Type II error: Failing to reject the null hypothesis when it is false.
At the other end of the continuum, we manipulate specific variables, keep everything else the same, and try to infer causality from the study (e.g., randomly assigning students to different workout schedules before measuring their caloric intake and body esteem). You can see that even in this second case, there are still a lot of other “unmeasured” variables (e.g., students’ workout schedules prior to the study or whether they are varsity athletes) we would need to assess to be confident that differences in workout schedules caused differences in caloric intake and body esteem.
Correlation Versus Causation
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.
Try This Now 3.1
List another variable that might be used to help explain this significant relationship between students’ GPAs and the distance of the college from their hometowns.
Perhaps you said, “attended boarding school.” Perhaps going to boarding school prepares you for attending college a far distance from home, and it is this boarding school preparation, not the distance from home itself, that better explains this relationship with GPA. The essence of research in which causality can be inferred is control—control over every aspect of the research endeavor that can be controlled. When control is not possible, we have other ways we try to spread out the variability or differences in humans that can interfere with the factors we are studying. For example, when we do a study where people are exposed to different stimuli (e.g., pictures of natural and built environments) to measure environmental preference (i.e., how much they prefer particular environments), we randomly assign the participants to the different pictures (i.e., conditions) to spread out or distribute the variability that exists in the population (our participants). We do this random distribution to try to make sure, for example, that all the environmental studies majors don’t end up in the condition with pictures of nature! If they did, a higher preference for pictures of the natural environment might be explained by the students’ major, not by qualities depicted in the pictures themselves.
When we make a statement about causality, we have to persuade our audience that there are no other likely explanations; we have to rule out what are known as alternative variables represented by unmeasured or third variables, which was discussed at the end of Chapter 2. In correlational studies, such variables may lead us to infer incorrectly relationships between our variables of interest when, in fact, it is the third variable at work. Excerpts from a wonderful video from Frans de Waal’s TED Talk, “Moral Behavior in Animals,” show what happens when two capuchin monkeys were rewarded unequally for the same “work” and the less-well-rewarded monkey tests out an alternative explanation (https://www.youtube.com/watch?v=meiU6TxysCg).
One monkey is rewarded with a grape (the preferred food); the other monkey is rewarded with a piece of cucumber for the same task (handing a small stone to the experimenter). When this happens, the monkey that received the cucumber is quite unhappy (throwing the cucumber back at the researcher) and next tests a stone against the side of the cage to make sure that the stone hasn’t somehow produced the inequitable result. What this monkey is doing is testing for a third variable, as if to say, “maybe it’s the stone that’s the problem.” Even capuchin monkeys are capable of thinking about alternative explanations!
Combing two previous examples, how might you infer causality in a study involving distance and working out? In the earlier example, we could not randomly assign people to their hometowns, but we might be able to assign first-year students randomly to residence halls at different distances from the campus fitness center. Then our research question might be whether living closer to the fitness center has an effect on the number of times a week students go to the center to work out. There are many other variables (representing alternative explanations; see Chapter 2) that we might need to rule out (whether the student is a varsity or club athlete, any health restrictions, athlete status in high school, and so on), but we have manipulated and controlled a variable (distance from the fitness center) and randomly assigned people to the conditions (different residence halls located at different distances from the fitness center). If there is a result showing that people who live closer to the fitness center work out more times per week than do those who live farther from the fitness center, then we might infer causality related to the variable of distance.
Why Conduct Correlational Research?
You might be asking yourself why people do correlational research if the goal of research is to explain behavior (hence, to determine causality). Correlational research has important purposes. First, it is sometimes used as exploratory research to see whether relationships exist before investing more resources in experimental research. Second, there are many instances in which it is not possible to manipulate variables (such as people’s hometowns). Third, it may be unethical to manipulate variables. For example, we could not tell people that a car accident had occurred to a member of their family or that they had failed a final exam to determine their emotional reaction. As Chapter 4 will explain, research on human participants is monitored by review boards to make sure ethical principles are followed.
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