 Dear students, in this module, we are going to talk about a very important aspect that often confuses us in the theory building process, which is the difference between correlation and causation. Causation is when one variable directly affects another variable. For example, variable A is directly leading to have an effect that is produced as variable B. If A exists, then B exists. If A does not exist, then B does not exist. Correlation is when two variables are related to each other, but one does not necessarily cause the other variable. It does not mean that if two variables are simultaneously coexisting, they must have the causal relationship between each other. So criteria for establishing the causation must be the temporal priority that one variable must precede the other variable. And correlation, both variables must exist simultaneously. And ruling out of alternative explanations, it means that it doesn't mean that if two variables are coexisting simultaneously, there would not be the possibility that any other variable can have an effect on the relationship of these two variables. In simple words, correlation and causation do not reflect each other. In fact, causation means that two variables have an obvious relationship. For example, if we give one variable the name A, then it will necessarily cause B. The reason for A is B. If A exists, then B exists. If A does not exist, then B cannot exist. In correlation, A and B can exist at the same time, but it is not necessary that A exists because of the same reason. It is possible that the B variable exists because of some C. It is just a coincidence that maybe by chance two variables were existing at the same time. But correlation is generally confusing. We consider correlation as causation. Then there are spurious correlations in this. For example, when we think that these two variables are interrelated, there is a connection between them. But this relationship is actually caused by some third variable or C variable. So these variables are called spurious variables that need to be controlled by research. That is why we call them control variables. So both causation and correlation are important for developing the theories in sociology. Examples, if we look at establishing the causal relationship between poverty and crime. We feel that poverty is related to the crime rate. For example, we take these hypotheses that poor people commit more crimes. Or people commit crimes because of poverty. So can this be a direct causal relationship? That necessarily, poverty is the reason for the crime. We can see this as a correlation between the age and political participation. How different ages and people are voting behavior. So these correlations can be. For example, we make it clear that if we want to see a direct relationship with poverty. We feel that there are some spurious variables that can intervene between these two. It can be unemployment, education, geographic locality or location. Where there are already more crime rates in a geographic area. From there, people commit crimes. And that area is a ghetto. That is, the area of poor people where there are already poor people. So it is not necessary that a direct relationship can be established. Because of one variable, another variable is being caused. There are many spurious variables that need to be controlled by research.