If two variables are correlated, it still does not mean that one variable causes the other to vary as it does even if the statement makes sense (Jaccard & Becker, 2002; para. 1, “What is the difference,” n.d.). If one action causes another, then they are most certainly correlated therefore causation causes correlation and not the other way around (Deutsch, 2005; para. 1, “What is the difference,” n.d.). Moreover, in using correlational data, causal inferences cannot be made even if we obtain a perfect correlation which may be a +1.00 or -1.00 (Myers & Hansen, 2006). If causal inferences are to be drawn from correlational analyses, extreme caution must be made (Jaccard & Becker, 2002).
Actually, there are four possible reasons as to why two variables X and Y might be correlated. Four possibilities are that (1) X causes Y, (2) Y causes X, (3) X and Y affect each other which is known as bidirectional causation, or (4) some additional variable(s) causes both X and Y (Jaccard & Becker, 2002; Myers & Hansen, 2006).
To further illustrate these possibilities, let us explore some examples. For illustration purposes, let us say we find a positive correlation between the number of hours college students spend working for pay and the number of campus organizations college students belong to, it is unlikely that working causes students to join organizations or that membership in organizations causes students to work but the correlation between hours of work and group membership is probably attributable to students’ desire to achieve and related personality characteristics (Jaccard & Becker, 2002). There are also examples wherein the causal relationship underlying a correlation is ambiguous such as the correlation between the amount of violent television a child watches and child’s aggressiveness. In this case, there are four possible