4.3 Nomothetic causality

Causality refers to the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief. In other words, it is about cause and effect. It seems simple, but you may be surprised to learn there is more than one way to explain how one thing causes another. How can that be? How could there be many ways to understand causality?

One way to understand causality could be to look for causal explanations that are universally true for everyone, everywhere. These causal relationships should look pretty basic to you. They should look like “x causes y.” Indeed, you may be looking at a causal explanation and thinking, “wow, there are so many other things that are missing in here.” Nomothetic causal explanations boil things down to two (or often more) key variables and assert a one-way causal explanation between them. This is by design, as they are trying to generalize across all people to all situations. The more complicated, circular, and often contradictory causal explanations are idiographic, which we will cover in section 4.4 of this chapter.

Criteria for establishing nomothetic causality

Let’s say you conduct your study and you find evidence that supports your hypothesis, as age increases, support for marijuana legalization decreases. Success! Causal explanation complete, right? Not quite.

You’ve only established one of the criteria for causality: covariation. The criteria for causality must include all of the following: covariation, plausibility, temporality, and nonspuriousness. In our example from Figure 4.7, we have established only one criterion—covariation. When variables covary, they vary together. There are plenty of examples of variables that vary, but not together.  For example, Nesterko et al. (2020)^[1] studied factors that predict trauma-related mental health symptoms in recently arrived refugees. They found that flight duration (i.e., the length of time a refugee spent between fleeing their country of origin and arriving at the country of refuge) did not covary with symptoms of somatization. In other words, the length of time a refugee was in flight varied, but not with the degree of somatic symptoms. As a result, we have not met the criterion of covariance to establish a causal link between flight duration and symptoms of somatization.

Once we’ve established that there is a plausible association between the two variables, we also need to establish whether the cause occurred before the effect, the criterion of temporality. A person’s age is a quality that appears long before any opinions on drug policy, so temporally the cause comes before the effect. It wouldn’t make any sense to say that support for marijuana legalization makes a person’s age increase. Even if you could predict someone’s age based on their support for marijuana legalization, you couldn’t say someone’s age was caused by their support for legalization of marijuana.

Finally, scientists must establish nonspuriousness. A spurious association is one in which an association between two variables appears to be causal but can in fact be explained by some third variable. This third variable is often called a confound or confounding variable because it clouds and confuses the association between your independent and dependent variable, making it difficult to discern the true causal association.

Continuing with our example, we could point to the fact that older adults are less likely to have used marijuana recreationally. Maybe it is actually recreational use of marijuana that leads people to be more open to legalization, not their age. In this case, our confounding variable would be recreational marijuana use. Perhaps the association between age and attitudes towards legalization is a spurious association that is accounted for by previous use. This is also referred to as the third variable problem, where a seemingly true causal association is actually caused by a third variable not in the hypothesis. In this example, the association between age and support for legalization could be more about having tried marijuana than the age of the person.

Quantitative researchers are sensitive to the effects of potentially spurious associations. As a result, they will often measure these third variables in their study, so they can control for their effects in their statistical analysis. These are called control variables, and they refer to potentially confounding variables whose effects are controlled for mathematically in the data analysis process. Control variables can be a bit confusing, but think about it as an argument between you, the researcher, and a critic.

Researcher: “The older a person is, the less likely they are to support marijuana legalization.”

Critic: “Actually, it’s more about whether a person has used marijuana before. That is what truly determines whether someone supports marijuana legalization.”

Researcher: “Well, I measured previous marijuana use in my study and mathematically controlled for its effects in my analysis. Age explains most of the variation in attitudes towards marijuana legalization.”

Let’s consider a few additional, real-world examples of spuriousness. Did you know, for example, that high rates of ice cream sales have been shown to cause drowning? Of course, that’s not really true, but there is a positive association between the two. In this case, the third variable that causes both high ice cream sales and increased deaths by drowning is time of year, as the summer season sees increases in both (Babbie, 2010).^[2]

Here’s another good one: it is true that as the salaries of Presbyterian ministers in Massachusetts rise, so too does the price of rum in Havana, Cuba. Well, duh, you might be saying to yourself. Everyone knows how much ministers in Massachusetts love their rum, right? Not so fast. Both salaries and rum prices have increased, true, but so has the price of just about everything else (Huff & Geis, 1993).^[3]

Finally, research shows that the more firefighters present at a fire, the more damage is done at the scene. What this statement leaves out, of course, is that as the size of a fire increases so too does the amount of damage caused as does the number of firefighters called on to help (Frankfort-Nachmias & Leon-Guerrero, 2011).^[4] In each of these examples, it is the presence of a confounding variable that explains the apparent association between the two original variables.

• The two variables must vary together.
• The association must be plausible.
• The cause must precede the effect in time.
• The association must be nonspurious (not due to a confounding variable).

For a humorous blog post about causality, check out sociologist’s Bradley Wright’s post on causality.