Greetings fellow bloggers!
This week I am going to look at the subject of correlational studies and ask the question “Can a correlation ever prove a causality in a relationship?”
So let’s start with the basics, a correlation study is a type of study used to determine whether there is a relationships between any and all variables used. A correlation is described by the APA as “Interdependence of variable quantities.”
So what forms of correlations can you get? There are positive correlations, negative correlations. A positive correlation is where one variable will either increase or decrease and the second variable will increase or decrease along with the first (not to be misunderstood as a causality I might add, this effect could be caused from either variable). A negative correlation, on the other hand, occurs when one variable increases and the second variable decreases (or vice versa) at the same time. And of course, there are “no correlation” results, when, as the title suggests, has no discernible pattern to it. Correlations can also be obtained via such techniques as observations, archival study and questionnaires/interviews.
Correlation graphs sound great! They take all of your data and compare them, to look for traits/relationships and then explain them all to you! From this you can see what happens(ed), how it happened, and why it happened! But wait.. can a correlation really tell you how and why an effect happens(ed)? Let’s create a scenario to envision this idea… Imagine there is a study conducted on school children and it’s discovered that at dinner time the students who pick the healthy options tend to score above average on tests in the afternoon, compared to the students who do not pick the healthy options. Now what this statement would appear to say is that eating healthily at dinner time would seem to increase how well a student performed afterwards, but this is not the case! For here, we only have a correlation, this is not a causality. There may be many factors that interfere with these statistics, maybe certain types of classes, or attentional span and so on. This is the main disadvantage of using a correlational study, you can never assume a causality from a correlation, whether positive or negative, you must only determine thatthere is a relationship between one variable and another.
Let’s try with a real world example instead. A study run by Buss (1984) looked at the correlation between the choosing of a spouse in marraige and that persons’ qualities, and also whether or not the length of a relationship/marriage affected the cohesion of a couples’ interactions. The study found that indeed there was a correlation regarding the qualities a spouse had and their being chosen by their partner (particularly with domain such as quarrelsomeness, dominance, and extraversion). The study also found that the cohesion between a couple does increase (and subsequently correlate with,) the increased length of a relationship/marriage. These finds seems to be pretty conclusive don’t they? Couples form a tighter, more on track, relationship with a partner the longer the relationship lasts, and partners pick people who rate highly with particular traits of their spouse. But once again, with these correlations, we can only assume that there is a relationship, we cannot determine which direction the effect is going.
So to conclude, when conducting correlational studies, although very useful in terms of determining relationships, we must be very careful to avoid the urge of applying causality to our data as they cannot be used to determine causality.