Читать книгу The Research Experience - Ann Sloan Devlin - Страница 116

You may have heard the phrase “Correlation is not causation.” These two concepts lie at different ends of the spectrum of certainty about relationships. That does not mean that one kind of relationship is always preferable to the other; each is suited to different research questions and situations. Ultimately, most researchers seek to understand causes of behavior, it is true, but in some kinds of situations, research that would result in making statements about causality is not possible.

When the research approach is correlational, the focus is on the relationships between variables. We change nothing about the situation of interest and simply assess whether relationships exist. We have no evidence that a change in one variable caused a change in the other. Because the variables have not been manipulated, there is no opportunity to assess causality; there is no evidence of influence (that is, one variable cannot be said to affect another). Rather, the concepts of interest are associated or related to one another. When the research approach is causal, there is evidence of influence. In this situation, there is an explicit manipulation of (i.e., change to) one or more variables. This change allows us to assess causality.

Correlational research: Approach to research where no variables are manipulated.

Causal research: When the research design enables you to test cause-and-effect relationships.

To illustrate the difference, we might first investigate the relationship between students’ GPAs and the distance of the college they attend from the students’ hometowns. We cannot randomly assign people to a given GPA, nor can we randomly assign them to living in a specific hometown. Students “come that way.” We might hypothesize that these variables (GPA and distance of the hometown from the college) co-vary, such that changes in one are associated with changes in the other—for example, that students who have higher GPAs live farther away from their hometown (and those with lower GPAs live closer). In this case, we are predicting a positive relationship (higher GPAs correlate with longer distances), but we cannot infer causality. Why? Because there are many other explanations other than distance for that GPA.