Concomitant variation is one of the conditions necessary for a researcher to claim causal relationship. Select the best definition of concomitant variation.
a The cause variable must occur before the effect variable
b The two variables must move together.
C There cannot be any other explanations for the relationship.
d All of the above.
The correct answer and explanation is :
The correct answer is:
b) The two variables must move together.
Explanation (around 300 words):
Concomitant variation refers to a situation where two variables change in a related way — when one variable changes, the other also changes, either increasing or decreasing. It is one of the three key conditions needed to establish a causal relationship between variables, alongside temporal precedence and elimination of alternative explanations.
In simpler terms, concomitant variation means that if Variable A (the cause) changes, then Variable B (the effect) also changes consistently. For example, if an increase in advertising spending (Variable A) leads to an increase in sales (Variable B), and if a decrease in advertising spending leads to a decrease in sales, we observe concomitant variation. However, it’s important to remember that this alone does not prove causality. It simply shows that the two variables are related in a consistent pattern.
The other two conditions — temporal precedence (the cause must occur before the effect) and elimination of alternative explanations (ruling out other factors) — are also necessary to establish true causality, but they are not what concomitant variation specifically refers to.
Thus, option b, “The two variables must move together,” precisely defines concomitant variation.
For researchers, demonstrating concomitant variation is usually the first step in building evidence for a causal link. However, without confirming that the cause precedes the effect and that no confounding variables are responsible for the observed relationship, one cannot definitively claim causality.
In conclusion, while concomitant variation is critical, it is part of a broader framework needed to firmly establish causal relationships in research. Always think of it as the “variables moving together” piece of the causality puzzle.