spurious relationship exists if both X and Y are dependent on Z, so that the association disappears when Z is controlled.
Social research establishes causal claims by demonstrating temporally ordered covariation among variables and by discrediting alternative explanations. It is not always evident which variables should be controlled in causal analysis using multivariate techniques. Researchers should thus develop a thorough knowledge of the theory and past research relating to their empirical inquiry so that plausible spurious relationships can be quickly detected and potential confounding variables appropriately controlled for.
- McClendon, M. J. (2002) Multiple Regression and Causal Analysis. Waveland Press, Long Grove, IL.