Sociologists spend a lot of time talking and thinking about causality. (We probably spend even more time with office politics, but that’s not very interesting, so I won’t write about that).
Now, I have a sneaking suspicion that philosophers spend a lot of time defining exactly what is the essence of causality, and they probably trace it back to Romans and Greeks and people like that. Rather than go into this type of philosophical analysis, I will simply focus on how we might test for its existence.
If “A” is a cause of “B”, what does that mean? In this case, “A” and “B” could be just about anything—characteristics of people, interactions, groups, societies.
First off, we would expect some level of association between “A” and “B”. By this we mean that as levels in “A” change, we would expect usually to see some change in “B”. Some associations are positive, meaning that “A” and “B” move in the same direction. So, as “A” increases, “B” does also. (Or, conversely, if “A” decreases, so does “B”). Other associations are negative, meaning that as “A” increases, “B” decreases or the reverse.
Second, we should see changes in “A” occur before changes in “B”. Since very few sociologists can afford time-travel machines (though I think that I saw a colleague with a flux capacitor in their office), we are stuck with the linear progression of time. That means that changes in a cause have to happen before the resulting consequences in the effect. Sometimes this time difference is miniscule, so that changes in “A” and “B” seem to happen almost simultaneously, but there is still some ordering. At the very least, if “A” causes “B”, then changes in “B” can not happen before corresponding changes in “A”.
Third, there should be no spurious correlation. A spurious correlation means that some other variable causes both “A” and “B” such that they correlate with each other, and maybe “A” comes before “B”, but in fact there is not causal connection between them. (For a fuller explanation, read this previous post). This is where things get a little tricky. Researchers can measure if two variables are associated, and he or she can measure which came first, but how can you know that there is no secret variable out there that makes the correlation spurious? Who knows, given what “A” and “B” are, there could be dozens if not hundreds possible spurious correlates. How can a researcher rule out all of them? They can’t. The researcher can measure and rule out any obvious spurious correlates, but ultimately it’s an act of faith (or, as it’s called in sociology, “theory”) that a correlation between “A” and “B” is not spurious.
Finally, we like to know how “A” causes “B”. There can be a causal relationship between the two even if we don’t know how they affect each other, but knowing “how” makes us more confident the causal connection. Basically, sociologists sleep better at night if they know the causal mechanism.
So far I’ve discussed this in rather abstract terms, and you’re probably wondering if I had intended to put you to sleep at your computer. (Sociologists sometimes forget that regular human beings don’t get excited talking about vague “A”s and “B”s).
Here’s an example.
Suppose that a friend told you that they had a bag of magic M&Ms. Now, I realize that for some people, any bag of candy is magic, but these are special M&Ms, according to your friend. If you eat a green one, you will instantly become amazingly physically attractive (if you’re not already). You’ll be so handsome or beautiful, that you’ll end up on lists like this, this, and this. (Okay, the last one was just to see if you were paying attention.)
You are intrigued, but you want to find out if it’s true. Does eating green M&Ms make you attractive? Or, to put into boring sociological notation, does “A” cause “B”? To test this, you give a bag of the magic M&Ms to your friends, and then you take notes.
First you notice whether the friends who ate green M&Ms are more attractive than those who didn’t. If so, this would be a positive association—more green M&Ms = more good looks.
Then you would look to see which came first. Perhaps beautiful people just happen to eat more green M&Ms; if so, they “B” comes before “A”, and we don’t think “A” is a cause of “B”.
Can you think of any spurious correlation between green M&Ms and attractiveness? At this particular moment, I can’t (but then again, I may just be thinking about how thin this example is getting).
Finally, you wonder how green M&Ms would change a person so dramatically. You might send them off to the lab and have them analyzed.
Once you’ve answered all these questions, you can decide for yourself if there is hope that green M&Ms will make you so good looking. Then again, maybe you should have some anyway… just in case.