This lesson defines correlational studies. It also contrasts them with experimental studies. You’ll also learn the basic concepts of correlational studies in general.
What Is a Correlational Study?
Extra! Extra! Eating blueberries prolongs your life!
Have you ever heard a news headline similar to something like that? You almost certainly have. Do you know how researchers came to that conclusion? Did the researchers force a bunch of people to eat blueberries to figure this out? If not, then how in the world did they figure out if this is actually the case? Well, it boils down to understanding the basics of correlational studies.
A correlational study is a type of research design where a researcher seeks to understand what kind of relationships naturally occurring variables have with one another. In simple terms, correlational research seeks to figure out if two or more variables are related and, if so, in what way.
Of course, it would help to understand what a variable is, right? Variables can be seen as topics of interest that can take on different values. A naturally occurring variable is a variable that has not undergone any manipulation by the researcher.
Examples of variables include:
Weight: Some people weigh 150 pounds and others weigh 250. Weight clearly varies.
Smoking status: Some people smoke and others don’t.
Quality of life: Some of us are very happy with our lives, some are moderately happy, and others think their life is horrible.
All of these things can vary and are, thus, variables.
Examples of Correlational Studies
There are many examples of correlational studies or questions such studies may seek to answer:
Someone may want to find out if drinking while pregnant is associated with an increased risk of depression for the teenage child.
Is smoking associated with Alzheimer’s disease?
Are child victims of abuse more likely to become abusers themselves?
There’s a key concept in the definition of a correlational study that you should’ve taken care to note. It is the concept of a naturally occurring variable, which you’ll recall is a variable that has not undergone any manipulation by the researcher. In other words, correlational studies do not alter the experimental systems. They simply observe what is naturally going on (or what has happened).
This is very different from studies that, in essence, artificially create variation. These studies, sometimes broadly called manipulative studies, are also known as experimental studies. These are studies where researchers manipulate the study environment or system in some way (they introduce a change of some sort) in order to then measure the effects their manipulations have had on an outcome of interest.
This is an important distinction. Due to the nature of correlational studies, which passively seek to find relationships between variables, one cannot accurately ascertain a cause and effect relationship using a correlational study. Do blueberries really prolong life? Or, is it that people who can afford those expensive blueberries have longer lives due to other factors, such as access to good quality healthcare?
Here’s a more obvious example: A correlational study can show that children with high intelligence come from homes with carpeting instead of hardwood flooring. Does that mean carpets cause children to grow up to be more intelligent? That’s a bit unlikely, isn’t it?
So, remember this important point: correlation does not equal causation. Correlation only shows there is (or isn’t) some sort of relationship, but its exact nature is unclear. In contrast, experimental studies seek to figure out a cause and effect relationship by carefully changing and modifying various factors of the study design.
You might be tempted to believe that, because of this, correlational studies are kind of useless if we can’t accurately ascertain a cause and effect relationship. But you’d be wrong. This is because ethics comes into play with respect to correlational research and experimental research.
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