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. In a quantitative study, a researcher is likely to aim for a nomothetic understanding of the phenomenon that he or she is investigating. In the case of media or device dependency, the researcher may be unable to identify the specific idiosyncrasies of individuals’ particular patterns and perceptions of use. However, by analyzing data from a much larger and more representative group, the researcher will be able to identify the most likely, and more general, factors that account for students’ addictions to electronic gadgets. The researcher might choose to collect survey data from a wide swath of university students from around the country. He might find that students who report addictive tendencies when it comes to their gadgets also tend to be people who participate across more social media platforms, are more likely to be men, and tend to engage in rude or disrespectful behaviors more often than peers without an addiction. It is possible, then, that these associations can be said to have some causal relationship to electronic gadget addiction. However, items that seem to be related are not necessarily causal. To be considered causally related in a nomothetic study, such as the survey research in this example, there are a few criteria that must be met.

The main criteria for causality have to do with plausibility, temporality, and spuriousness. Plausibility means that in order to make the claim that one event, behavior, or belief causes another, the claim has to make sense. For example, during a series of lectures, if certain students engage in mid-class texting or web surfing even though they are aware this distracts others, one might begin to wonder whether people who are insensitive to others are more likely to exhibit dependency upon their electronic devices. However, the fact that there might be a relationship between insensitivity toward others and device dependence does not mean that a student’s insensitivity could cause him to be device dependent. In other words, just because there might be some correlation between two variables does not mean that a causal relationship between the two is really plausible.

The criterion of temporality means that whatever cause you identify must precede its effect in time. For instance, a survey researcher examining the causes of students’ digital device dependence might derive a number of findings. First, the researcher may find that those who identify as male exhibit greater device dependence than those who identify as female; that is, there is a relationship between gender identity and device dependence. In this case, one’s gender identity is more likely to precede device dependence in time than device dependence is to precede one’s gender identification. As a matter of logic, then, it may be able to establish the temporal order of the variables.

Alternately, consider the finding that the longer one has owned a smartphone, the more likely one is to exhibit device dependence. In this case, the researcher has found an association between duration of smartphone ownership and device dependence; however, what is the temporal order? It is equally plausible that those who are more device dependent will have contrived to get a smartphone earlier as it is to argue that the longer one has had a smartphone the more likely one will be device dependent. We will return to this point later when we discuss cross-sectional and experimental research.

Finally, a spurious relationship is one in which an association between two variables appears to be real but can in fact be explained by some third variable. Did you know, for example, that rates of ice cream sales have been shown to be related to the number of drowning deaths? Of course, it is not a true relationship. It is a mathematical artefact that arises because both drowning deaths and ice cream sales go up and down based on the level of a third variable. The third variable is time of year, across which both ice cream sales and drowning deaths rise or fall according to the temperature. Another classic example is that the more firefighters show up at a fire, the more damage is done at the scene. Of course, firefighters are not the cause of damage; rather, the amount of damage caused and the number of firefighters called on to help are both related to the size of the fire (Frankfort-Nachmias & Leon-Guerro, 2011). In each of these examples, it is the presence of a third variable that explains the apparent relationship between the two original variables.

In sum, the following criteria must be met in order for a correlation to be considered causal:

• The relationship must be plausible.
• The cause must precede the effect in time.
• The relationship must be nonspurious.

What we’ve been talking about here is relationships between variables. When one variable causes another, we have what researchers call independent and dependent variables. In the example where gender identity was found to be causally linked to electronic gadget addiction, gender would be the independent variable and electronic gadget addiction would be the dependent variable. An independent variable is one that causes another. A dependent variable is one that is caused by another. An easy was to remember this is that dependent variables depend on independent variables.

Relationship strength is another important factor to take into consideration when attempting to make causal claims if your research approach is nomothetic. I’m not talking strength of your friendships or marriage (though of course that sort of strength might affect your likelihood to keep your friends or stay married). In this context, relationship strength refers to statistical significance. The more statistically significant a relationship between two variables is shown to be, the greater confidence we can have in the strength of that relationship. We’ll discuss statistical significance in greater detail in. For now, keep in mind that for a relationship to be considered causal, it cannot exist simply because of the chance selection of participants in a study.